From Stochastic Variability to Binary and Transient Signatures: Probing Massive Black Holes with Time-Domain Surveys

Tuesday 3 Feb 2026, 21:40 → 22:00 Asia/Shanghai

Tsung-Dao Lee Institute

Speaker: Weixiang Yu（Bishop’s University）
Time:  21:40-22:00 (UTC+8), 3 February 2026, Tuesday
Host: Dong Lai
Location: Online
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Abstract:

Active galactic nuclei exhibit complex stochastic variability that both encodes black hole accretion physics and obscures rarer signals from massive black hole binaries and nuclear transients. In this talk, I present a time-domain framework that treats variability as both a physical probe and a statistical baseline for discovering and interpreting these phenomena in the era of large time-domain surveys.

I begin with results from modeling decades-long quasar light curves as stochastic processes, demonstrating that variability on both long and short timescales correlates with fundamental accretion properties such as Eddington ratio. To scale these analyses to Rubin–LSST, I developed EzTaoX, a multi-band Gaussian-process framework that jointly models stochastic variability and inter-band lags with orders-of-magnitude speedup over existing methods.

Building on this foundation, I outline two discovery paths enabled by this approach: (1) robust identification of massive black hole binary candidates through separation of periodic signatures from intrinsic red-noise variability, and (2) systematic detection and characterization of nuclear transients embedded in AGN light curves, where stochastic variability dominates the background. I also briefly discuss implications for multi-messenger astronomy, including electromagnetic counterparts to LISA-detected mergers.

Together, these efforts establish time-domain variability as a unifying tool for connecting accretion physics, binary evolution, and nuclear transients in massive black hole systems.

Biography:

Dr. Weixiang Yu is a Canadian–Rubin Postdoctoral Fellow at Bishop’s University. He earned his Ph.D. in Physics from Drexel University in 2023 and a B.S. in Physics from the University of Illinois at Urbana–Champaign. His research focuses on time-domain and multi-messenger astronomy, with an emphasis on accretion disk physics, black holes, and AGN variability. He develops statistical and deep-learning methods to extract physical insights from large astronomical datasets, particularly in the LSST/Rubin era.