Searching for Supernovae in HETDEX Data Release 3

TL;DR

This study utilizes HETDEX spectra and machine learning to classify transient sources from ZTF, successfully identifying supernovae and other variable objects, demonstrating the value of spectral data in transient classification.

Contribution

The paper introduces a method combining machine learning and template matching to classify transients in HETDEX spectra, identifying supernovae and other variables from ZTF data.

Findings

Identified two supernova candidates with spectral analysis.

Classified AT2020fiz as a Type IIP supernova.

Proposed ZTF19abdkelq as a likely Type Ia supernova.

Abstract

We have extracted 636 spectra taken at the positions of 583 transient sources from the third Data Release of the Hobby-Eberly Telescope Dark Energy eXperiment (HETDEX). The transients were discovered by the Zwicky Transient Facility (ZTF) during 2018 - 2022. The HETDEX spectra are useful to classify a large number of objects found by photometric surveys for free. We attempt to explore and classify the spectra by utilizing machine learning (ML) and template matching techniques. We have identified two transient sources, ZTF20aatpoos = AT2020fiz and ZTF19abdkelq as supernova candidates. We classify AT2020fiz as a Type IIP supernova observed ~10 days after explosion, and we propose ZTF19abdkelq as a likely Type Ia SN caught ~40 days after maximum light. ZTF photometry of these two sources are consistent with their classification as supernovae. Beside these two objects, we have confirmed several ZTF transients as variable AGNs based on their spectral appearance, and also determined the host galaxy types for several other ZTF transients.