Data-driven detection of multimessenger transients

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F20%3A00546364" target="_blank" >RIV/68378271:_____/20:00546364 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.3847/2041-8213/ab8b5f" target="_blank" >https://doi.org/10.3847/2041-8213/ab8b5f</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3847/2041-8213/ab8b5f" target="_blank" >10.3847/2041-8213/ab8b5f</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Data-driven detection of multimessenger transients

Popis výsledku v původním jazyce

The primary challenge in the study of explosive astrophysical transients is their detection and characterization using multiple messengers. For this purpose, we have developed a new data-driven discovery framework, based on deep learning. We demonstrate its use for searches involving neutrinos, optical supernovae, and gamma-rays. We show that we can match or substantially improve upon the performance of state-of-the-art techniques, while significantly minimizing the dependence on modeling and on instrument characterization. Particularly, our approach is intended for near- and real-time analyses, which are essential for effective follow-up of detections. Our algorithm is designed to combine a range of instruments and types of input data, representing different messengers, physical regimes, and temporal scales. The methodology is optimized for agnostic searches of unexpected phenomena, and has the potential to substantially enhance their discovery prospects.

Název v anglickém jazyce

Data-driven detection of multimessenger transients

Popis výsledku anglicky

The primary challenge in the study of explosive astrophysical transients is their detection and characterization using multiple messengers. For this purpose, we have developed a new data-driven discovery framework, based on deep learning. We demonstrate its use for searches involving neutrinos, optical supernovae, and gamma-rays. We show that we can match or substantially improve upon the performance of state-of-the-art techniques, while significantly minimizing the dependence on modeling and on instrument characterization. Particularly, our approach is intended for near- and real-time analyses, which are essential for effective follow-up of detections. Our algorithm is designed to combine a range of instruments and types of input data, representing different messengers, physical regimes, and temporal scales. The methodology is optimized for agnostic searches of unexpected phenomena, and has the potential to substantially enhance their discovery prospects.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10303 - Particles and field physics

Projekt

—

Návaznosti

—

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Astrophysical Journal Letters

ISSN

2041-8205

e-ISSN

2041-8213

Svazek periodika

894

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

L25

Kód UT WoS článku

000536137000001

EID výsledku v databázi Scopus

2-s2.0-85086221804