UNSUPERVISED CLUSTERING OF TYPE II SUPERNOVA LIGHT CURVES

Adam Rubin, Avishay Gal‐Yam

The Astrophysical Journal 2016 article cited by 42

Source Open-access PDF More in Physics & Astronomy

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

Problem

Astronomers needed a systematic, unbiased way to classify Type II supernova light curves ahead of upcoming large transient surveys.

Why it matters

Systematic classification of supernova light curves underpins understanding of stellar explosions and handling of future extremely large data sets.

What the AI did

Unsupervised k-means clustering was applied to 59 R-band SN II light curves, showing the rise-to-peak phase matters and dividing the sample into three classes plus three outliers.

Results

Sample divided into three classes (II-S, II-FS, II-FF) with three outliers; II-FS significantly less luminous; II-S bridges the gap between II-P/II-L populations.

Impact

The clustering result offered an explanation for recent conflicting findings on II-P/II-L populations.

Record

Venue	The Astrophysical Journal
Published	2016
Work type	article
Authors	Adam Rubin, Avishay Gal‐Yam
Citations	42 (OpenAlex, at import time)
References	21
Model or method	k-means clustering (with principal component analysis)
Organization	not stated in source
Primary source	https://doi.org/10.3847/0004-637x/828/2/111
Open access	bronze
Keywords	supernovae, light curves, k-means, unsupervised clustering, transients

Importance rating

4 / 10

real but narrower advance

Systematic SN II light-curve classification ahead of extremely large transient surveys; foundational astronomy but narrow specific problem.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: 59 R-band SN II light curves. Method and limits.

More in Physics & Astronomy

#	Paper	Year	Citations	Importance
1	Magnetic control of tokamak plasmas through deep reinforcement learning Jonas Degrave, Federico Felici, Jonas Buchli, Michael Neunert, Brendan...	2022	739	7
2	Solving the quantum many-body problem with artificial neural networks Giuseppe Carleo, Matthias Troyer	2017	2,205	7
3	Learning high-accuracy error decoding for quantum processors Johannes Bausch, Andrew Senior, Francisco J. H. Heras, Thomas Edlich,...	2024	90	7
4	Optimizing Quantum Error Correction Codes with Reinforcement Learning Hendrik Poulsen Nautrup, Nicolas Delfosse, Vedran Dunjko, Hans J. Brie...	2019	158	7
5	Machine-learning-assisted correction of correlated qubit errors in a topological code Paul Baireuther, Thomas E. O’Brien, Brian Tarasinski, C. W. J. Beenakk...	2018	141	7