Convolutional neural networks for signal detection in real LIGO data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985815%3A_____%2F24%3A00587826" target="_blank" >RIV/67985815:_____/24:00587826 - isvavai.cz</a>

Výsledek na webu

<a href="https://hdl.handle.net/11104/0354909" target="_blank" >https://hdl.handle.net/11104/0354909</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1103/PhysRevD.110.024024" target="_blank" >10.1103/PhysRevD.110.024024</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Convolutional neural networks for signal detection in real LIGO data

Popis výsledku v původním jazyce

Searching the data of gravitational-wave detectors for signals from compact binary mergers is a computationally demanding task. Recently, machine-learning algorithms have been proposed to address current and future challenges. However, the results of these publications often differ greatly due to differing choices in the evaluation procedure. The Machine Learning Gravitational-Wave Search Challenge was organized to resolve these issues and produce a unified framework for machine-learning search evaluation. Six teams submitted contributions, four of which are based on machine-learning methods, and two are state-of-the-art production analyses. This paper describes the submission from the team TPI FSU Jena and its updated variant. We also apply our algorithm to real O3b data and recover the relevant events of the GWTC-3 catalog.

Název v anglickém jazyce

Convolutional neural networks for signal detection in real LIGO data

Popis výsledku anglicky

Searching the data of gravitational-wave detectors for signals from compact binary mergers is a computationally demanding task. Recently, machine-learning algorithms have been proposed to address current and future challenges. However, the results of these publications often differ greatly due to differing choices in the evaluation procedure. The Machine Learning Gravitational-Wave Search Challenge was organized to resolve these issues and produce a unified framework for machine-learning search evaluation. Six teams submitted contributions, four of which are based on machine-learning methods, and two are state-of-the-art production analyses. This paper describes the submission from the team TPI FSU Jena and its updated variant. We also apply our algorithm to real O3b data and recover the relevant events of the GWTC-3 catalog.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10308 - Astronomy (including astrophysics,space science)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Physical Review D

ISSN

2470-0010

e-ISSN

2470-0029

Svazek periodika

110

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

024024

Kód UT WoS článku

001266959300001

EID výsledku v databázi Scopus

2-s2.0-85198606725