Data-driven detection of multimessenger transients

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Data-driven detection of multimessenger transients
Original language description
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.
Czech name
—
Czech description
—

Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10303 - Particles and field physics

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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