A photometric classifier for tidal disruption events in Rubin LSST

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F25%3A00642119" target="_blank" >RIV/68378271:_____/25:00642119 - isvavai.cz</a>

Result on the web

<a href="https://hdl.handle.net/11104/0372143" target="_blank" >https://hdl.handle.net/11104/0372143</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1051/0004-6361/202556839" target="_blank" >10.1051/0004-6361/202556839</a>

Result language

angličtina

Original language name

A photometric classifier for tidal disruption events in Rubin LSST

Original language description

Context. Tidal disruption events (TDEs) are astrophysical phenomena that occur when stars are disrupted by supermassive black holes. The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), with its unprecedented depth and cadence, will detect thousands of TDEs, creating the need for robust photometric classifiers capable of efficiently distinguishing these events from other extragalactic transients. Aims. We developed and validated a machine learning pipeline for photometric TDE identification in LSST-scale datasets. Our classifier is designed to provide high precision and recall, enabling the construction of reliable TDE catalogs for multi-messenger follow-up and statistical studies. Methods. Using the second Extended LSST Astronomical Time Series Classification Challenge (ELAsTiCC2) dataset, we fit Gaussian processes (GPs) to light curves for feature extraction (e.g., color, rise and fade times, and GP length scales). We then trained and tuned boosted decision-tree models (XGBoost) with a custom scoring function that emphasizes the high-precision recovery of TDEs. Our pipeline was tested on diverse simulations of transient and variable events, including supernovae, active galactic nuclei, and superluminous supernovae. Results. We achieve high precision (up to 95%) while maintaining competitive recall (about 72%) for TDEs, with minimal contamination from non-TDE classes. Key predictive features include post-peak colors and GP hyperparameters that reflect the characteristic timescales and spectral behaviors of TDEs. Conclusions. Our photometric classifier provides a practical and scalable approach to identifying TDEs in forthcoming LSST data. By capturing essential color and temporal properties through GP-based feature extraction, it enables the efficient construction of clean TDE candidate samples. Future refinements will incorporate real data and additional features (e.g., photometric redshifts), further enhancing the reliability and scientific impact of this classification framework.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10303 - Particles and field physics

Project

<a href="/en/project/EH22_008%2F0004632" target="_blank" >EH22_008/0004632: Fundamental constituents of matter through frontier technologies</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

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

Name of the periodical

Astronomy & Astrophysics

ISSN

0004-6361

e-ISSN

1432-0746

Volume of the periodical

703

Issue of the periodical within the volume

Nov

Country of publishing house

FR - FRANCE

Number of pages

9

Pages from-to

A95

UT code for WoS article

001609664600015

EID of the result in the Scopus database

2-s2.0-105021346182