The Deeper, Wider, Faster Program: Exploring stellar flare activity with deep, fast cadenced DECam imaging via machine learning
Sara Webb, Chris Flynn, Jeff Cooke, Jielai Zhang, Ashish Mahabal, Tim, Abbott, Rebecca Allen, Igor Andreoni, Sarah Bird, Simon Goode, Michelle, Lochner, Tyler Pritchard

TL;DR
This study uses deep, fast-cadence DECam imaging combined with machine learning to detect and analyze stellar flares across various stars, revealing correlations with stellar age, spectral type, and galactic position.
Contribution
It introduces a novel unsupervised machine learning technique for anomaly detection in stellar flare light curves and provides new insights into flare energies and rates across different stellar populations.
Findings
Detected nearly 20,000 sources with Gaia distances.
Identified 96 stellar flare events, mostly on M dwarfs.
Found a volumetric flare rate of approximately 2.9 x 10^-6 flares pc^-3 hr^-1.
Abstract
We present our 500 pc distance-limited study of stellar fares using the Dark Energy Camera as part of the Deeper, Wider, Faster Program. The data was collected via continuous 20-second cadence g band imaging and we identify 19,914 sources with precise distances from Gaia DR2 within twelve, ~3 square-degree, fields over a range of Galactic latitudes. An average of ~74 minutes is spent on each field per visit. All light curves were accessed through a novel unsupervised machine learning technique designed for anomaly detection. We identify 96 flare events occurring across 80 stars, the majority of which are M dwarfs. Integrated are energies range from erg, with a proportional relationship existing between increased are energy with increased distance from the Galactic plane, representative of stellar age leading to declining yet more energetic are events. In agreement…
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