Matthew J. Graham

I'm a Research Professor of Astronomy at the California Institute of Technology and the Project Scientist for the Zwicky Transient Facility (ZTF), the first of a next generation of time-domain sky surveys producing hundreds of thousands of public transient alerts per night. I have previously worked on the Catalina Real-time Transient Survey (CRTS), a still unmatched data set in terms of temporal baseline coverage; the NOAO DataLab; the Virtual Observatory; and the Palomar-Quest Digital Sky Survey.

My main research interests are the application of machine learning and advanced statistical methodologies to astrophysical problems, particularly the variability of quasars and other stochastic time series. This is in part to deal with the unprecedented volumes of data that 21st century astronomy is generating but also to expand our ability to work with complex systems of information beyond simple correlations.

I am also part of the NSF-funded HDR Institute for Accelerated AI Algorithms for Data-Driven Discovery (A3D3) which aims to provide real-time AI at scale in high energy physics, multimessenger astronomy, and neuroscience. My particular interest is in low-latency inferencing from astronomical alert streams with commodity hardware accelerators and using reinforcement learning to optimize followup strategies.

RECENT PAPERS

Latent stochastic differential equations for modeling quasar variability and inferring black hole properties

Fagin, J., Park, J.W., et al., arXiv:2304.04277, accepted at the ICLR 2023 Workshop on Physics for Machine Learning

Active galactic nuclei (AGN) are believed to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The variability of an AGN's brightness over time can reveal important information about the physical properties of the underlying black hole. The temporal variability is believed to follow a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). With upcoming wide-field surveys set to observe 100 million AGN in multiple bandpass filters, there is a need for efficient and automated modeling techniques that can handle the large volume of data...

A light in the dark: searching for electromagnetic counterparts to black hole-black hole mergers in LIGO/Virgo O3 with the Zwicky Transient Facility

Graham, M.J. et al., ApJ, 2023, 942, 9

The accretion disks of active galactic nuclei (AGN) are promising locations for the merger of compact objects detected by gravitational wave (GW) observatories. Embedded within a baryon-rich, high density environment, mergers within AGN are the only GW channel where an electromagnetic (EM) counterpart must occur (whether detectable or not). Considering AGN with unusual flaring activity observed by the Zwicky Transient Facility (ZTF), we describe a search for candidate EM counterparts to binary black hole (BBH) mergers detected by LIGO/Virgo in O3. After removing probable false positives, we find nine candidate counterparts to black hole mergers mergers during O3 (seven in O3a, two in O3b) with...

The Type 1 and Type 2 AGN dichotomy according to their ZTF optical variability

Lopez-Navas, E., et al., 2023, MNRAS, 518, 1531

The scarce optical variability studies in spectrally classified Type 2 active galactic nuclei (AGNs) have led to the discovery of anomalous objects that are incompatible with the simplest unified models (UM). This paper focuses on the exploration of different variability features that allows to separate between obscured, Type 2 AGNs, and the variable, unobscured Type 1s. We analyse systematically the Zwicky Transient Facility, 2.5 years long light curves of ~ 15000 AGNs from the Sloan Digital Sky Survey Data Release 16, which are generally considered Type 2s due to the absence of strong broad emission lines (BELs). Consistently with the expectations from the UM, the variability features are distributed differently for distinct populations, with spectrally classified weak Type 1s showing 1 order of magnitude...

RECENT PREPRINTS

Autonomous real-time science-driven follow-up of survey transients

Sravan, N., et al., 2021, conf. proc.

Astronomical surveys continue to provide unprecedented insights into the time-variable Universe and will remain the source of groundbreaking discoveries for years to come. However, their data throughput has overwhelmed the ability to manually synthesize alerts for devising and coordinating necessary follow-up with limited resources. The advent of Rubin Observatory, with alert volumes an order of magnitude higher at otherwise sparse cadence, presents an urgent need to overhaul existing human-centered protocols in favor of machine-directed infrastructure for conducting science inference and optimally planning expensive follow-up observations. We present the first implementation of autonomous real-time science-driven follow-up using value iteration to perform sequential experiment design...

Starfall: a heavy rain of stars in "turning on" AGN

McKernan, B., et al., 2021, submitted

As active galactic nuclei (AGN) `turn on', some stars end up embedded in accretion disks around supermassive black holes (SMBHs) on retrograde orbits. Such stars experience strong headwinds, aerodynamic drag, ablation and orbital evolution on short timescales. Loss of orbital angular momentum in the first $\sim 0.1$~Myr of an AGN leads to a heavy rain of stars (`starfall') into the inner disk and onto the SMBH. A large AGN loss cone ($\theta_{\rm AGN,lc}$) can result from binary scatterings in the inner disk and yield tidal disruption events (TDEs). Signatures of starfall include optical/UV flares that rise in luminosity over time, particularly in the inner disk...