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A Convolutional Autoencoder-Based Pipeline For Anomaly Detection And Classification Of Periodic Variables
Ho-Sang Chan · Siu Hei Cheung · Shirley Ho
The periodic pulsations of stars teach us about their underlying physical process. We present a convolutional autoencoder-based pipeline as an automatic approach to search for out-of-distribution anomalous periodic variables within the Zwicky Transient Facility (ZTF) catalog of periodic variables. We use an isolation forest to rank each periodic variable by the anomaly score. Our overall most anomalous events have a unique physical origin: they are mostly, red, cool, high variability, and irregularly oscillating periodic variables. Observational data suggest that they are most likely young and massive ($\simeq5-10$M$_\odot$) Red Giant or Asymptotic Giant Branch stars. Furthermore, we use the learned latent feature for the classification of periodic variables through a hierarchical random forest. This novel semi-supervised approach allows astronomers to identify the most anomalous events within a given physical class, significantly increasing the potential for scientific discovery.

#### Author Information

##### Shirley Ho (Flatiron institute/ New York University/ Carnegie Mellon)

Shirley Ho is a group leader and acting director at Flatiron Institute at Simons foundation, a research professor of physics and an affiliated faculty at Center for Data Science at NYU. Ho also holds associate (adjunct) professorship at Carnegie Mellon University and visiting appointment at Princeton University. She was a senior scientist at Berkeley National Lab from 2016-2018 and a Cooper-Siegel Development chair professor at Carnegie Mellon University before that. Ho was a Seaborg and Chamberlain Fellow from 2008-2011 at Berkeley Lab, after receiving her PhD in Astrophysics from Princeton University in 2008 under supervision of David Spergel. Ho graduated summa cum laude with a B.A. in Physics and a B.A. in Computer Science from UC Berkeley. A cited expert in cosmology, machine learning applications in astrophysics and data science,her interests are using deep learning accelerated simulations to understand the Universe, and other astrophysical phenomena. She tries her best to balance her love for the Universe, the machine and life especially during these crazy times.