<p>The authors used the <a href="http://www.sdss3.org/" rel="nofollow noreferrer">SDSS (Sloan Digital Sky Survey) III dataset</a> (data release 9):</p>
<blockquote>
<p>We use a state-of-the-art probabilistic algorithm, adapted to localise and classify galaxy clusters from other astronomical objects in SDSS imaging.</p>
</blockquote>
<p>This data can be accessed through <a href="https://skyview.gsfc.nasa.gov/current/cgi/titlepage.pl" rel="nofollow noreferrer">Nasa SkyView</a>. The training and test sets were constructed from subsets of the <a href="https://ui.adsabs.harvard.edu/abs/1958ApJS....3..211A/abstract" rel="nofollow noreferrer">Abell catalogue</a>:</p>
<blockquote>
<p>We use the Abell galaxy clusters identified in the <a href="https://iopscience.iop.org/article/10.1088/0067-0049/199/2/34" rel="nofollow noreferrer">Wen et. al (2012)</a> catalogue,
to obtain the labelled data needed to create the training set.</p>
</blockquote>
<p>Most of the clusters in the Abell catalogue have been verified, but not all. Hence the use only of those which are included in Wen et. al (2012). The clusters were filtered based on the following criteria (section 2.2):</p>
<ul>
<li>Photometric redshift (<span class="math-container">$z$</span>) range limited to <span class="math-container">$0.1 &lt; z &lt; 0.2$</span>.</li>
<li>Minimum of 20 galaxy members within <span class="math-container">$R_{200}$</span> radius.</li>
</ul>
<p><span class="math-container">$497$</span> Abell clusters satisfy these criteria. The authors applied translational shifts to augment the data:</p>
<blockquote>
<p>... one of the properties of the FasterRCNN algorithm is translational invariance, which means the algorithm is robust at learning translated objects. We train the algorithm to recognise that an object could appear at any location in an image. Since our method  applies a random offset to the input coordinates via translation we augment the sample set three additional times, which boosts the sample size to <span class="math-container">$1988$</span>.</p>
</blockquote>
<p>This set of <span class="math-container">$1988$</span> inputs was randomly sampled to produce the training and test sets:</p>
<blockquote>
<p>The training set is made up of <span class="math-container">$∼90$</span> percent of the sample set consisting of <span class="math-container">$1784$</span> labelled
galaxy clusters and the test set is made up of the remaining <span class="math-container">$∼10$</span> per cent consisting of <span class="math-container">$204$</span> labelled galaxy clusters.</p>
</blockquote>
<p>The authors do not provide the translated data. However, since the process is statistically robust, applying three different set of random translations to the <span class="math-container">$497$</span> images and running the model should produce results which are consistent with those reported. This is, in fact, a fundamental assumption of the technique.</p>

CC BY-SA 4.0
2020-08-13T04:37:30.363Z
Thanks a lot :)
Madara
CC BY-SA 4.0
2020-08-07T23:46:55.863Z
2020-08-07T23:46:55.863Z
William Miller
<p>I was reading <a href="https://arxiv.org/pdf/1906.08784.pdf" rel="nofollow noreferrer">Deep-CEE I: Fishing for Galaxy Clusters with Deep Neural Nets</a> and I couldn't find the dataset.</p>
<p>Could someone provide me with the links related to this?</p>

CC BY-SA 4.0
2020-08-11T22:37:27.030Z
Have you had a chance to look at my answer?
William Miller
CC BY-SA 4.0
2020-08-12T19:02:35.523Z
@WilliamMiller I did! :-)
uhoh
<galaxy-cluster><neural-networks>
Deep-CEE I data
Madara
# Deep-CEE I data

(Asked by: username_0 on 2020-08-04)

I was reading [Deep-CEE I: Fishing for Galaxy Clusters with Deep Neural Nets](https://arxiv.org/pdf/1906.08784.pdf) and I couldn't find the dataset.

Could someone provide me with the links related to this?

**Tags:** galaxy-cluster, neural-networks

> **Comments:**
> **username_1**: Have you had a chance to look at my answer?  
> **username_2**: @WilliamMiller I did! :-)  

---

## 1 answers

### Answer by username_1 on 2020-08-07. Score: 2 | Accepted answer

The authors used the [SDSS (Sloan Digital Sky Survey) III dataset](http://www.sdss3.org/) (data release 9):

> We use a state-of-the-art probabilistic algorithm, adapted to localise and classify galaxy clusters from other astronomical objects in SDSS imaging.

This data can be accessed through [Nasa SkyView](https://skyview.gsfc.nasa.gov/current/cgi/titlepage.pl). The training and test sets were constructed from subsets of the [Abell catalogue](https://ui.adsabs.harvard.edu/abs/1958ApJS....3..211A/abstract):

> We use the Abell galaxy clusters identified in the [Wen et. al (2012)](https://iopscience.iop.org/article/10.1088/0067-0049/199/2/34) catalogue,
> to obtain the labelled data needed to create the training set.

Most of the clusters in the Abell catalogue have been verified, but not all. Hence the use only of those which are included in Wen et. al (2012). The clusters were filtered based on the following criteria (section 2.2):

* Photometric redshift ($z$) range limited to $0.1 < z < 0.2$.
* Minimum of 20 galaxy members within $R\_{200}$ radius.

$497$ Abell clusters satisfy these criteria. The authors applied translational shifts to augment the data:

> ... one of the properties of the FasterRCNN algorithm is translational invariance, which means the algorithm is robust at learning translated objects. We train the algorithm to recognise that an object could appear at any location in an image. Since our method applies a random offset to the input coordinates via translation we augment the sample set three additional times, which boosts the sample size to $1988$.

This set of $1988$ inputs was randomly sampled to produce the training and test sets:

> The training set is made up of $∼90$ percent of the sample set consisting of $1784$ labelled
> galaxy clusters and the test set is made up of the remaining $∼10$ per cent consisting of $204$ labelled galaxy clusters.

The authors do not provide the translated data. However, since the process is statistically robust, applying three different set of random translations to the $497$ images and running the model should produce results which are consistent with those reported. This is, in fact, a fundamental assumption of the technique.

> **Comments:**
> **username_0**: Thanks a lot :)  

---

