Researchers evaluate machine learning methods for separating pulsar signals from noise
Machine learning may help us better understand our galaxy
Credit: skeeze, www.pixabay.com, CC0
Machine learning (ML) is a form of artificial intelligence (AI) that uses algorithms to teach computer systems. It allows these systems to learn from experience without being programmed to do so. We already use ML widely in daily life, in online customer support, social media and other applications, such as virtual personal assistants like Alexa and Siri. Can ML now help us to understand our galaxy better?
A team of researchers from the Indian Institute of Technology (IIT), Hyderabad, India, believe so. In a paper published in the journal Astronomy and Computing, they evaluated four machine learning algorithms that can separate pulsar signals from noise such as radio-frequency interference (RFI).
A pulsar is a rotating neutron star which emits pulsed electromagnetic radiation in radio waves. The IIT team believe that studying pulsars can provide valuable insight into questions of physics and astrophysics. “Pulsars are celestial laboratories where extreme phenomena can be studied and understood, and could also help us discover new physics,” explains lead author Suryarao Bethapudi.
Observing pulsars led to the first confirmed discovery of extrasolar planets and also provided indirect evidence for gravitational waves. We now estimate that there are tens of thousands of pulsars in our galaxy, though only around 2000 have so far been discovered.
“What if all the pulsars discovered until now are just small-scale celestial labs? What if we discover a pulsar which shows all the pulsars studied until now to be just 'regular' pulsars. We could grasp insights that would revolutionise our understanding,” says Bethapudi.
Enter machine learning
When experts search for pulsars, there is a processing pipeline they must undertake to verify whether ‘candidates’ really are – or are not – pulsars. This work, which includes separating pulsar signals from artificial noise, is currently carried out by humans eye-scanning the data. This is time-consuming and creates significant bottlenecks in the pipeline. Enter machine learning. “Using ML, we can search unhindered by the physical and computational limitations of the human mind,” explains Bethapudi.
The IIT team evaluated four ML algorithms: Artificial Neural Network Multi-Layer Perceptron (ANN MLP), Adaboost, Gradient Boosting Classifier (GBC), and XGBoost. We currently use these algorithms in optical astronomy and we could apply them in processing pipelines to separate pulsar signals from noise.
Bethapudi described the results of the team’s evaluations as “exceptionally good”. “We have demonstrated that the fraction of false positives seen with these ML algorithms is about an order of magnitude lower than normal pulsar search pipelines give for the same detection efficiency,” he adds.
Finding out what's out there
Bethapudi also believes that this technology will have useful applications for separating pulsars from noise during pulsar surveys in the Square Kilometer Array (SKA) era. He wants to use science and technology to accurately probe what is happening in our galaxy; to reveal new information about how things work, far from Earth.
“It is like an adventure where you don’t know what treasure – pulsars and other exotic species of this Universe – you’re going to find, and all you have is a belief that there is something out there waiting to be discovered.”
Bethapudi, S. et al.: "Separation of pulsar signals from noise using supervised machine learning algorithms," Astronomy and Computing (2018)