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The Real-Time Detection Infrastructu...

Sharma Chaudhary, Sushant,

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The Real-Time Detection Infrastructure of LIGO, Virgo, and KAGRA: Data Products, Current Performance, and Future Developments /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The Real-Time Detection Infrastructure of LIGO, Virgo, and KAGRA: Data Products, Current Performance, and Future Developments // Sushant Sharma Chaudhary.
作者:	Sharma Chaudhary, Sushant,
面頁冊數:	1 electronic resource (197 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
Contained By:	Dissertations Abstracts International86-12B.
標題:	Physics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31997157
ISBN:	9798286424344

The Real-Time Detection Infrastructure of LIGO, Virgo, and KAGRA: Data Products, Current Performance, and Future Developments /
Sharma Chaudhary, Sushant,

The Real-Time Detection Infrastructure of LIGO, Virgo, and KAGRA: Data Products, Current Performance, and Future Developments /Sushant Sharma Chaudhary. - 1 electronic resource (197 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

The discovery of the binary neutron star merger event GW170817 marked the dawn of Multi-Messenger Astronomy (MMA) with Gravitational Waves (GWs). Such multi-messenger events are of immense scientific interest due to the wealth of information they provide through joint observations across different messengers. In this rapidly evolving field, prompt identification and timely distribution of alerts is critical for follow-up observations.This work offers a comprehensive overview of the LIGO-Virgo-KAGRA (LVK) Collaboration's low-latency analysis pipeline for GW events, covering key stages from calibration and data analysis to the issuance of public alerts. I examine the latency and accuracy of each stage in the pipeline, emphasizing the performance of current data products. Furthermore, I address challenges in identifying compelling astrophysical merger candidates-such as electromagnetically bright mergers, mass-gap objects, and sub-solar mass objects-using machine learning techniques within the constraints of low-latency frameworks. Lastly, I present novel machine learning methods designed to provide reliable bounds on source parameters of merging binaries, highlighting their potential for real-time parameter estimation and their role in expanding data products to offer richer astrophysical insights to the astronomy community.

English

ISBN: 9798286424344Subjects--Topical Terms:

516296
Physics.
Subjects--Index Terms:

Multi-Messenger Astronomy

The Real-Time Detection Infrastructure of LIGO, Virgo, and KAGRA: Data Products, Current Performance, and Future Developments /
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