Project:	Hunting for the most extreme nuclear transients
Disciplines:	Astrophysics, Data Science
Mentor:	Matthew Graham, Research Professor, (PMA), mjg@caltech.edu, Phone: 8030
Mentor URL:	https://www.astro.caltech.edu/~mjg  (opens in new window)
AO Contact:	Matthew Graham, mjg@caltech.edu
Background:	Since their discovery more than 60 years ago, accreting supermassive black holes (SMBH) in active galactic nuclei (AGN) have been recognized as highly variable sources, requiring an extremely compact, dynamic environment. Their variability is related to several phenomena, including changing accretion rates, temperature changes, foreground absorbers and structural changes to the accretion disk. Spurred by a new generation of time domain surveys, the extremes of black hole variability are now being probed. The most energetic variable behavior is attributed to the tidal disruption of massive (M > 3 M_sol) stars in a new rare class of astronomical transient: the extreme nuclear transient (ENT). We recently reported the detection of the most luminous example so far known - the disruption of a >30 M_sol star by a SMBH 11 billion years ago. We are keen to find further examples to probe the stellar population in the vicinity of SMBHs and related physics. The durations of individual time domain surveys are not optimal for finding ENTs since they can have intrinsic lifetimes of thousands of days and cosmological time dilation makes this many years for observers. Instead we need to stitch surveys together to provide decadal baselines for optimal searching.
Description:	The student will conduct a search for ENTs employing a combination of digital sky surveys covering the past 20 years plus further archival measurements from Palomar photographic surveys extending to 75 years ago. This will support a general characterization of AGN variability on decadal timescales as well as identifying the most extreme variable sources - ENTs. This will involve processing millions of multivariate time series using AI/machine learning techniques.
References:	Graham et al., 2025, https://www.nature.com/articles/s41550-025-02699-0 Wiseman et al., 2025, https://academic.oup.com/mnras/article/537/2/2024/7965975 Hinkle et al., 2025, https://www.science.org/doi/10.1126/sciadv.adt0074
Student Requirements:	Experience with Python programming in a research environment is required. Previous ML experience is preferred.
Programs:	This AO can be done under the following programs:   Program    Available To        SURF    both Caltech and non-Caltech students  Click on a program name for program info and application requirements.