Journal of Pathology Informatics

: 2014  |  Volume : 5  |  Issue : 1  |  Page : 1-

Mapping stain distribution in pathology slides using whole slide imaging

Fang-Cheng Yeh1, Qing Ye2, T Kevin Hitchens2, Yijen L Wu2, Anil V Parwani3, Chien Ho1,
1 Department of Biomedical Engineering; Department of Biological Science, Pittsburgh NMR Center for Biomedical Research, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
3 Department of Pathology, Division of Pathology Informatics, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA

Correspondence Address:
Chien Ho

Background: Whole slide imaging (WSI) offers a novel approach to digitize and review pathology slides, but the voluminous data generated by this technology demand new computational methods for image analysis. Materials and Methods: In this study, we report a method that recognizes stains in WSI data and uses kernel density estimator to calculate the stain density across the digitized pathology slides. The validation study was conducted using a rat model of acute cardiac allograft rejection and another rat model of heart ischemia/reperfusion injury. Immunohistochemistry (IHC) was conducted to label ED1 + macrophages in the tissue sections and the stained slides were digitized by a whole slide scanner. The whole slide images were tessellated to enable parallel processing. Pixel-wise stain classification was conducted to classify the IHC stains from those of the background and the density distribution of the identified IHC stains was then calculated by the kernel density estimator. Results: The regression analysis showed a correlation coefficient of 0.8961 between the number of IHC stains counted by our stain recognition algorithm and that by the manual counting, suggesting that our stain recognition algorithm was in good agreement with the manual counting. The density distribution of the IHC stains showed a consistent pattern with those of the cellular magnetic resonance (MR) images that detected macrophages labeled by ultrasmall superparamagnetic iron-oxide or micron-sized iron-oxide particles. Conclusions: Our method provides a new imaging modality to facilitate clinical diagnosis. It also provides a way to validate/correlate cellular MRI data used for tracking immune-cell infiltration in cardiac transplant rejection and cardiac ischemic injury.

How to cite this article:
Yeh FC, Ye Q, Hitchens T K, Wu YL, Parwani AV, Ho C. Mapping stain distribution in pathology slides using whole slide imaging.J Pathol Inform 2014;5:1-1

How to cite this URL:
Yeh FC, Ye Q, Hitchens T K, Wu YL, Parwani AV, Ho C. Mapping stain distribution in pathology slides using whole slide imaging. J Pathol Inform [serial online] 2014 [cited 2020 Jan 19 ];5:1-1
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Full Text

Optical microscopy has been a primary tool in pathology diagnosis for decades. The recent development of whole slide imaging (WSI) is rapidly making many advances in pathology and enabling the creation of novel tools and applications for the pathology community, including virtual microscopy, [1],[2] and computer-aided quantification and diagnosis. [3],[4],[5],[6] In contrast to conventional light microscopy that allows only a view of a fraction of a specimen at a time, WSI offers a digital replica of an entire histopathology slide. The data can be processed with a pattern recognition algorithm to identify features of interest. [7] This technique, called whole slide pattern recognition, can provide more information than traditional pathology slide analysis, minimizes tedious visual inspection by trained pathologists and has the potential to improve clinical diagnosis. Implementing WSI analysis, however, faces the following challenges. First of all, a typical WSI dataset can contain a number of pixels at tera-scale (10 12 ) and previous computational methods used to identify targets on traditional histopathology images, [8],[9] including texture-analysis, [10],[11] nuclei pattern classification, [12],[13] and edge detection, [14] are impractical for WSI due to computational time requirements. Parallel processing strategies have been developed to handle WSI; [10],[15],[16] however, boundary artifacts can jeopardize the integration of the analysis results and introduce a substantial amount of re-processing time. In addition, the abundant information provided by the whole slide analysis also requires a method to provide a quantitative assessment and to present meaningful results in a manner that is easy to interpret. [17]

In this paper, we report a WSI analysis method that conducts automatic stain recognition to estimate the density distribution of the recognized stains across an entire pathology slide. The whole slide images were tessellated into smaller image blocks with overlapping regions at the image boundary to enable parallel processing. This strategy ensures that the analysis result is free from the boundary effect. Pixel-based stain recognition with morphological smoothing [10],[18],[19] was applied to identify stains in the tessellated images. The density distribution of the identified stains was calculated by the kernel density estimator, [20],[21] yielding a distribution image of a feature or a particular cell type labeled by the stains.

To validate our method, we applied it to specimen slides from an ongoing study using a rat cardiac allograft rejection model and also another study using a rat heart ischemia/reperfusion injury model. In both studies, the macrophage infiltration was revealed by immunohistochemistry (IHC) with anti-rat ED1 antibody. The accuracy of the stain recognition algorithm was first validated by comparing its results with manual counting without involving the WSI technique. The pathology images were obtained with a standard light microscope and the results of our stain recognition algorithm were compared with those obtained from manual counting. Then, our method was further validated/correlated with the results obtained from in-vivo cellular magnetic resonance imaging (MRI), an imaging technique that can detect macrophage infiltration when labeled with ultrasmall superparamagnetic iron-oxide (USPIO) or micron-sized iron-oxide (MPIO) particles. [22],[23] Our method was also validated/correlated with the results obtained from ex-vivo cellular MRI, which were conducted using magnetic resonance microscopy (MRM) to achieve a much higher resolution for our final examination. [23]

To demonstrate the potential utilities of our method, we trained the algorithm to recognize different targets under hematoxylin and eosin (H and E) stain and to present their distribution across the tissue sections. We further calculated the diameter of the recognized targets and estimated the spatial distribution of cell nuclear diameter, which may have potential application in characterizing tissue in histopathology slides.

 Materials and Methods

Animal Models

The rat cardiac allograft rejection model, as described in a study, [23] was established using Dark Agouti and Brown Norway rats (Harlan, Indianapolis, IN) as the donor-recipient transplantation pairs. The superior vena cava of the graft heart was anastomosed to the recipient inferior vena cava (IVC) and the aorta of the graft heart was anastomosed to the recipient abdominal aorta, with the recipient proximal IVC partially obstructed to increase the pre-load. The graft, with intact pulmonary circulation, received proper pressure and volume loading without detectable atrophy over time. On day 7 after the transplantation, the graft hearts were scanned using in-vivo cellular MRI and then harvested for pathology inspections.

The rat myocardial ischemia/reperfusion injury model was established without thrombolysis component. BN rats were used in this experiment. A suture was tied around the left anterior descending artery for 45 min and then was cut to allow reperfusion. After 2 days, the heart was harvested and scanned by ex-vivo MRM.

Cellular MRI

Cellular MRI experiments were conducted by using dextran-coated USPIO particles [24] or polystyrene-coated MPIO particles (Bangs Laboratories, Fishers, IN) as an MRI contrast agent to image macrophage distribution. [22],[23],[25] The iron-oxide particles are taken up by macrophages in circulation and create signal-voids in T 2 *-weighted images to reveal the macrophage infiltration in-vivo, offering a unique way to correlated the IHC stain distribution obtained from our method.

In our study, the iron-oxide particles were given to the animals before surgery. The rats, undergoing heterotopic heart transplantation, received USPIO particles and were scanned by in-vivo cellular MRI. The in-vivo MRI scan was conducted on a Bruker 7-T scanner (Bruker, Billerica MA). The rats were intubated and ventilated during the MRI scan and electrocardiogram (ECG) leads were placed on the abdomen using a gating and monitoring system (SA Instruments, Stony Brook, NY). T 2 *-weighted magnetic resonance (MR) images, an imaging modality known to be sensitive to the signal void artifact created by iron-oxide particles, [23] were acquired using a FLASH sequence with ECG and respiratory gating. TR = the respiration cycle (~1 s), TE = 5 ms, field of view (FOV) =4 cm, slice thickness = 1.5 mm, in-plane resolution = 156 μm.

The ischemia/reperfusion model animals received MPIO particles and the hearts were harvested. The ex-vivo MRI scans were conducted using a Bruker 11.7-Tesla scanner (Bruker, Billerica MA). T 2 *-weighted MR images were also acquired using a FLASH sequence with TE = 5.8 ms, FOV = 1.2 cm, and isotropic resolution = 23 μm.

Specimen Preparation and IHC

The hearts were fixed in 4% paraformaldehyde for 1-2 weeks and embedded in paraffin. 5-μm transverse heart tissue sections were taken. Before staining, the sections were deparaffinized. Rehydrated through graded alcohols and rinsed with distilled water. Antigen retrieval was performed in a digital decloaking chamber (Biocare Medical, Cat. #DC2002), which streamed slides in preheated high pH target retrieval solution (Dako #S3308, Carpinteria, CA, USA) for 15 min. This was followed by cooling of the slides at room temperature for 20 min and rinsing the slides in distilled water before commencing the staining steps. To stain the slides, the tissue sections were incubated with 10% horse serum for 10 min at room temperature to block non-specific binding. The slides were then incubated with 1:200 anti-rat ED1 monoclonal purified immunoglobulin G (AbD SeroTec, Oxford, UK) for 2 h. After PBS wash, the slides were incubated with biotinylated secondary antibodies (LSAB kit, Dako, Carpinteria, CA, USA) for 30 min and labeled with the streptavidin-horseradish peroxidase (Dako, Carpinteria, CA, USA) for 30 min. Then the slides were stained with aminoethyl carbazole chromogen (SkyTek Laboratories, Inc., Logan, UT) and counterstained with Mayer's hematoxylin. After the slides were dehydrated, the coverslips were mounted. A rat spleen tissue section was used as a positive control section, whereas a normal rat cardiac tissue was used as a negative control section.

The stained slides were scanned on a whole slide scanner (Nanozoomer 2.0-HT, Hamamatsu, Japan) to acquire WSI at ×20 magnification. The resulting pathology images were then processed by our whole slide pattern recognition method, as detailed in the following section.

Whole Slide Pattern Recognition

The whole slide image was tessellated to enable the whole slide pattern recognition, as shown in [Figure 1]. The tessellation lines divide the whole slide image into several n-by-n sized blocks and the choice of n depends on the application scenario and the applied recognition algorithm. A higher value offers a larger field of view, but it may exceed the maximum allowable image size for the algorithm. After tessellation, each block was padded with an additional margin, m-pixel wide, to the neighboring block. The padding setting was devised for accurate accounting of stains near the block boundary, since a strict boundary could bisect a stain, incorrectly assigning it to both neighboring blocks. The choice of m considers the span of individual stain and requires prior information about their morphology. An m-pixel-wide margin allows stains with a maximum span of m to be fully recognized within the tessellated image. In our study, we used n = 1000 and the value of m was set to 100.{Figure 1}

The center of each recognized stain can be used to determine whether it should be counted or ignored to avoid double counting of stains, as illustrated by the example scenario in [Figure 2]. One should note that the tessellation borders are present at all sides of the tessellated image and [Figure 2] shows only the border between two adjacent images blocks to simplify the illustration. As shown in [Figure 2]a, our stain recognition algorithm is applied to the image block to the left and the recognized stains with center points inside the tessellation line (annotated by yellow) are counted, whereas those outside the line (annotated by red) are ignored. Similarly, in [Figure 2]b, our stain recognition algorithm is applied to the image block to the right. The recognized stains with center points inside the tessellation line (annotated by yellow) are counted, whereas the rest are ignored (annotated by red). This approach avoids any double counting and ensures the consistency of the results.{Figure 2}

Stain Recognition Algorithm

The flow chart of our stain recognition algorithm is shown in [Figure 3]. The method is composed of three modules, including a classification module that performs pixel-wise color classification, a morphology smoothing module that eliminates the fragments considered to be false positives and an analysis module that identifies each isolated stain region. It is noteworthy that a variety of stain recognition algorithms can be applied to this problem, and in this study, we have used the simplest pixel-wise color recognition approach for its low computation time and stable performance. This algorithm viewed the stain recognition as a classification problem. The red-green-blue color channels were used as the classification features and the output (classification result) was either 0 (background) or 1 (foreground). The stain classification was conducted using nearest neighbor method, a popular method in data mining. [26] Nearest neighbor method required a training dataset of background and foreground stain image. In this study, we manually cropped the background and foreground stain from the WSI data as the training dataset. Alternatively, one may also get training data from the positive and negative controls. The nearest neighbor method classified each image pixel based on its color difference with the pixels in the training images. If the minimum difference was from the foreground stain, then the pixel was classified as foreground; otherwise, it was classified as background. This classification was applied to each pixel in the input image [Figure 3]b to obtain the binary recognition output [Figure 3]c. This output image might have small fragments due to error classification or the partial slice coverage of the stained cells. To eliminate fragments in the binary images and to facilitate analysis, morphological opening and closing were applied 3 times using a 3-by-3 square structuring element to smooth the contours of the recognized stains, [27] as shown in [Figure 3]d. Finally, the connected component analysis [28] identified each isolated stain region in the image and the center location of each stain was calculated for further density estimation.{Figure 3}

Density Estimation and Feature Mapping

Since the locations of the recognized stains were discrete points scattered across the WSI, a regression analysis method was used to calculate their density distribution. This was carried out by the kernel density estimator [20],[21] as shown in [Figure 4], where [Figure 4]a shows an exemplary result of the discrete stain locations and [Figure 4]b shows their density distribution estimated by the kernel density estimator. We had a total of k stains obtained from our whole slide pattern recognition and each of them was located at coordinate xi . The kernel density estimator estimated the density distribution function by using the following formulae:{Figure 4}

where h is the kernel bandwidth and is the kernel function. The scalar feature (e.g. diameter or area) of the k recognized stains can also be estimated using kernel regression.

where yi is a scalar feature of the ith stain. Equation 2 can be used to estimate the spatial distribution of the feature across the entire pathology slide.

The bandwidth of the kernel determines the variability of the density estimation. A higher value of the bandwidth generates a smoothing effect, leading to reduced variability. On the contrary, a lower value creates a sharper estimation, but may cause over-fitting and larger variability. In this study, we used a data-driven method to automatically determine the bandwidth. [29] We used Equation 1 to estimate the ED1 stain distribution images and Equation 2 to estimate the spatial distribution of the stain feature. The source codes of our whole slide recognition program (WS Recognizer) are publicly available at

Manual Validation

We first validated our stain recognition algorithm without involving WSI. The validation was conducted using a total of 4 rats from the acute cardiac allograft rejection study in our laboratory. For each rat, one tissue section was obtained from the mid portion of the excised heart. For each slide, 16 pathology images were acquired on a transmitted light microscope (Olympus Proves AX 70) at ×40 objective lens. The field of view covered different areas in the slide to acquire images with different macrophage density. In each pathology image, manual counting and automatic stain recognition were conducted separately to avoid a spurious correlation between the manual counting and the automated recognition. Manual counting was conducted without knowing the stain recognition results and the training data for automatic recognition were selected without knowledge of the manual-counting results. The final comparison was conducted by performing regression analysis to calculate the correlation coefficient.

Cellular MRI Validation

To validate our method on WSI data, we compared the ED1 stain distribution image with both in-vivo and ex-vivo cellular MR images. To facilitate a visual comparison, the orientations of the MR images were manually rotated to match those of the pathology images. The hypointensity patches shown in the T 2 *-weighted images were due to the accumulation of the iron-oxide labeled macrophages, [23] a feature that allowed us to examine whether our ED1 stain distribution image was in good agreement with the distribution shown on cellular MRI.

The sample result of the stain recognition algorithm is shown in [Figure 5]a, where the recognized stains are annotated by the yellow contour. The training images are presented in [Figure 5]b and c, which are the background and foreground IHC stain images, respectively. The image shows the infiltration of ED1 + macrophages due to acute cardiac allograft transplant rejection and the brownish IHC stains were identified by our recognition method. The total numbers of macrophages were counted by summing up the number of annotated regions in the image and the center point of each annotated region can be calculated to facilitate further stain density estimation.{Figure 5}

The result of the manual validation is shown in [Figure 6], where the numbers of IHC stains counted by our automated stain recognition algorithm were correlated with manual counting. The analysis included 64 observations obtained from 64 pathology images. The regression line and 95% of confidence band are also plotted in the figure. The analysis shows an R2 = 0.8029 and a correlation coefficient of 0.8961 between the number of cells counted manually and that recognized by our algorithm, suggesting that the results obtained from our automatic recognition algorithm was in good agreement with the manual counting results. The narrow bank of the confidence interval further suggests that the high correlation result is highly reproducible. One may note that the coefficient of the regression line was 0.445, meaning that the automatic recognition algorithm tends to count twice as many as that by the manual counting. The systematic overestimation of the algorithm could be due to the fact that the algorithm counted the IHC stains regardless of their size, whereas manual counting may have ignored small stains. A feasible approach to improve consistency is adding a size filter to the stain recognition algorithm. Nonetheless, this discrepancy does not invalidate our stain distribution images, since the high correlation coefficient ensured that our recognition algorithm revealed the same distribution pattern and only differed by a scaling factor.{Figure 6}

The in-vivo cellular MRI is shown in [Figure 7], where the T 2 *-weighted image (left) was compared with the ED1 stain distribution image (right). The animal from our rat heart transplantation model developed acute cardiac rejection and USPIO was used as a macrophage-labeling agent in the cellular MRI. As shown in [Figure 7]a, the signal-void artifact in the T 2 *-weighted MR image, as illustrated by the arrow, suggested the accumulation of USPIO-labeled macrophages in the endocardium of the left ventricle. In comparison, our ED1 stain distribution image shown in [Figure 7]b illustrated a similar pattern of accumulation of ED1 + macrophages in the endocardium. One may note that the T 2 *-weighted image does not match perfectly with the ED1 stain distribution image. This may be due to the fact that there could be tissue distortion during the fixation procedure and that the images may not align perfectly with the pathology slides. Despite this difference, the highly similar pattern suggested that our method can be used to reveal ED1 + macrophages within pathology slides.{Figure 7}

The ex-vivo MRM is shown in [Figure 8], where the T 2 *-weighted image of the ex-vivo tissue (left) was compared with the ED1 stain distribution image (right). The rat was from our model of ischemia/reperfusion injury, with MPIO particles used as the macrophage labeling agent in the cellular MRM. The tissue section is outlined (black) in the ED1 stain distribution image to facilitate comparison. As shown in [Figure 8]a, the MPIO-labeled macrophages generate dark patches and spots in the MRM images, suggesting massive macrophage infiltration in the myocardium with ischemia/reperfusion injury. Similarly, our ED1 stain distribution image shown in [Figure 8]b showed the same pattern of ED1 + macrophage infiltration in the left ventricle. The high similarity of both imaging approaches confirmed that our method can reliably identify ED1 + macrophages within pathology slides.{Figure 8}

[Figure 9]a shows the spatial distribution of the cell nucleus, lipofusin and red blood cell in 3 rats with heart ischemia/reperfusion injury. The stain recognition algorithm was trained to recognize cell nuclei (N), lipofusin (L) and red blood cells (R) under H and E stain, as shown in [Figure 9]b. The distribution of cell nuclei can be used to investigate the increase of immune cells during the inflammation process, whereas the distribution of lipofusin may be used to investigate oxidative stress in the myocardium. The distribution of red blood cells indicates hemorrhage due to ischemic injury. Both three distribution images offer paranomic pathology information of the ischemia/reperfusion injury across tissue sections.{Figure 9}

[Figure 10] is an example of feature distribution image showing the spatial distribution of the cell nuclear diameter. The diameter of the recognized cell nuclei was estimated using the kernel regression formulated in Equation 2. The feature distribution image can be used to provide a quantitative evaluation of a particular feature and to assist tissue characterization in histopathology.{Figure 10}

In this paper, we report on an automated method that conducts whole slide pattern recognition to obtain the stain distribution. The results of our stain recognition algorithm correlate well with manual counting. Regression analysis showed a correlation coefficient of 0.8961 between manual counting and our stain recognition algorithm, suggesting that the labor intensive manual counting may be replaced by our automated recognition algorithm, which is critical for further analysis of the image data from WSI. Moreover, the ED1 stain distribution image was in good agreement with both in-vivo cellular MRI and ex-vivo cellular MRM. The high density areas revealed in the ED1 stain distribution images correlated well with the hypo-intensity areas generated by iron-oxide-labeled macrophages, suggesting that our method can serve as a surrogate to reveal the distribution of macrophages across tissue sections.

The novelty of our approach includes the following: First, we have implemented a parallel processing approach that makes whole slide pattern recognition possible. We have demonstrated the performance using a pixel-wise stain recognition algorithm. It is possible that the same processing approach can be combined with more advanced pattern recognition algorithms to extract meaningful features from pathology/IHC digital slides. Second, the stain distribution images obtained from our method offered a panorama of pathology information instead of a fraction of a view under the light microscope. This panoramic view is less susceptible to errors caused by subjective selection of the data. In contrast, traditional pathology validation often requires expert selection of the lesion site, making the process subjective and potentially biased. Whole slide pattern recognition leaves no bias and presents all the information across the pathology slide to offer more objective results. Third, the stain distribution images are quantitative information that can be further analyzed statistically. This unique feature is provided by the kernel density estimator, which is a widely-used regression approach in the field of statistics and has been used in a variety of real world applications. [30] Lastly, our method can be fully automated and no human intervention is needed to obtain the stain distribution mapping. This offers a highly objective and efficient method that may enable a high throughput processing of data and massive screening.

Despite the many advantages mentioned above, there are several possible pitfalls in our method that are noteworthy of discussion. The accuracy of our method depends on the performance of the recognition algorithm and it is possible that an algorithm may give false positive results secondary to poor slide preparation or artifacts in the preparation. Similarly, the stain variability may alter the recognition results and may create a false perception of the changes in the stain distribution. Both pitfalls may lead to misleading results that are hard to identify and thus, a post-processing inspection may be needed for quality control.

There are also limitations in our experiment design. First, we did only a qualitative comparison and did not conduct rigorous registration to compare our stain distribution imaging with MRI. This limitation is due to the fact that the tissue specimen deforms substantially in the fixation process and the spatial information between MRI and WSI cannot be perfectly aligned. Furthermore, the ex-vivo MRI of the tissue-embedded paraffin block cannot be acquired because the MR spectrum is dominated by the paraffin resonance. Another limitation of our experiment is that we did not examine the positive and negative predicting values of our stain recognition algorithm. Although the accuracy of the morphology-based stain recognition has already been examined, [10],[18],[19] it is unclear whether the same performance can be achieved in counting macrophages due to their highly pleomorphic shapes.

Our stain recognition algorithm also has limitations. The current setting is optimized to identify stains with an oval shape, but there can be other morphological presentations in the pathology slides. To overcome this limitation, the algorithm can be customized to accommodate different morphology of the stains. Moreover, although there is a positive correlation between the number of the recognized stains and manual cell counts, our stain recognition algorithm still tends to count more than that of manual counting. This may be due to the fact that macrophages have cytoplasmic projections that can lead to separated regions and result in duplicated counts. In addition, the nuclei of macrophages are often lobulated, further adding the complexity of the cell recognition in our use case scenario. Counting the number of macrophages is still a challenging task that requires more future studies. Last, the stain distribution value can be affected by several factors, including the thickness of the sections, the z-depth and the bandwidth used in the kernel density estimator. Therefore, a comparison based on the stain-distribution value should be conducted in a carefully controlled manner.

A method is proposed to obtain stain distribution across a pathology slide. The obtained stain distribution image represents a panorama of the pathology information and can be used in both qualitative and quantitative analyses. This imaging approach may also assist pathologists in clinical diagnosis by offering them a toolkit for multiple applications in diagnostic pathology as well as yield newer methods to use image analysis to quantitate and analyze biomarkers, which are both diagnostic and prognostic. In addition, the algorithm described in this study may serve as a new and systematic approach to validate other imaging modalities. The ED1 stain distribution image confirms that the cellular MRI technique is able to detect the macrophage infiltration of cardiac transplantation rejection or cardiac ischemic injury in our rat models.

We would like to thank Ms. Lily Chen and Mr. Brent Barbe for manual counting of the macrophages in the IHC slides. Our research is supported by NIH grants (P41EB-001977 and R01HL-081349)

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By David A. Baum

Baum and Smith, either professors evolutionary biology and researchers within the box of systematics, current this hugely obtainable advent to phylogenetics and its significance in smooth biology. Ever due to the fact Darwin, the evolutionary histories of organisms were portrayed within the type of branching timber or “phylogenies.”  in spite of the fact that, the vast importance of the phylogenetic timber has grow to be preferred simply relatively lately. Phylogenetics has myriad purposes in biology, from learning the good points found in ancestral organisms, to discovering the assets of invasive species and infectious ailments, to picking our closest residing (and extinct) hominid relatives.  Taking a conceptual process, Tree considering introduces readers to the translation of phylogenetic timber, how those bushes will be reconstructed, and the way they are often used to reply to organic questions. Examples and bright metaphors are included all through, and every bankruptcy concludes with a suite of difficulties, helpful for either scholars and lecturers. Tree considering is must-have textbook for any pupil looking a superior origin during this basic region of evolutionary biology.

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So, why kill oﬀ an invading bacterium if it conveys a survival advantage? Why would an invader try to kill oﬀ his host cell if it is enjoying a comfy lifestyle with a stable food source and shelter from the elements? 5 to 2 billion years. Gradual evolution of cells relied on the chance copying of errors of genetic matter or the exchange of small bits of DNA between cells. More rapid changes could occur if cells acquired big chunks of genetic material from other organisms, along with the features encoded by their genes.

Fantastical rock formations surround you. Your grip on reality begins to slip. Have you time-travelled back billions of years? Are you on an alien planet? Surely, this sight cannot exist on twenty-ﬁrst century Earth. Nevertheless, to a scientist who has descended deep under the ocean in a tiny submarine to study new forms of life, the sight is both real and truly amazing. 8 kilometers) off the coast of Washington and Oregon, a ridge of undersea volcanic mountains rises along an area where tectonic plates intersect.

They are an important part of ocean life, serving as a critical link in the food chain for higher animals. A long ﬂagellum at one end propels the tiny organisms through the water as they collect food particles in their tentacle-like ﬁlaments arranged like a collar. Scientists have only recently learned that these protists are among the closest single-celled relatives to animals. It is likely that some single-celled ancestor gave rise to both present-day choanoﬂagellates and another line that led to the animal kingdom.

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Summary: determine the suspicious activities by logging into the

Summary: The
Investigation team has been able to detect and collect few pieces of evidence
on which they can easily detect the suspect of series of kidnappings happened
lately. The international stuffed slave market has become the main source where
all the illegal activities been taking up. The Toy story Investigation Response
(TSIR) team has finally recorded all the evidence.

The TSPD has collected
few items from the suspect to which he claimed he is innocent. The possible
objectives are the data which was stored and what data were stored in the hard
drive of the computer, also the registry and the browser history and files for
which the data can collect of the last few days. The Investigation team can
determine the suspicious activities by logging into the data and decoding the
files by inserting commands that will give the exact information loaded into

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The TSIR team can
investigate all the data which would help them more and will help them in
reaching to the main suspect behind this kidnapping. Sherriff Woody has made
the list of all possible suspects and is collecting data. The Investigation
will clarify if the suspect they have detained is the main culprit behind it or
other sources are also there which has been working on his system in his

The software which the
investigation team has developed will be able to detect all the files and
folders stored in the disks and registry that is connected to the current
activity. The team requires tools to generate the metadata should have TSK (The
Sleuth Kit) File system that would help to investigate metadata.

The management system
of TSK The TSK organizes the information inside database system in the 5
different classes: Data Unit, System File, data stored as Metadata, the subject
of the file and the application of it. This chain stores all information such
as recent access information of items required authorization and pointers to
the information data that is linked with the file directory of the system.

The detailed related to
the key file can be obtained but difficult to memorize the numerical secret
number or coding of it. Hence, all statistics which is its data only has been done
in device is kept which needs to be taken out with the help of TSK system. The
Deleted files can be restored using the same tool which is advanced in
retrieving information i.e. all the previously mentioned techniques can be
merged alone by the TSK System.

The System will detect
all the directories and will flash all the names it detected. It will not display
those names that are unallocated or marked unallocated it will not flash them
up. The unallocated information can be easily found with the help of F.L.S. by
scraping into the M.F.T that will resend into the address list called as the
directory of it.

This will make easier
to explain and detect the files removed by the suspect and in what way they
were displayed in data system in Master File Table. This is very easy to tell
the difference between that information which was deleted because the files
that were recovered from M.F.T shows a “-” sign in their name such as -/ab than
ab/ab. The file carving of the fragmented file can be done by using the tool
Scalpel. Based on the data fragment types or data file prototype, the
operations are performed by Scalpel. These prototypes are based on general
expressions and binary strings. Various default prototypes are stored in the
configuration file which is stored in “scalpel.conf”. Scalpel
supports the comments in the configuration file which is used to explain the structure
of file carving prototypes.

 The data can be retrieved by carving  with which reads data from Top and Bottom of
page which then match all raw files, images etc. It can easily carve all files
types like NTFS, FATx or raw partitions as well. All kinds of services like
file recovering or even investigation can be easily done by this application.
Firefox SQLite Manager Addon can easily detect all those sites which have been
used regularly without knowledge of the Admin and can show all details loaded
on the screen. It will inform the investigator about how many files were
accidentally opened or casually opened.

History file will also
be detected during the same time. The intent and the occurrence behind data
searched will get loaded into it verifying the intent or the purpose from the
history of the browser. Regripper is the tool that will help in identifying the
registry information Win32R which is registry component which is used to access
the registry information. This operation is done in an object-oriented manner.

The Registry key nodes
within the hive file can be detected including the data and value node as well.
The last name can be easily retrieved by the key node access, and it enables
after parsing it to an investigator in the easiest form to understand it. The
best feature of this is that it enables the function and passes reports in a
readable manner to understand easily. Wireshark has the immense good role in
identifying activities inside the system. In the detection data such as emails
and links inside them are generated by this system which could become the
potential evidence for the digital forensics team.

 No one can be able to cheat ever by fraud like
stealing someone’s connection by any malfunction in his or her IP address. The
activities of a person can be easily detected by using Wireshark, it enables to
detect IP and MAC addresses of suspicious person. It will help in restoring all
the information behind the screen such as emails and links that were used and
deleted. The power of Wireshark can be enhanced by using tools like aircrack_ng
which is used to examine the wireless network’s traffic, this enables Wireshark
a powerful tool.


Second star to the right and straight on ’til morning—Astrophysics white papers

What will be the next big thing in astronomy? One of the hard things about research is that you often don’t know what you will discover before you embark on an investigation. An idea might work out, or it might not, or along the way you might discover something unexpected which is far more interesting. As you might imagine, this can make laying definite plans difficult…

However, it is important to have plans for research. While you might not be sure of the outcome, it is necessary to weigh the risks and rewards associated with the probable results before you invest your time and taxpayers’ money!

To help with planning and prioritising, researchers in astrophysics often pull together white papers [bonus note]. These are sketches of ideas for future research, arguing why you think they might be interesting. These can then be discussed within the community to help shape the direction of the field. If other scientists find the paper convincing, you can build support which helps push for funding. If there are gaps in the logic, others can point these out to ave you heading the wrong way. This type of consensus building is especially important for large experiments or missions—you don’t want to spend a billion dollars on something unless you’re really sure it is a good idea and lots of people agree.

I have been involved with a few white papers recently. Here are some key ideas for where research should go.

Ground-based gravitational-wave detectors: The next generation

We’ve done some awesome things with Advanced LIGO and Advanced Virgo. In just a couple of years we have revolutionized our understanding of binary black holes. That’s not bad. However, our current gravitational-wave observatories are limited in what they can detect. What amazing things could we achieve with a new generation of detectors?

It can take decades to develop new instruments, therefore it’s important to start thinking about them early. Obviously, what we would most like is an observatory which can detect everything, but that’s not feasible. In this white paper, we pick the questions we most want answered, and see what the requirements for a new detector would be. A design which satisfies these specifications would therefore be a solid choice for future investment.

Binary black holes are the perfect source for ground-based detectors. What do we most want to know about them?

  1. How many mergers are there, and how does the merger rate change over the history of the Universe? We want to know how binary black holes are made. The merger rate encodes lots of information about how to make binaries, and comparing how this evolves compared with the rate at which the Universe forms stars, will give us a deeper understanding of how black holes are made.
  2. What are the properties (masses and spins) of black holes? The merger rate tells us some things about how black holes form, but other properties like the masses, spins and orbital eccentricity complete the picture. We want to make precise measurements for individual systems, and also understand the population.
  3. Where do supermassive black holes come from? We know that stars can collapse to produce stellar-mass black holes. We also know that the centres of galaxies contain massive black holes. Where do these massive black holes come from? Do they grow from our smaller black holes, or do they form in a different way? Looking for intermediate-mass black holes in the gap in-between will tells us whether there is a missing link in the evolution of black holes.
Detection horizon as a function of binary mass for Advanced LIGO, A+, Cosmic Explorer and the Einstein Telescope

The detection horizon (the distance to which sources can be detected) for Advanced LIGO (aLIGO), its upgrade A+, and the proposed Cosmic Explorer (CE) and Einstein Telescope (ET). The horizon is plotted for binaries with equal-mass, nonspinning components. Adapted from Hall & Evans (2019).

What can we do to answer these questions?

  1. Increase sensitivity! Advanced LIGO and Advanced Virgo can detect a 30 M_\odot + 30 M_\odot binary out to a redshift of about z \approx 1. The planned detector upgrade A+ will see them out to redshift z \approx 2. That’s pretty impressive, it means we’re covering 10 billion years of history. However, the peak in the Universe’s star formation happens at around z \approx 2, so we’d really like to see beyond this in order to measure how the merger rate evolves. Ideally we would see all the way back to cosmic dawn at z \approx 20 when the Universe was only 200 million years old and the first stars light up.
  2. Increase our frequency range! Our current detectors are limited in the range of frequencies they can detect. Pushing to lower frequencies helps us to detect heavier systems. If we want to detect intermediate-mass black holes of 100 M_\odot we need this low frequency sensitivity. At the moment, Advanced LIGO could get down to about 10~\mathrm{Hz}. The plot below shows the signal from a 100 M_\odot + 100 M_\odot binary at z = 10. The signal is completely undetectable at 10~\mathrm{Hz}.

    Gravitational wave signal from a binary of two 100 solar mass black holes at a redshift of 10

    The gravitational wave signal from the final stages of inspiral, merger and ringdown of a two 100 solar mass black holes at a redshift of 10. The signal chirps up in frequency. The colour coding shows parts of the signal above different frequencies. Part of Figure 2 of the Binary Black Holes White Paper.

  3. Increase sensitivity and frequency range! Increasing sensitivity means that we will have higher signal-to-noise ratio detections. For these loudest sources, we will be able to make more precise measurements of the source properties. We will also have more detections overall, as we can survey a larger volume of the Universe. Increasing the frequency range means we can observe a longer stretch of the signal (for the systems we currently see). This means it is easier to measure spin precession and orbital eccentricity. We also get to measure a wider range of masses. Putting the improved sensitivity and frequency range together means that we’ll get better measurements of individual systems and a more complete picture of the population.

How much do we need to improve our observatories to achieve our goals? To quantify this, lets consider the boost in sensitivity relative to A+, which I’ll call \beta_\mathrm{A+}. If the questions can be answered with \beta_\mathrm{A+} = 1, then we don’t need anything beyond the currently planned A+. If we need a slightly larger \beta_\mathrm{A+}, we should start investigating extra ways to improve the A+ design. If we need much larger \beta_\mathrm{A+}, we need to think about new facilities.

The plot below shows the boost necessary to detect a binary (with equal-mass nonspinning components) out to a given redshift. With a boost of \beta_\mathrm{A+} = 10 (blue line) we can survey black holes around 10 M_\odot30 M_\odot across cosmic time.

Boost to detect a binary of a given mass at a given redshift

The boost factor (relative to A+) \beta_\mathrm{A+} needed to detect a binary with a total mass M out to redshift z. The binaries are assumed to have equal-mass, nonspinning components. The colour scale saturates at \log_{10} \beta_\mathrm{A+} = 4.5. The blue curve highlights the reach at a boost factor of \beta_\mathrm{A+} = 10. The solid and dashed white lines indicate the maximum reach of Cosmic Explorer and the Einstein Telescope, respectively. Part of Figure 1 of the Binary Black Holes White Paper.

The plot above shows that to see intermediate-mass black holes, we do need to completely overhaul the low-frequency sensitivity. What do we need to detect a 100 M_\odot + 100 M_\odot binary at z = 10? If we parameterize the noise spectrum (power spectral density) of our detector as S_n(f) = S_{10}(f/10~\mathrm{Hz})^\alpha with a lower cut-off frequency of f_\mathrm{min}, we can investigate the various possibilities. The plot below shows the possible combinations of parameters which meet of requirements.

Noise curve requirements for intermediate-mass black hole detection

Requirements on the low-frequency noise power spectrum necessary to detect an optimally oriented intermediate-mass binary black hole system with two 100 solar mass components at a redshift of 10. Part of Figure 2 of the Binary Black Holes White Paper.

To build up information about the population of black holes, we need lots of detections. Uncertainties scale inversely with the square root of the number of detections, so you would expect few percent uncertainty after 1000 detections. If we want to see how the population evolves, we need these many per redshift bin! The plot below shows the number of detections per year of observing time for different boost factors. The rate starts to saturate once we detect all the binaries in the redshift range. This is as good as you’ll ever going to get.

Detections per redshift bin as a function of boost factor

Expected rate of binary black hole detections R_\mathrm{det} per redshift bin as a function of A+ boost factor \beta_\mathrm{A+} for three redshift bins. The merging binaries are assumed to be uniformly distributed with a constant merger rate roughly consistent with current observations: the solid line is about the current median, while the dashed and dotted lines are roughly the 90% bounds. Figure 3 of the Binary Black Holes White Paper.

Looking at the plots above, it is clear that A+ is not going to satisfy our requirements. We need something with a boost factor of \beta_\mathrm{A+} = 10: a next-generation observatory. Both the Cosmic Explorer and Einstein Telescope designs do satisfy our goals.

Data is pleased. Credit: Paramount

Title: Deeper, wider, sharper: Next-generation ground-based gravitational-wave observations of binary black holes
1903.09220 [astro-ph.HE]
Contribution level: ☆☆☆☆☆ Leading author
Theme music: Daft Punk

Extreme mass ratio inspirals are awesome

We have seen gravitational waves from a stellar-mass black hole merging with another stellar-mass black hole, can we observe a stellar-mass black hole merging with a massive black hole? Yes, these are a perfect source for a space-based gravitational wave observatory. We call these systems extreme mass-ratio inspirals (or EMRIs, pronounced em-rees, for short) [bonus note].

Having such an extreme mass ratio, with one black hole much bigger than the other, gives EMRIs interesting properties. The number of orbits over the course of an inspiral scales with the mass ratio: the more extreme the mass ratio, the more orbits there are. Each of these gives us something to measure in the gravitational wave signal.

The intricate structure of an EMRI orbit

A short section of an orbit around a spinning black hole. While inspirals last for years, this would represent only a few hours around a black hole of mass M = 10^6 M_\odot. The position is measured in terms of the gravitational radius r_\mathrm{g} = GM/c^2. The innermost stable orbit for this black hole would be about r_\mathrm{g} = 2.3. Part of Figure 1 of the EMRI White Paper.

As EMRIs are so intricate, we can make exquisit measurements of the source properties. These will enable us to:

Event rates for EMRIs are currently uncertain: there could be just one per year or thousands. From the rate we can figure out the details of what is going in in the nuclei of galaxies, and what types of objects you find there.

With EMRIs you can unravel mysteries in astrophysics, fundamental physics and cosmology.

Have we sold you that EMRIs are awesome? Well then, what do we need to do to observe them? There is only one currently planned mission which can enable us to study EMRIs: LISA. To maximise the science from EMRIs, we have to support LISA.

Lisa Simpson dancing

As an aspiring scientist, Lisa Simpson is a strong supporter of the LISA mission. Credit: Fox

Title: The unique potential of extreme mass-ratio inspirals for gravitational-wave astronomy
1903.03686 [astro-ph.HE]
Contribution level: ☆☆☆☆☆ Leading author
Theme music: Muse

Bonus notes

White paper vs journal article

Since white papers are proposals for future research, they aren’t as rigorous as usual academic papers. They are really attempts to figure out a good question to ask, rather than being answers. White papers are not usually peer reviewed before publication—the point is that you want everybody to comment on them, rather than just one or two anonymous referees.

Whilst white papers aren’t quite the same class as journal articles, they do still contain some interesting ideas, so I thought they still merit a blog post.

I have blogged about EMRIs before, so I won’t go into too much detail here. It was one of my former blog posts which inspired the LISA Science Team to get in touch to ask me to write the white paper.

The O2 Catalogue—It goes up to 11

Black hole and neutron star masses

The papers

The O2 Catalogue Paper

 1811.12907 [astro-ph.HE]
Data: Catalogue; Parameter estimation results

More details: The O2 Catalogue Paper

The O2 Populations Paper

 1811.12940 [astro-ph.HE]

More details: The O2 Populations Paper

The O2 Catalogue Paper

Synopsis: O2 Catalogue Paper

The instruments

The searches

Inverse false alarm rates

The source properties

All binary masses

All final black hole masses and spins

All effective inspiral spin parameters

All effective precession spin parameters

All distances and inclinations

Waveform reconstructions

Spectrograms and waveforms

Merger rates

NSBH rate upper limits

The O2 Populations Paper

Synopsis: O2 Populations Paper
Favourite part: A maximum black hole mass?

Population vs black hole mass

To model the spins:

Parametric and binned spin magnitude distributions

Redshift evolution

Rate versus redshift

Bonus notes

Trust LIGO

Burning bright

August 2017—Something fishy or just Poisson statistics?

Detector sensitivity across O2

Sign errors

New sources

Accuracy of inference on the physics of binary evolution from gravitational-wave observations

Gravitational-wave astronomy lets us observing binary black holes. These systems, being made up of two black holes, are pretty difficult to study by any other means. It has long been argued that with this new information we can unravel the mysteries of stellar evolution. Just as a palaeontologist can discover how long-dead animals lived from their bones, we can discover how massive stars lived by studying their black hole remnants. In this paper, we quantify how much we can really learn from this black hole palaeontology—after 1000 detections, we should pin down some of the most uncertain parameters in binary evolution to a few percent precision.

Life as a binary

There are many proposed ways of making a binary black hole. The current leading contender is isolated binary evolution: start with a binary star system (most stars are in binaries or higher multiples, our lonesome Sun is a little unusual), and let the stars evolve together. Only a fraction will end with black holes close enough to merge within the age of the Universe, but these would be the sources of the signals we see with LIGO and Virgo. We consider this isolated binary scenario in this work [bonus note].

Now, you might think that with stars being so fundamentally important to astronomy, and with binary stars being so common, we’d have the evolution of binaries figured out by now. It turns out it’s actually pretty messy, so there’s lots of work to do. We consider constraining four parameters which describe the bits of binary physics which we are currently most uncertain of:

  • Black hole natal kicks—the push black holes receive when they are born in supernova explosions. We now the neutron stars get kicks, but we’re less certain for black holes [bonus note].
  • Common envelope efficiency—one of the most intricate bits of physics about binaries is how mass is transferred between stars. As they start exhausting their nuclear fuel they puff up, so material from the outer envelope of one star may be stripped onto the other. In the most extreme cases, a common envelope may form, where so much mass is piled onto the companion, that both stars live in a single fluffy envelope. Orbiting inside the envelope helps drag the two stars closer together, bringing them closer to merging. The efficiency determines how quickly the envelope becomes unbound, ending this phase.
  • Mass loss rates during the Wolf–Rayet (not to be confused with Wolf 359) and luminous blue variable phases–stars lose mass through out their lives, but we’re not sure how much. For stars like our Sun, mass loss is low, there is enough to gives us the aurora, but it doesn’t affect the Sun much. For bigger and hotter stars, mass loss can be significant. We consider two evolutionary phases of massive stars where mass loss is high, and currently poorly known. Mass could be lost in clumps, rather than a smooth stream, making it difficult to measure or simulate.

We use parameters describing potential variations in these properties are ingredients to the COMPAS population synthesis code. This rapidly (albeit approximately) evolves a population of stellar binaries to calculate which will produce merging binary black holes.

The question is now which parameters affect our gravitational-wave measurements, and how accurately we can measure those which do?

Merger rate with redshift and chirp mass

Binary black hole merger rate at three different redshifts z as calculated by COMPAS. We show the rate in 30 different chirp mass bins for our default population parameters. The caption gives the total rate for all masses. Figure 2 of Barrett et al. (2018)

Gravitational-wave observations

For our deductions, we use two pieces of information we will get from LIGO and Virgo observations: the total number of detections, and the distributions of chirp masses. The chirp mass is a combination of the two black hole masses that is often well measured—it is the most important quantity for controlling the inspiral, so it is well measured for low mass binaries which have a long inspiral, but is less well measured for higher mass systems. In reality we’ll have much more information, so these results should be the minimum we can actually do.

We consider the population after 1000 detections. That sounds like a lot, but we should have collected this many detections after just 2 or 3 years observing at design sensitivity. Our default COMPAS model predicts 484 detections per year of observing time! Honestly, I’m a little scared about having this many signals…

For a set of population parameters (black hole natal kick, common envelope efficiency, luminous blue variable mass loss and Wolf–Rayet mass loss), COMPAS predicts the number of detections and the fraction of detections as a function of chirp mass. Using these, we can work out the probability of getting the observed number of detections and fraction of detections within different chirp mass ranges. This is the likelihood function: if a given model is correct we are more likely to get results similar to its predictions than further away, although we expect their to be some scatter.

If you like equations, the from of our likelihood is explained in this bonus note. If you don’t like equations, there’s one lurking in the paragraph below. Just remember, that it can’t see you if you don’t move. It’s OK to skip the equation.

To determine how sensitive we are to each of the population parameters, we see how the likelihood changes as we vary these. The more the likelihood changes, the easier it should be to measure that parameter. We wrap this up in terms of the Fisher information matrix. This is defined as

\displaystyle F_{ij} = -\left\langle\frac{\partial^2\ln \mathcal{L}(\mathcal{D}|\left\{\lambda\right\})}{\partial \lambda_i \partial\lambda_j}\right\rangle,

where \mathcal{L}(\mathcal{D}|\left\{\lambda\right\}) is the likelihood for data \mathcal{D} (the number of observations and their chirp mass distribution in our case), \left\{\lambda\right\} are our parameters (natal kick, etc.), and the angular brackets indicate the average over the population parameters. In statistics terminology, this is the variance of the score, which I think sounds cool. The Fisher information matrix nicely quantifies how much information we can lean about the parameters, including the correlations between them (so we can explore degeneracies). The inverse of the Fisher information matrix gives a lower bound on the covariance matrix (the multidemensional generalisation of the variance in a normal distribution) for the parameters \left\{\lambda\right\}. In the limit of a large number of detections, we can use the Fisher information matrix to estimate the accuracy to which we measure the parameters [bonus note].

We simulated several populations of binary black hole signals, and then calculate measurement uncertainties for our four population uncertainties to see what we could learn from these measurements.

Using just the rate information, we find that we can constrain a combination of the common envelope efficiency and the Wolf–Rayet mass loss rate. Increasing the common envelope efficiency ends the common envelope phase earlier, leaving the binary further apart. Wider binaries take longer to merge, so this reduces the merger rate. Similarly, increasing the Wolf–Rayet mass loss rate leads to wider binaries and smaller black holes, which take longer to merge through gravitational-wave emission. Since the two parameters have similar effects, they are anticorrelated. We can increase one and still get the same number of detections if we decrease the other. There’s a hint of a similar correlation between the common envelope efficiency and the luminous blue variable mass loss rate too, but it’s not quite significant enough for us to be certain it’s there.

Correaltions between population parameters

Fisher information matrix estimates for fractional measurement precision of the four population parameters: the black hole natal kick \sigma_\mathrm{kick}, the common envelope efficiency \alpha_\mathrm{CE}, the Wolf–Rayet mass loss rate f_\mathrm{WR}, and the luminous blue variable mass loss rate f_\mathrm{LBV}. There is an anticorrealtion between f_\mathrm{WR} and \alpha_\mathrm{CE}, and hints at a similar anticorrelation between f_|mathrm{LBV} and \alpha_\mathrm{CE}. We show 1500 different realisations of the binary population to give an idea of scatter. Figure 6 of Barrett et al. (2018)

Adding in the chirp mass distribution gives us more information, and improves our measurement accuracies. The fraction uncertainties are about 2% for the two mass loss rates and the common envelope efficiency, and about 5% for the black hole natal kick. We’re less sensitive to the natal kick because the most massive black holes don’t receive a kick, and so are unaffected by the kick distribution [bonus note]. In any case, these measurements are exciting! With this type of precision, we’ll really be able to learn something about the details of binary evolution.

Standard deviation of measurements of population parameters

Measurement precision for the four population parameters after 1000 detections. We quantify the precision with the standard deviation estimated from the Fisher inforamtion matrix. We show results from 1500 realisations of the population to give an idea of scatter. Figure 5 of Barrett et al. (2018)

The accuracy of our measurements will improve (on average) with the square root of the number of gravitational-wave detections. So we can expect 1% measurements after about 4000 observations. However, we might be able to get even more improvement by combining constraints from other types of observation. Combining different types of observation can help break degeneracies. I’m looking forward to building a concordance model of binary evolution, and figuring out exactly how massive stars live their lives.

arXiv: 1711.06287 [astro-ph.HE]
Journal: Monthly Notices of the Royal Astronomical Society; 477(4):4685–4695; 2018
Favourite dinosaur: Professor Science

Bonus notes

Channel selection

In practise, we will need to worry about how binary black holes are formed, via isolated evolution or otherwise, before inferring the parameters describing binary evolution. This makes the problem more complicated. Some parameters, like mass loss rates or black hole natal kicks, might be common across multiple channels, while others are not. There are a number of ways we might be able to tell different formation mechanisms apart, such as by using spin measurements.

Kick distribution

We model the supernova kicks v_\mathrm{kick} as following a Maxwell–Boltzmann distribution,

\displaystyle p(v_\mathrm{kick}) = \sqrt{\frac{2}{\pi}}  \frac{v_\mathrm{kick}^2}{\sigma_\mathrm{kick}^3} \exp\left(\frac{-v_\mathrm{kick}^2}{2\sigma_\mathrm{kick}^2}\right),

where \sigma_\mathrm{kick} is the unknown population parameter. The natal kick received by the black hole v^*_\mathrm{kick} is not the same as this, however, as we assume some of the material ejected by the supernova falls back, reducing the over kick. The final natal kick is

v^*_\mathrm{kick} = (1-f_\mathrm{fb})v_\mathrm{kick},

where f_\mathrm{fb} is the fraction that falls back, taken from Fryer et al. (2012). The fraction is greater for larger black holes, so the biggest black holes get no kicks. This means that the largest black holes are unaffected by the value of \sigma_\mathrm{kick}.

The likelihood

In this analysis, we have two pieces of information: the number of detections, and the chirp masses of the detections. The first is easy to summarise with a single number. The second is more complicated, and we consider the fraction of events within different chirp mass bins.

Our COMPAS model predicts the merger rate \mu and the probability of falling in each chirp mass bin p_k (we factor measurement uncertainty into this). Our observations are the the total number of detections N_\mathrm{obs} and the number in each chirp mass bin c_k (N_\mathrm{obs} = \sum_k c_k). The likelihood is the probability of these observations given the model predictions. We can split the likelihood into two pieces, one for the rate, and one for the chirp mass distribution,

\mathcal{L} = \mathcal{L}_\mathrm{rate} \times \mathcal{L}_\mathrm{mass}.

For the rate likelihood, we need the probability of observing N_\mathrm{obs} given the predicted rate \mu. This is given by a Poisson distribution,

\displaystyle \mathcal{L}_\mathrm{rate} = \exp(-\mu t_\mathrm{obs}) \frac{(\mu t_\mathrm{obs})^{N_\mathrm{obs}}}{N_\mathrm{obs}!},

where t_\mathrm{obs} is the total observing time. For the chirp mass likelihood, we the probability of getting a number of detections in each bin, given the predicted fractions. This is given by a multinomial distribution,

\displaystyle \mathcal{L}_\mathrm{mass} = \frac{N_\mathrm{obs}!}{\prod_k c_k!} \prod_k p_k^{c_k}.

These look a little messy, but they simplify when you take the logarithm, as we need to do for the Fisher information matrix.

When we substitute in our likelihood into the expression for the Fisher information matrix, we get

\displaystyle F_{ij} = \mu t_\mathrm{obs} \left[ \frac{1}{\mu^2} \frac{\partial \mu}{\partial \lambda_i} \frac{\partial \mu}{\partial \lambda_j}  + \sum_k\frac{1}{p_k} \frac{\partial p_k}{\partial \lambda_i} \frac{\partial p_k}{\partial \lambda_j} \right].

Conveniently, although we only need to evaluate first-order derivatives, even though the Fisher information matrix is defined in terms of second derivatives. The expected number of events is \langle N_\mathrm{obs} \rangle = \mu t_\mathrm{obs}. Therefore, we can see that the measurement uncertainty defined by the inverse of the Fisher information matrix, scales on average as N_\mathrm{obs}^{-1/2}.

For anyone worrying about using the likelihood rather than the posterior for these estimates, the high number of detections [bonus note] should mean that the information we’ve gained from the data overwhelms our prior, meaning that the shape of the posterior is dictated by the shape of the likelihood.

Interpretation of the Fisher information matrix

As an alternative way of looking at the Fisher information matrix, we can consider the shape of the likelihood close to its peak. Around the maximum likelihood point, the first-order derivatives of the likelihood with respect to the population parameters is zero (otherwise it wouldn’t be the maximum). The maximum likelihood values of N_\mathrm{obs} = \mu t_\mathrm{obs} and c_k = N_\mathrm{obs} p_k are the same as their expectation values. The second-order derivatives are given by the expression we have worked out for the Fisher information matrix. Therefore, in the region around the maximum likelihood point, the Fisher information matrix encodes all the relevant information about the shape of the likelihood.

So long as we are working close to the maximum likelihood point, we can approximate the distribution as a multidimensional normal distribution with its covariance matrix determined by the inverse of the Fisher information matrix. Our results for the measurement uncertainties are made subject to this approximation (which we did check was OK).

Approximating the likelihood this way should be safe in the limit of large N_\mathrm{obs}. As we get more detections, statistical uncertainties should reduce, with the peak of the distribution homing in on the maximum likelihood value, and its width narrowing. If you take the limit of N_\mathrm{obs} \rightarrow \infty, you’ll see that the distribution basically becomes a delta function at the maximum likelihood values. To check that our N_\mathrm{obs} = 1000 was large enough, we verified that higher-order derivatives were still small.

Michele Vallisneri has a good paper looking at using the Fisher information matrix for gravitational wave parameter estimation (rather than our problem of binary population synthesis). There is a good discussion of its range of validity. The high signal-to-noise ratio limit for gravitational wave signals corresponds to our high number of detections limit.

Inference on gravitational waves from coalescences of stellar-mass compact objects and intermediate-mass black holes

I love collecting things, there’s something extremely satisfying about completing a set. I suspect that this is one of the alluring features of Pokémon—you’ve gotta catch ’em all. The same is true of black hole hunting. Currently, we know of stellar-mass black holes which are a few times the mass of our Sun, up to a few tens of the mass of our Sun (the black holes of GW150914 are the biggest yet to be observed), and we know of supermassive black holes, which are ten thousand to ten billion times the mass our Sun. However, we are missing intermediate-mass black holes which lie in the middle. We have Charmander and Charizard, but where is Charmeleon? The elusive ones are always the most satisfying to capture.

Knitted black hole

Adorable black hole (available for adoption). I’m sure this could be a Pokémon. It would be a Dark type. Not that I’ve given it that much thought…

Intermediate-mass black holes have evaded us so far. We’re not even sure that they exist, although that would raise questions about how you end up with the supermassive ones (you can’t just feed the stellar-mass ones lots of rare candy). Astronomers have suggested that you could spot intermediate-mass black holes in globular clusters by the impact of their gravity on the motion of other stars. However, this effect would be small, and near impossible to conclusively spot. Another way (which I’ve discussed before), would to be to look at ultra luminous X-ray sources, which could be from a disc of material spiralling into the black hole.  However, it’s difficult to be certain that we understand the source properly and that we’re not misclassifying it. There could be one sure-fire way of identifying intermediate-mass black holes: gravitational waves.

The frequency of gravitational waves depend upon the mass of the binary. More massive systems produce lower frequencies. LIGO is sensitive to the right range of frequencies for stellar-mass black holes. GW150914 chirped up to the pitch of a guitar’s open B string (just below middle C). Supermassive black holes produce gravitational waves at too low frequency for LIGO (a space-based detector would be perfect for these). We might just be able to detect signals from intermediate-mass black holes with LIGO.

In a recent paper, a group of us from Birmingham looked at what we could learn from gravitational waves from the coalescence of an intermediate-mass black hole and a stellar-mass black hole [bonus note].  We considered how well you would be able to measure the masses of the black holes. After all, to confirm that you’ve found an intermediate-mass black hole, you need to be sure of its mass.

The signals are extremely short: we only can detect the last bit of the two black holes merging together and settling down as a final black hole. Therefore, you might think there’s not much information in the signal, and we won’t be able to measure the properties of the source. We found that this isn’t the case!

We considered a set of simulated signals, and analysed these with our parameter-estimation code [bonus note]. Below are a couple of plots showing the accuracy to which we can infer a couple of different mass parameters for binaries of different masses. We show the accuracy of measuring the chirp mass \mathcal{M} (a much beloved combination of the two component masses which we are usually able to pin down precisely) and the total mass M_\mathrm{total}.

Measurement of chirp mass

Measured chirp mass for systems of different total masses. The shaded regions show the 90% credible interval and the dashed lines show the true values. The mass ratio q is the mass of the stellar-mass black hole divided by the mass of the intermediate-mass black hole. Figure 1 of Haster et al. (2016).

Measurement of total mass

Measured total mass for systems of different total masses. The shaded regions show the 90% credible interval and the dashed lines show the true values. Figure 2 of Haster et al. (2016).

For the lower mass systems, we can measure the chirp mass quite well. This is because we get a little information from the part of the gravitational wave from when the two components are inspiralling together. However, we see less and less of this as the mass increases, and we become more and more uncertain of the chirp mass.

The total mass isn’t as accurately measured as the chirp mass at low masses, but we see that the accuracy doesn’t degrade at higher masses. This is because we get some constraints on its value from the post-inspiral part of the waveform.

We found that the transition from having better fractional accuracy on the chirp mass to having better fractional accuracy on the total mass happened when the total mass was around 200–250 solar masses. This was assuming final design sensitivity for Advanced LIGO. We currently don’t have as good sensitivity at low frequencies, so the transition will happen at lower masses: GW150914 is actually in this transition regime (the chirp mass is measured a little better).

Given our uncertainty on the masses, when can we conclude that there is an intermediate-mass black hole? If we classify black holes with masses more than 100 solar masses as intermediate mass, then we’ll be able to say to claim a discovery with 95% probability if the source has a black hole of at least 130 solar masses. The plot below shows our inferred probability of there being an intermediate-mass black hole as we increase the black hole’s mass (there’s little chance of falsely identifying a lower mass black hole).

Intermediate-mass black hole probability

Probability that the larger black hole is over 100 solar masses (our cut-off mass for intermediate-mass black holes M_\mathrm{IMBH}). Figure 7 of Haster et al. (2016).

Gravitational-wave observations could lead to a concrete detection of intermediate mass black holes if they exist and merge with another black hole. However, LIGO’s low frequency sensitivity is important for detecting these signals. If detector commissioning goes to plan and we are lucky enough to detect such a signal, we’ll finally be able to complete our set of black holes.

arXiv: 1511.01431 [astro-ph.HE]
Journal: Monthly Notices of the Royal Astronomical Society457(4):4499–4506; 2016
Birmingham science summary: Inference on gravitational waves from coalescences of stellar-mass compact objects and intermediate-mass black holes (by Carl)
Other collectables: Breakthrough, Gruber, Shaw, Kavli

Bonus notes

The coalescence of an intermediate-mass black hole and a stellar-mass object (black hole or neutron star) has typically been known as an intermediate mass-ratio inspiral (an IMRI). This is similar to the name for the coalescence of a a supermassive black hole and a stellar-mass object: an extreme mass-ratio inspiral (an EMRI). However, my colleague Ilya has pointed out that with LIGO we don’t really see much of the intermediate-mass black hole and the stellar-mass black hole inspiralling together, instead we see the merger and ringdown of the final black hole. Therefore, he prefers the name intermediate mass-ratio coalescence (or IMRAC). It’s a better description of the signal we measure, but the acronym isn’t as good.

Parameter-estimation runs

The main parameter-estimation analysis for this paper was done by Zhilu, a summer student. This is notable for two reasons. First, it shows that useful research can come out of a summer project. Second, our parameter-estimation code installed and ran so smoothly that even an undergrad with no previous experience could get some useful results. This made us optimistic that everything would work perfectly in the upcoming observing run (O1). Unfortunately, a few improvements were made to the code before then, and we were back to the usual level of fun in time for The Event.


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Background of the Study Language is very important, because it always used in our daily activities to communicate with the others people. We cant communicate with each other without language because it is one of the communication tools that used by people in the world. We can use language to deliver our messages through spoken and written. There are many kind of languages in the world. But many people in the world using English to communicate with the others from different countries, because English is an international language. Entering the era of globalization, every individual to prepare a reliable power source, especially in the field of communication. In this case the role of English is required in both the control and communication technology to interact directly. English is a foreign language in Indonesia. English should be

studied because of the use of the breadth of the language of international communication. To be able to communicate with people of different cultural backgrounds and nationalities, English has become the main option that is often used in communication. In teaching learning process of English concentrates on four skills, there are writing, reading, listening, and speaking. Writing is one of four language basic skills. It is very important in teaching and learning English. According to Gebhardt and Down Rodrigus (1989 : 1), writing is one of the most important things you do in college (in It is an activity that create a note or an information through a media, such as a book and a pen.

It is generally agreed that writing is the most difficult skill to master. This is due not only to the need to generate and organize ideas using an appropriate choice of vocabulary, sentence, and paragraph organization but also to turn such ideas into a readable text (Richard & Renandya, 2002 in Endang Fauziati. 2010. Teaching Englisgh as a Foreign Language: Surakarta: Era Pustaka Utama). It can be seen at the students in SMP Negeri 1 Bendosari, especially in writing recount text. The researcher get the informations from interview with the English teacher. The teacher said that the in the eight grade students in English subject is in writing, especially recount text. First, they are difficulties when they will write their ideas on writing. Second, they have a low vocabularies to develop their writing. The last, maybe the students not understand about grammar. Based on the explanation above, the researcher give solution using picture series. It is on of media that can used in teaching and learning process. The researcher hopes that using picture series can help the students in writing recount text. So, the researcher choose the title IMPROVING STUDENTS WRITING SKILL USING PICTURE SERIES IN TEACHING RECOUNT TEXT IN THE VII GRADE OF SMP NEGERI 1 BENDOSARI 2012/ 2013 ACADEMIC YEARS

B. Identification of the Problem The researcher get the informations from interview with the English teacher of SMP Negeri 1 Bendosari. There are the informations that the researcher found: 1) she said that the students get difficulties when the students write their ideas or arranged the word into a good sentences. 2) the students vocabulary and grammar is low.

C. Limitation of the Problem Based on the problem, the researcher focuse in writing and give solution in teaching learning writing process. Because in writing we also

learn about vacabulary and grammar. So, learning writing also can learning two masteries. The researcher give a way in the teaching learning process of writing, especially the students writing recount text using picture series. Recount text is a text that telling the reader about one story, action or activity. Its goal is to entertaining or informing the reader. Picture series will make the students interesting in teaching learning process because they can see the pictures to help and they will write or describe from the picture series that we given.

D. Formulation of the Problem In this research, the researcher tries to describe the students ability in writing skill of recount text using picture series. So, the formulation is: Can picture series improve the students writing skill in teaching recount text in the VII grade of SMP Negeri 1 Bendosari?

E. The Objective of the Study Based on the formulation of the problem above, the objective of the study is: To improve the students writing skill in teaching recount text in the VII grade of SMP Negeri 1 Bendosari.

F. The Benefit of the Study The writer hopes this research can give contribution to the English teaching and learning. It has three major, there are: a. For students Recount text is the text that tell the students own experiences. So, it can simple and write their history, biography, and their experiences in the past.

b. For teacher This research will be one way to use the picture series media in teaching writing, it is one of medi that effective, efficient and will make the students become active during the learning process.

c. For the readers This research will be useful to the readers who are interested in analyzing teaching writing.


Hydrazine Analyzer – Model CA6

CA6 Analyzer

The colorimetric analyzer is assembled with two separated sections with two lockable doors. The bottom section is the LIQUIDS section. It includes all of the components involved in the flow, mixing and reaction stages of the sample and reagents (sampling pump, colorimetric reaction cell, reagents micro pumps,..). Numerous analysis configurations can be programmed, depending on accessories and of the number of micro pumps mounted in the Liquid Section. The top section is the ELECTRICAL enclosure. It includes the main power supply, the controller PCB assembly and the touch screen interface.

A typical Analysis Program in the Model CA-6 Iron Analyzer would have the following structure: Rinse the colorimetric reaction cell with the sample solution and take a sample, add one or more reagents, like a buffer or masking agent and then make the first measurement, the Reference measurement. The Reference measurement eliminates interfering factors such as sample color and turbidity, miscellaneous color from the reagents and refractive index variations.

After the reference reading, the color producing reagent(s) are added. The sample is mixed and allowed time to complete the color forming reactions before taking the second measurement, the Reading measurement. The reference and reading values are used to calculate the concentration using the calibration factor. The reaction cell is drained and rinsed several times before starting the next measurement.

Model CA-6 Hydrazine Analyzer

The CA-6 can also be configured to measure: Aluminum, Ammonia, Chlorine, Chloride, Chromium VI, Copper, Cyanide, Hardness, Iron, Manganese, Nickel, Nitrate, Nitrite, Phosphate, Total Phosphate, Silica, Sulfate, Zinc

Visit our ECD Store


To Sleep, Perchance to Dream: Helping a Child with ADHD Get a Better Night’s Sleep

Research has shown that up to seven of every 10 children with ADHD have clinical sleep disturbances. This is important because disturbed or inadequate sleep can exacerbate the symptoms of ADHD including hyperactivity, impulsivity, or irritability. Here are a number of steps parents can take to help a child with ADHD get a better night’s sleep.

Avoiding the Internet Rabbit Hole When You Have ADHD

The internet has evolved into a wonderfully useful medium for learning new things, staying up on the news, shopping, and keeping in touch with friends on social media. For individuals with ADHD, it provides instant rewards, lots of stimulation, and an almost infinite variety of things to explore. But it can draw the ADHD brain into an endless rabbit hole where significant amounts of unproductive time is spent. Here are some tips you can use to help keep your Internet usage in check.

Heading to College with ADHD

The transition from high school to college can be difficult if you have ADHD. There are more distractions, more to manage in the daily routine in terms of classes and social life, a more challenging academic environment, and less day-to-day support from parents. These factors can combine to make college a struggle and academic success less certain. Here are some things you can do to improve your odds of a successful start to college if you have ADHD.

The Daily Groove: Learning to Love Routines When You Have ADHD

You have ADHD and your life seems chaotic and disorganized. You want to add more structure to your daily activities. You need to approach it carefully. Making changes that are too big or complicated, or tackling too much at one time generally won’t work. If you add structure in small steps, you won’t always get it 100 percent right, but you’ll probably be better off than you were before. We offer tips to help you create a daily routine that will give your life structure without being burdensome.


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New hotel brand targets adventure-seeking Millennials

New hotel brand targets adventure-seeking Millennials

Fujita Kanko Inc., a Japanese hospitality company headquartered in Tokyo, announced the launch of “Tavinos,” a new hotel brand aimed at adventure-seeking millennials and like-minded travelers from around the world.

According to the Japan National Tourism Organization (JNTO), the number of international visitors to Japan has increased in recent years, hitting a record high in 2017. Fujita Kanko is adding this brand to their portfolio of 70+ well-established properties to target international visitors and millennials, a fast-growing, yet relatively untapped segment for the company.

With a state-of-the-art blending of design, space, and technology, the company is positioning Tavinos as a sleek hub for international visitors to meet fellow travelers, gather local information, and have fun during their travels.
By implementing technologies such as an AI concierge and automated check-in/check-out as well as minimal staffing, Tavinos can offer average room rates of 10,000 yen/night for a twin/double room at premier urban locations.

“There are already affordable accommodations throughout Japan, but travelling millennials are hungry for the community of a hostel and the professionalism and expertise of an experienced hotelier,” says Akira Segawa, President & CEO of Fujita Kanko. “Tavinos will offer what outgoing and adventurous travelers want: the opportunity to meet interesting people, plan activities together, and explore.”

Tavinos’ design concept is based on Japanese pop culture, with eye-popping Manga (animation), covering the hotel. The stylish guest rooms, 12 square meters/129 square feet on average, are minimally furnished with smart storage to maximize space. Continental breakfast will be available in the spacious lobby lounge, also serving as a communal area for guests to meet and mingle. An AI concierge will provide local information and matching for guests interested in similar activities.

Fujita Kanko will open the first 188-room Tavinos in Hamamatsu-cho, Tokyo, in August 2019, followed by the second 278-room Tavinos in Asakusa, Tokyo, in May, 2020.