Abstract: Standard stellar-mass binary black holes inspiral and merge in just a few seconds when observed in ground-based gravitational-wave detector data. However, future detectors will probe much lower frequencies, implying that sources of gravitational waves will spend even longer time in-band than those observed today. Such long-lived signals could result from the early inspiral of binary neutron stars, sub-solar mass primordial black holes, and (mini) extreme mass ratio inspirals, all of which could be visible in both future ground-and space-based detectors, such as Cosmic Explorer and LISA. However, data quality problems, such as gaps, glitches and non-stationary noise, and computational cost, will inhibit the observations of these systems if robust methods are not designed to handle these issues . In this talk, I will describe each of these sources individually, the problems with computation and data quality that we are likely to face in future detectors, and methods to actually perform searches for these systems that are robust against not only noise disturbances, but also deviations in the waveforms used to in the search.
Mini-bio: Andrew Miller received his PhD from the University of Florida, in US, and the Sapienza University of Rome, in Italy, for a thesis on using machine learning to detect transient gravitational waves from remnants of neutron star mergers. He did his first postdoc at the Université catholique de Louvain, in Belgium, and is now a postdoc at the National Institute of Subatomic Physics (Nikhef) and Utrecht University, in the Netherlands, where he works on new probes of dark matter, primordial black holes and neutron stars, as well as on machine learning techniques to mitigate noise disturbances in the LIGO/Virgo data.
Google Meet: https://meet.google.com/pcw-gmem-jyi
Link da transmissão: https://www.youtube.com/c/AstronomiaIAGUSP/live