Assessing the detectability of a realistic massive black hole binary population in LSST
Abstract
Electromagnetic signatures of massive black hole binaries (MBHB) are expected to be found in quasar light curves. Specifically, MBHB light curves are expected to show periodic modulations which may originate from different physical processes. The detection of such periodicities requires algorithms suitable to model modulations with arbitrary shapes embedded in quasar red noise, in massive datasets. With the advent of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), such sources may finally be within reach in the next decade. In this work, we assess the performance of a recently proposed flexible detection algorithm for periodicities of arbitrary shapes with simulated LSST-like data that span its 10-year baseline. For the first time, we examine a physically motivated population of binaries and assess the effect of assumed priors on the period on the periodicity search. We study two methods to set the detection threshold: the first based on the posterior odds ratio, which assumes knowledge of the expected fraction of binaries in quasar light curves, and the second, which limits the detection of false positives. We find that the expected distribution of detected periods shows a clear peak between 5 and 10 years, depending on the specific LSST band considered. We recover a detected binary fraction ranging from ~ 20% to ~ 3% depending on the detection threshold assumed and the band analysed, with the peak of the observed period distribution depending on the underlying binary period distribution. Therefore, the detection of a population of binaries in LSST can constrain the binary population and shed light on their evolution.
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BibTeX
@misc{bertassi2026assessing,
title = {{Assessing the detectability of a realistic massive black hole binary population in LSST}},
author = {Lorenzo Bertassi and Beatrice Sala and Edyn Davies and Fabio Rigamonti and Maria Charisi and Riccardo Buscicchio and Paola Severgnini and Massimo Dotti},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.33159},
eprint = {2610.33159},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.33159},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Assessing the detectability of a realistic massive black hole binary population in LSST
AU - Lorenzo Bertassi
AU - Beatrice Sala
AU - Edyn Davies
AU - Fabio Rigamonti
AU - Maria Charisi
AU - Riccardo Buscicchio
AU - Paola Severgnini
AU - Massimo Dotti
PY - 2026
DA - 2026/10/23
PB - arXiv
DO - 10.48550/arxiv.2610.33159
UR - https://arxiv.org/abs/2610.33159
AB - Electromagnetic signatures of massive black hole binaries (MBHB) are expected to be found in quasar light curves. Specifically, MBHB light curves are expected to show periodic modulations which may originate from different physical processes. The detection of such periodicities requires algorithms suitable to model modulations with arbitrary shapes embedded in quasar red noise, in massive datasets. With the advent of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), such sources may finally be within reach in the next decade. In this work, we assess the performance of a recently proposed flexible detection algorithm for periodicities of arbitrary shapes with simulated LSST-like data that span its 10-year baseline. For the first time, we examine a physically motivated population of binaries and assess the effect of assumed priors on the period on the periodicity search. We study two methods to set the detection threshold: the first based on the posterior odds ratio, which assumes knowledge of the expected fraction of binaries in quasar light curves, and the second, which limits the detection of false positives. We find that the expected distribution of detected periods shows a clear peak between 5 and 10 years, depending on the specific LSST band considered. We recover a detected binary fraction ranging from ~ 20% to ~ 3% depending on the detection threshold assumed and the band analysed, with the peak of the observed period distribution depending on the underlying binary period distribution. Therefore, the detection of a population of binaries in LSST can constrain the binary population and shed light on their evolution.
ER -