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Preprint
CCSNscore: A multi-input deep learning tool for classification of core-collapse supernovae using SED-Machine spectra
IACAPAP ArXiv (Online)
11 Dec 2024
Abstract
Supernovae (SNe) come in various flavors and are classified into different
types based on emission and absorption lines in their spectra. SN candidates
are now abundant with the advent of large systematic dynamic sky surveys like
the Zwicky Transient Facility (ZTF), however, the identification bottleneck
lies in their spectroscopic confirmation and classification. Fully robotic
telescopes with dedicated spectrographs optimized for SN follow-up have eased
the burden of data acquisition. However, the task of classifying the spectra
still largely rests with the astronomers. Automating this classification step
reduces human effort, and can make the SN type available sooner to the public.
For this purpose, we have developed a deep-learning based program for
classifying core-collapse supernovae (CCSNe) with ultra-low resolution spectra
obtained with the SED-Machine IFU spectrograph on the Palomar 60-inch
telescope. The program consists of hierarchical classification task layers,
with each layer composed of multiple binary classifiers which are run in
parallel to produce a reliable classification. The binary classifiers utilize
RNN and CNN architecture and are designed to take multiple inputs, to
supplement spectra with g- and r-band photometry from ZTF. On
non-host-contaminated and good quality SEDM spectra ("gold" test set),
CCSNscore is ~94% accurate (correct classifications) in distinguishing between
hydrogen-rich (Type II) and hydrogen-poor (Type Ibc) CCSNe. With the help of
light curve input, CCSNscore classifies ~83% of the gold set with high
confidence (score >= 0.8 and score-error < 0.05), with ~98% accuracy.
Metrics
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Details
- Title
- CCSNscore: A multi-input deep learning tool for classification of core-collapse supernovae using SED-Machine spectra
- Creators
- Yashvi SharmaAshish A MahabalJesper SollermanChristoffer FremlingS. R KulkarniNabeel RehemtullaAdam A MillerMarie AubertTracy X ChenMichael W CoughlinMatthew J GrahamDavid HaleMansi M KasliwalYoung-Lo KimJames D NeillJosiah N PurdumBen RusholmeAvinash SinghNiharika Sravan
- Publication Details
- IACAPAP ArXiv (Online)
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Physics
- Other Identifier
- 991022005788104721