Towards deeper neural networks for Fast Radio Burst detection. (arXiv:1902.06343v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Agarwal_D/0/1/0/all/0/1">Devansh Agarwal</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Aggarwal_K/0/1/0/all/0/1">Kshitij Aggarwal</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Burke_Spolaor_S/0/1/0/all/0/1">Sarah Burke-Spolaor</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Lorimer_D/0/1/0/all/0/1">Duncan R. Lorimer</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Garver_Daniels_N/0/1/0/all/0/1">Nathaniel Garver-Daniels</a>
With the upcoming commensal surveys for Fast Radio Bursts (FRBs), and their
high candidate rate, usage of machine learning algorithms for candidate
classification is a necessity. Such algorithms will also play a pivotal role in
sending real-time triggers for prompt follow-ups with other instruments. In
this paper, we have used the technique of Transfer Learning to train the
state-of-the-art deep neural networks for classification of FRB and Radio
Frequency Interference (RFI) candidates. These are convolutional neural
networks which work on radio frequency-time and dispersion measure-time images
as the inputs. We trained these networks using simulated FRBs and real RFI
candidates from telescopes at the Green Bank Observatory. We present 11 deep
learning models, each with an accuracy and recall above 99.5% on our test
dataset comprising of real RFI and pulsar candidates. As we demonstrate, these
algorithms are telescope and frequency agnostic and are able to detect all FRBs
with signal-to-noise ratios above 10 in ASKAP and Parkes data. We also provide
an open-source python package FETCH (Fast Extragalactic Transient Candidate
Hunter) for classification of candidates, using our models. Using FETCH, these
models can be deployed along with any commensal search pipeline for real-time
candidate classification.
With the upcoming commensal surveys for Fast Radio Bursts (FRBs), and their
high candidate rate, usage of machine learning algorithms for candidate
classification is a necessity. Such algorithms will also play a pivotal role in
sending real-time triggers for prompt follow-ups with other instruments. In
this paper, we have used the technique of Transfer Learning to train the
state-of-the-art deep neural networks for classification of FRB and Radio
Frequency Interference (RFI) candidates. These are convolutional neural
networks which work on radio frequency-time and dispersion measure-time images
as the inputs. We trained these networks using simulated FRBs and real RFI
candidates from telescopes at the Green Bank Observatory. We present 11 deep
learning models, each with an accuracy and recall above 99.5% on our test
dataset comprising of real RFI and pulsar candidates. As we demonstrate, these
algorithms are telescope and frequency agnostic and are able to detect all FRBs
with signal-to-noise ratios above 10 in ASKAP and Parkes data. We also provide
an open-source python package FETCH (Fast Extragalactic Transient Candidate
Hunter) for classification of candidates, using our models. Using FETCH, these
models can be deployed along with any commensal search pipeline for real-time
candidate classification.
http://arxiv.org/icons/sfx.gif
