Collaborative Research: RUI: New Insights from a Systematic Approach to Quasar Variability

Middlebury College, Middlebury VT

Investigators

Abstract

A longstanding problem in astrophysics is to understand how galaxies form and develop throughout their lifetimes. Such understanding is necessary to uncover how our Universe evolved and to gain insight into the origin of our own Milky Way Galaxy. One important aspect of understanding galaxy formation and evolution is to study quasars and other active galactic nuclei. They are also interesting astrophysical phenomena in their own right and serve as a probe of relativistic physics. They co-evolve with their host galaxies and trace the evolution of cosmic structure. However, most of the methods for quasar discovery are based on the properties of their broadband spectral energy distributions, and nearly all known quasars and quasar candidates come from samples that use some type of flux ratios or just the presence of a non-thermal emission. Variability offers a spectrum-independent method for quasar discovery. Although variability has been much studied on the basis of individual or a few objects, variability-based quasar surveys have so far been limited to small dedicated regions of sky with at most a few thousand objects and/or poor time resolution. This study of quasar variability employs the Catalina Real-time Transient Survey (CRTS) data set, which covers about 80\% of the sky over a baseline greater than 9 years. Its 500 million object data set currently holds about 250,000 known quasars, 500,000 photometric quasar candidates and an estimated 1,000,000 new variability-selected quasars. This will form the largest quasar data set to date. In keeping with the CRTS Open Data policy, all quasars (and other classified objects) identified in this project will be released to the community. This will form a major new resource for both quasar and more general variability studies. The statistical methods to be used are also applicable to any irregular-sampled time series, and the combination of these with machine-learning techniques is a case study for data-intensive science. This project is a collaboration with a primarily undergraduate institution and directly enhances the STEM education of two undergraduates. Working with scientists at the Center for Data-Driven Discovery (CD3) at Caltech, they will be exposed to cutting-edge techniques in data science, including high level usage of data mining and extracting meaningful results from these large data sets. Data products from this project also form the basis for student projects at the joint US-Chile-funded La Serena School for Data Science, training the next generation in applied tools for handling big astronomical data. In particular, this project will focus on (i) the correlation of quasar variability features, particularly characteristic timescales, with physical parameters, such as luminosity, black hole mass, and the Eddington ratio; (ii) periodic variability as possible evidence for supermassive black hole binaries; (iii) variability as a probe of obscuration in young dust-enshrouded red quasars; and (iv) quantifying wavelength dependencies of variability to improve quasar selection and constrain different models of physical processes. The study will employ modern statistical techniques that can work naturally with irregularly-sampled gappy time series without the need for reprojection or smoothing. In combination with machine-learning methods, these will produce optimal ensemble-based results, such as new variability-polychromatic methods for quasar selection. This project will be a key study on optical quasar variability well into the LSST era. It is at least two orders of magnitude larger than any previous study in terms of sky coverage and number of quasars and an order of magnitude better in terms of time resolution (number of observations / baseline). It will also substantially increase the number of high likelihood quasar candidates known, particularly in the regions of the sky not covered by SDSS.