most citedInferring the population properties of galactic binaries from LISA's stochastic foreground

1 citations · 1 across the 2 of their papers we have counts for

collaborators

9 papers

astro-ph.GA2026

A recoiling supermassive black hole in a powerful quasar

Marco Chiaberge, Takahiro Morishita, Matteo Boschini +14

Supermassive black holes (SMBH) are thought to grow through accretion of matter and mergers. Models of SMBH mergers have long suffered the final parsec problem, where SMBH binaries…

astro-ph.HE20261 cited

Inferring the population properties of galactic binaries from LISA's stochastic foreground

Federico De Santi, Alessandro Santini, Alexandre Toubiana +2

Galactic binaries are expected to be the most numerous LISA sources and to produce a stochastic gravitational-wave foreground whose spectral shape encodes information about the und…

gr-qc2026

Comparing astrophysical models to gravitational-wave data in the observable space

Alexandre Toubiana, Davide Gerosa, Matthew Mould +9

Comparing population-synthesis models to the results of hierarchical Bayesian inference in gravitational-wave astronomy requires a careful understanding of the domain of validity o…

gr-qc2026

Ab uno disce omnes: Single-harmonic search for extreme mass-ratio inspirals

Lorenzo Speri, Rodrigo Tenorio, Christian Chapman-Bird +1

Extreme mass-ratio inspirals (EMRIs) are one of the key sources of gravitational waves for space-based detectors such as LISA. However, their detection remains a major data analysi…

astro-ph.HE2026

Exceptionality of exceptional gravitational-wave events

Rodrigo Tenorio, Davide Gerosa

In gravitational-wave astronomy, as in other scientific disciplines, ``exceptional'' sources attract considerable interest because they challenge our current understanding of the u…

astro-ph.GA2025

Bayesian luminosity function estimation in multi-depth datasets with selection effects: A case study for Lyman emitters

Davide Tornotti, Matteo Fossati, Michele Fumagalli +3

We present a hierarchical Bayesian framework designed to infer the luminosity function of any class of object by jointly modelling data from multiple surveys with varying depth, co…