Ghosts of eruptions past: Searching for historical Galactic supernovae using variable thermal dust echoes and machine learning
Justin Vega, Kishalay De, Ashish Mahabal, Jacob E. Jencson, Viraj R. Karambelkar, Armin Rest, Megan Masterson

TL;DR
This study develops a machine learning approach to identify historical Galactic supernova dust echoes using 12 years of infrared data, aiming to uncover missed supernova events obscured by dust.
Contribution
It introduces an all-sky, untargeted search method combining difference imaging and CNN classification to detect thermal dust echoes, with the largest catalog of Cas A echoes.
Findings
Cas A is the only detected echo region at WISE sensitivity.
The CNN classifier achieves 94% accuracy in distinguishing echoes.
The catalog of Cas A echoes supports interstellar medium studies.
Abstract
The Galactic core-collapse supernova (SN) rate is estimated at per century; however, no optically visible SN has been discovered in the past 400 years. Although records of the last optically detected SN (Cassiopeia A) are debated, it is revealed today via its bright, variable mid-infrared (MIR) dust echoes -- offering the possibility of identifying dust-obscured, missed events via their dust echoes. We present the first all-sky, untargeted search for thermal dust echoes of luminous Galactic transients using difference imaging on 12 years of time-resolved NEOWISE co-adds (spanning ) followed by statistical detection of variable extended sources. We use echo features around Cas A, together with archival catalogs to train a convolutional neural network to classify transient candidates as dust echoes, point sources, artifacts, and high proper motion stars. Our model…
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