Using machine learning for transient classification in searches for gravitational-wave counterparts

C. Stachie, M. W. Coughlin, Nelson L Christensen, Daniel Muthukrishna

Monthly Notices of the Royal Astronomical Society 2020 article cited by 18

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importance 4/10 ai assisted Gamma-ray bursts and supernovae astrophysics stage: research

Problem

Large gravitational-wave sky localization regions produce many transient candidates that must be prioritized given restricted telescope time.

Why it matters

Efficient follow-up determines whether costly telescope time finds true kilonova counterparts.

What the AI did

The astrorapid multiband photometric light-curve classifier was used to differentiate kilonovae, supernovae, and other transients, tested on real events and simulated light curves.

Results

After only a few days of observations, candidates can be ruled out as supernovae and other known transients.

Impact

Demonstrated on real and simulated events that candidates can be rapidly excluded, enabling prioritized follow-up lists.

Record

Venue	Monthly Notices of the Royal Astronomical Society
Published	2020
Work type	article
Authors	C. Stachie, M. W. Coughlin, Nelson L Christensen, Daniel Muthukrishna
Citations	18 (OpenAlex, at import time)
References	84
Model or method	astrorapid (multiband photometric light-curve classifier)
Organization	not stated in source
Primary source	https://doi.org/10.1093/mnras/staa1776
Open access	green
Keywords	machine learning, transient classification, kilonovae, gravitational waves, light curves

Importance rating

4 / 10

real but narrower advance

Prioritizing transient candidates given restricted telescope time matters for gravitational-wave follow-up, but the abstract gives no scale of affected research or resources.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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