arXiv subjects

Cecilia Maria Fabbri

Publications and source records attributed to Cecilia Maria Fabbri.

4 recordsLinked to original sources

arXiv2026

End-to-End Population Inference from Gravitational-Wave Strain using Transformers

The population of compact binaries encodes information about their astrophysical origins and the expansion of the universe. Hierarchical Bayesian methods infer these properties by combining single-event posteriors. As catalogs grow, however, this approach becomes computationally expensive and is subject to increasing Monte Carlo uncertainty. We introduce Dingo-Pop, a simulation-based framework that infers population posteriors directly from gravitational-wave strain data. The data for each event are embedded into low-dimensional tokens and combined using a transformer trained on simulated catalogs subject to selection effects. This enables (i) population inference without per-event Monte Carlo sampling noise, (ii) amortization across variable catalog sizes using a single network, and (iii) end-to-end inference in about one second. We train a network for catalog sizes of 25 to 1000 events, and obtain well-calibrated posteriors consistent with traditional methods. By avoiding per-event analyses that can take hours to days, Dingo-Pop enables new classes of large-scale injection studies; as an application, we examine how spectral-siren Hubble constant uncertainties change with catalog size.

gr-qc↗

arXiv2026

Variance of gravitational-wave populations

We quantify the impact of finite catalog size, or "catalog variance," on current gravitational-wave population analyses. The distribution of merging binary black holes is commonly reconstructed via hierarchical Bayesian inference, with uncertainties reported as credible intervals. Such intervals are conditioned on the specific realization of the observed events and are therefore themselves subject to variability arising from the finite size of the catalog. We estimate this "uncertainty on the uncertainty" using statistical bootstrapping applied to data segments containing both detected events and sensitivity injections. Applying this framework to GWTC-4, we find that the inferred population distributions exhibit substantially broader uncertainties than those obtained in a standard single-catalog analysis. In particular, the $\sim 35\,M_\odot$ peak in the primary-mass distribution is largely absorbed by statistical fluctuations once catalog variance is taken into account. Unlike other studies that rely on simulating catalogs by assuming an underlying population, this work provides the first data-driven assessment of the uncertainty intrinsic to the observed gravitational-wave catalog. Accounting for catalog variance is important for drawing robust astrophysical conclusions from gravitational-wave data, avoiding inferences driven by a particular finite realization rather than genuine population features.

astro-ph.HE↗

arXiv2025

Reconstructing parametric gravitational-wave population fits from non-parametric results without refitting the data

Combining multiple events into population analyses is a cornerstone of gravitational-wave astronomy. A critical component of such studies is the assumed population model, which can range from astrophysically motivated functional forms to non-parametric treatments that are flexible but difficult to interpret. In practice, the current approach is to fit the data multiple times with different population models to identify robust features. We propose an alternative strategy: assuming the data have already been fit with a flexible model, we present a practical recipe to reconstruct the population distribution of a different model. As our procedure postprocesses existing results, it avoids the need to access the underlying gravitational-wave data again and handle selection effects. Additionally, our reconstruction metric provides a goodness-of-fit measure to compare multiple models. We apply this method to the mass distribution of black-hole binaries detected by LIGO/Virgo/KAGRA. Our work paves the way for streamlined gravitational-wave population analyses by fitting the data once and for all with advanced non-parametric methods and careful handling of selection effects, while the astrophysical interpretation is then made accessible using our reconstruction procedure on targeted models. The key principle is that of conceptually separating data description from data interpretation.

astro-ph.HE↗

arXiv2022

The irreducible mass and the horizon area of LIGO's black holes

The mass of a Kerr black hole can be separated into irreducible and rotational components -the former is a lower limit to the energy that cannot be possibly extracted from the event horizon and is related to its area. Here we compute the irreducible masses of the stellar-mass black holes observed by gravitational-wave interferometers LIGO and Virgo. Using single-event data, we present a re-parametrization of the posterior distribution that explicitly highlights the irreducible and rotational contributions to the total energy. We exploit the area law to rank the black-hole mergers observed to date according to their irreversibility, thus providing a guide to selecting events for targeted tests of General Relativity. Using population fits, we compute the rate by which the total area of black-hole horizons increases due to the observable mergers.

gr-qc↗