This talk will begin promptly at 1:30pm. No refreshments will be served.
Abstract: The majority of variable astronomical sources are aperiodic but represent a wide range of physical processes and scales. They can play a key role in our understanding of complex dynamic physical environments from stellar photospheres to accretion disks to merging galactic systems. However, they remain poorly studied in comparison to periodic sources, partly due to a lack of suitable statistical tools and methodologies. The best known aperiodic classes are quasars and young stellar objects (YSOs) but in both cases fundamental questions remain about the physical mechanisms behind their optical variability. A new generation of sky surveys is enabling systematic studies of astrophysical variability and discovering as many new phenomena as it seeks to explain: in quasars, we have discovered sub-parsec separated binaries, major multi-year long flares attributable to microlensing and explosive stellar-related activity in the accretion disk, and changing-state sources indicative of thermal fronts propagating through the accretion disk. In this talk, Professor Graham will discuss new approaches to characterize aperiodic variability using generative data-derived models and predict the future behavior of aperiodic sources. This allows them to be monitored in real-time with new synoptic facilities thus providing a more powerful way to detect unexpected behavior than differential photometry.
Dr. Matthew Graham is a Research Professor in Astrophysics at the California Institute of Technology and the Project Scientist for the Zwicky Transient Facility, which can be regarded as a stepping stone to LSST. He holds degrees in physics and astronomy from the University of Oxford and the University of Central Lancashire in the UK and has been at Caltech for 16 years as a research scientist in the Center for Advanced Computing Research and more recently the Center for Data-Driven Discovery. His research interests are the application of advanced statistical methodologies and computer science techniques to science problems, particular those involving astronomical time series.