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Searching for the Weirdest Stars: A Convolutional Autoencoder-Based Pipeline For Detecting Anomalous Periodic Variable Stars
Ho-Sang Chan · Siu Hei Cheung · Victoria Villar · Shirley Ho

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in Deep Generative Models and Downstream Applications »
Event URL: https://openreview.net/forum?id=brplec2JJx »

The physical processes of stars are encoded in their periodic pulsations. Millions of variable stars will be observed by the upcoming Vera Rubin Observatory's Legacy Survey of Space and Time. Here, we present a convolutional autoencoder-based pipeline as an automatic approach to search for anomalous periodic variables within The Zwicky Transient Facility Catalog of Periodic Variable Stars (ZTF CPVS). We encode their light curves using a convolutional autoencoder, and we use an isolation forest to sort each periodic variable star by an anomaly score with the latent space. Our overall most anomalous events share some similarities: they are mostly highly variable and irregular evolved stars. An exploration of multiwavelength data suggests that they are most likely Red Giant or Asymptotic Giant Branch stars concentrated in the disk of the Milky Way. 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, accelerating the potential for scientific discovery.

Author Information

Ho-Sang Chan (The Chinese University of Hong Kong)
Siu Hei Cheung (The Chinese University of Hong Kong)
Victoria Villar (Penn State)
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.

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