Bulletin of the American Physical Society
2024 APS April Meeting
Wednesday–Saturday, April 3–6, 2024; Sacramento & Virtual
Session E00: Poster Session I & Welcome Reception (5:30PM - 7:30PM PT)
5:30 PM,
Wednesday, April 3, 2024
SAFE Credit Union Convention Center
Room: Exhibit Hall A, Floor 1
Sponsoring
Unit:
APS
Abstract: E00.00073 : PACMANN: using machine learning to detect pulsars
Presenter:
Teddy S Masters
(Kenyon College)
Author:
Teddy S Masters
(Kenyon College)
Collaborations:
Maddie Wade, NANOGRAV, Quinn Curren
Pulsars are observed mainly using radio telescopes. We “fold” the data across many pulses in order to get a clearer picture of the pulsar in question. After folding, humans vet each pulsar candidate by observing its properties from a series of diagnostic plots. We primarily look at four plots. Persistence: how the pulsar persists over an observation, Subband: how well the pulsar emits over different radio frequencies, Pulse Profile: how the pulse itself actually appears, and DM: dispersion measure, which is a proxy for distance from Earth. This is a somewhat manual process, making the discovery of new pulsars slower than we would like.
We have developed a program: PulsAr Classifier Machine learning Algorithm with Neural Networks or PACMANN. PACMANN is a hierarchical machine learning algorithm that is trained on the same data structures seen by human rankers. PACMANN has been successful at identifying pulsars in a pool of candidates, making it a useful tool in the automation of pulsar identification. As a next step, we will be searching archival data from the GBNCC Survey performed with the Green Bank Telescope for new pulsar candidates.
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