BEGIN:VCALENDAR VERSION:2.0 X-WR-CALNAME:EventsCalendar PRODID:-//hacksw/handcal//NONSGML v1.0//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York LAST-MODIFIED:20240422T053451Z TZURL:https://www.tzurl.org/zoneinfo-outlook/America/New_York X-LIC-LOCATION:America/New_York BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:19700308T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:19701101T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT CATEGORIES:College of Engineering,Lectures and Seminars DESCRIPTION:Abstract: In the emerging field of gravitational wave astronomy , the data collected by gravitational-wave (GW) observatories is key to un derstanding the universe. However, in addition to astrophysical signals, t he data consists of non-stationary detector noise and transient bursts of noise known as glitches. These glitches impact the ability to both observe and characterize incoming gravitational-wave signals. Thus, it is imperat ive that we study these glitch populations to improve our sensitivity to r eal signals and provide feedback to instrumentalists. Current glitch mitig ation pipelines use glitch spectrogram images, which have been used to tra in many state-of-the-art glitch analysis tools. While this approach has pr oven to be effective, many aspects of the glitch, such as phase informatio n, short-glitch events, and time localization, are lost. Due to these limi tations of frequency-domain analysis, there is a need for glitch analysis tools that operate in the time domain. In this work, we present the first large- scale glitch time-domain model reconstruction analysis on glitch da ta from LIGO’s third observation run. We introduce a machine-learning ba sed tool to assess the quality of glitch time-domain reconstructions by ut ilizing non-Gaussianity tests to analyze glitch residuals and enabling the optimization of time-series models for various LIGO glitch classes. Using this framework, we demonstrate how large-scale time-domain datasets of re al, noise-free detector glitches can be rapidly produced and assessed, pav ing the way for improved glitch population studies and future developments in classification and simulation tools. Advisor: Dr. Sarah Caudill, Depar tment of Physics (scaudill@umassd.edu) Note: All PHY Graduate Students are encouraged to attend. \nEvent page: https://www.umassd.edu/events/cms/p hysics-master-of-science-project-presentation-by-bhaskar-verma.php X-ALT-DESC;FMTTYPE=text/html:

Abstract:

In the emergi ng field of gravitational wave astronomy\, the data collected by gravitati onal-wave (GW) observatories is key to understanding the universe. However \, in addition to astrophysical signals\, the data consists of non-station ary detector noise and transient bursts of noise known as glitches. These glitches impact the ability to both observe and characterize incoming grav itational-wave signals. Thus\, it is imperative that we study these glitch populations to improve our sensitivity to real signals and provide feedba ck to instrumentalists. Current glitch mitigation pipelines use glitch spe ctrogram images\, which have been used to train many state-of-the-art glit ch analysis tools. While this approach has proven to be effective\, many a spects of the glitch\, such as phase information\, short-glitch events\, a nd time localization\, are lost. Due to these limitations of frequency-dom ain analysis\, there is a need for glitch analysis tools that operate in t he time domain. In this work\, we present the first large- scale glitch ti me-domain model reconstruction analysis on glitch data from LIGO’s third observation run. We introduce a machine-learning based tool to assess the quality of glitch time-domain reconstructions by utilizing non-Gaussianit y tests to analyze glitch residuals and enabling the optimization of time- series models for various LIGO glitch classes. Using this framework\, we d emonstrate how large-scale time-domain datasets of real\, noise-free detec tor glitches can be rapidly produced and assessed\, paving the way for imp roved glitch population studies and future developments in classification and simulation tools.

Advisor:
Dr. Sarah Caudill\, Departmen t of Physics (scaudill@umassd.edu)

Note:
All PHY Graduate St udents are encouraged to attend.

Event p age: https://www.umassd.edu/events/c ms/physics-master-of-science-project-presentation-by-bhaskar-verma.php

DTSTAMP:20260418T054817 DTSTART;TZID=America/New_York:20260507T093000 DTEND;TZID=America/New_York:20260507T104500 LOCATION:SENG 201 SUMMARY;LANGUAGE=en-us:Physics Master of Science Project Presentation by Bh askar Verma UID:cce48be51047d8c152268e3726857022@www.umassd.edu END:VEVENT END:VCALENDAR