eROSITA’s Window into the X-ray-Transient Sky: AGN Accretion in the Era of Big Data

点击翻译按钮获取中文摘要

Open questions persist regarding the fueling mechanisms in Active Galactic Nuclei (AGN): What is the average AGN duty cycle? What is the structure and morphology of the innermost accretion flow? How does that flow depend on global accretion rate? Recent optical and X-ray observations have yielded tantalizing clues: rare accretion turn-on/turn off events and variations in line-of-sight columndensities due to transiting discrete clumps. However, progress has been limited by the low number of observed events.We will use the eROSITA X-ray telescope to compile a new, expanded X-ray monitoring variability statistics database for more than 1e6AGN. We will use machine learning to identify new changing-state and changing-obscuration events in AGN as they occur, boosting the currently small number of known events. The accumulated statistics on accretion ignition/quenching events will reveal AGN duty cycles, and our expected multi-wavelength campaigns will monitor how the accretion disk, BLR, and X-ray corona evolve during AGN ignition. We will also monitor about 800 Seyferts for cloud eclipse events, determine clouds' physical properties, and use the accumulated statistics on such events to determine the distribution and physical origin of clouds. We will also measure variability amplitudes in about 60000 AGN as function of mass and redshift. Finally, we will monitor rapid X-ray variability in about 1600 blazars and correlate with contemporaneous Fermi-LAT data to test single- and multi-zone emission models at all normal states, not just during outbursts.eROSITA will yield the only database (for the foreseeable future) combining X-ray monitoring with a statistically significant sample oftarget AGN, and thus the only bulk X-ray identification of new transient events. Our work will yield the first X-ray observation-based estimate for AGN duty cycles, test unification schemes, and constrain how quickly the accretion flow can evolve in AGN ignition events. We will also constrain the distribution of clumpy matter and the rate of observed obscuration transitions, helping us understand X-ray/optical type mismatches. We will also learn if the X-ray variability mechanism in Seyferts evolves with redshift. Our blazar variability subset will help distinguish various current blazar emission models. Our work also sets the stage for additional Big Dataresearch in the mid 2020s that also use machine learning to classify transients.Our collaboration combines complementary expertise spanning timing and spectroscopy of both AGN and Black Hole Binaries and large samples of high-redshift AGN across multiple bandpasses. To cement the collaboration, new PhD students in this project will be bilateral, with joint supervisors from both countries.