Classifying Astronomical Transients Using Only Host Galaxy Photometry

Abstract

The Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory will discover tens of thousands of extragalactic transients each night. The high volume of alerts demands immediate classification of transient types in order to prioritize observational follow-ups before events fade away. We use host galaxy features to classify transients, thereby providing classification upon discovery. In contrast to past work that focused on distinguishing Type Ia and core-collapse supernovae (SNe) using host galaxy features that are not always accessible (e.g., morphology), we determine the relative likelihood across 12 transient classes based on only 19 host apparent magnitudes and colors from 10 optical and IR photometric bands. We develop both binary and multiclass classifiers, using kernel density estimation to estimate the underlying distribution of host galaxy properties for each transient class. Even in this pilot study, and ignoring relative differences in transient class frequencies, we distinguish eight transient classes at purities significantly above the 8.3% baseline (based on a classifier that assigns labels uniformly and at random): tidal disruption events (TDEs; 48% ± 27%, where ± indicates the 95% confidence limit), SNe Ia-91bg (32% ± 18%), SNe Ia-91T (23% ± 11%), SNe Ib (23% ± 13%), SNe II (17% ± 2%), SNe IIn (17% ± 6%), SNe II P (16% ± 4%), and SNe Ia (10% ± 1%). We demonstrate that our model is applicable to LSST and estimate that our approach can accurately classify 59% of LSST alerts expected each year for SNe Ia, Ia-91bg, II, Ibc, SLSN-I, and TDEs. Our code and data set are publicly available.

TLW: A Real-Time Light Curve Classification Algorithm for Transients Based on Machine Learning

“…Pimentel et al proposed a deep attention model (TimeModAttn) to classify supernovae [37]. Kisley et al proposed a nuclear density estimation method which only relies on the host photometric information [38].…”

“…To sum up, the popular real-time classifiers mainly include adding host feature information [28,35,36,38] or using machine learning techniques such as recurrent neural network (RNN) [2,20,25,27,30,36], CNN [29,33,34], Transformer [31,32], RF [21,23,24,28,35], active learning [22], etc. The above methods have different advantages: RNN is suitable for a time series data modeling task; CNN has better feature extraction capability than a cyclic neural network; Transformer is usually based on multi-head attention, which can better capture the global information; and RF has high accuracy and strong robustness.…”

First Impressions: Early-time Classification of Supernovae Using Host-galaxy Information and Shallow Learning

“…FLEET, a random forest classifier used to recover SLSNe-I from survey streams, combines a set of fullphase parametric light curve features with host-galaxy properties and reports 20 SLSN-I discovered per year with an overall detection purity of 85% (Gomez et al 2020). More recent work has extended these insights to additional subclasses of SNe (Kisley et al 2023). None of these methods leverage both hostgalaxy and light curve information for real-time, adaptive photometric classification.…”