arXiv subjects

Riccardo Buscicchio

Publications and source records attributed to Riccardo Buscicchio.

At least 19 recordsLinked to original sources

arXiv2026

Implications of the LISA stochastic signal from eccentric stellar mass black hole binaries in vacuum

Astrophysical formation channels of stellar-mass binary black holes (sBBHs) can induce significant orbital eccentricities in their early inspiral. We analyze the implications on the stochastic gravitational-wave background (SGWB) from unresolved sBBHs, which can be detected with the Laser Interferometer Space Antenna (LISA). We develop an improved SGWB model for the case of an idealized Dirac-delta eccentricity distribution, and extend it to the more astrophysical case of a thermal distribution. Using a fully Bayesian framework, we find that, if all binaries have a high initial eccentricity $e_0 \gtrsim 0.9$ at an orbital frequency of $f_{\rm orb} = 10^{-4}\,\mathrm{Hz}$, the resulting SGWB can be robustly distinguished from a background of quasi-circular sBBHs. For a thermal eccentricity distribution, the SGWB is consistent with a circular model when binaries form at $f_{\rm orb} = 10^{-5}\,\mathrm{Hz}$, but leads to significant systematic biases if formation occurs at $f_{\rm orb} = 10^{-4}\,\mathrm{Hz}$. We also show that, when eccentricity is properly accounted for, environmental effects such as dynamical friction can be distinguished from vacuum evolution, but only for sufficiently dense environments with gas densities $ρ\gtrsim 10^{-7}\,\mathrm{g\,cm^{-3}}$. Finally, we show that a LISA detection of the sBBH SGWB would place an upper bound on the maximum eccentricity of the sBBH population in the band of ground-based detectors, with direct implications for template modeling and data analysis. Our results highlight the importance of incorporating eccentricity in SGWB modeling to enable accurate astrophysical interpretation of LISA observations.

gr-qc↗

arXiv2026

Fast premerger detection of massive black-hole binaries in LISA based on time-frequency excess power

The Laser Interferometer Space Antenna is expected to observe gravitational waves from massive black hole binaries across cosmic time. Many are anticipated to be detectable hours to weeks before coalescence. We present a fast algorithm for the premerger detection and preliminary characterization of such binaries. The method performs a search for excess power with a chirping time-frequency morphology in short-time Fourier transform spectrograms. By tiling the time-frequency plane with slices defined by the quadrupole frequency evolution, we define a signal significance relative to a fitted background distribution of instrumental noise and Galactic foreground. Individual search triggers are followed by a coherence tracker that groups triggers consistent with the same physical signal over time. Doing so, our analysis provides progressively refined estimates of the chirp mass and coalescence time. We validate our algorithm on the SangriaHM LISA Data Challenge dataset, successfully detecting all 15 injected massive black-hole binaries: 14 of them hours to weeks before merger, while one is only detected after the binary coalescence. The algorithm yields chirp mass relative errors below $3\%$ for high-SNR sources and coalescence time uncertainties of up to a few hours. With a computational cost of less than a second to process a 10-day data segment on a single core, our approach is suitable for generating real-time alerts, triggering protected observational periods, and providing informative priors for Bayesian parameter estimation.

astro-ph.IM↗

arXiv2026

Accurate and efficient simulation-based inference for massive black-hole binaries with LISA

We develop an accurate simulation-based inference framework for high-mass ($\gtrsim\!10^7 \rm{M_\odot}$) black-hole binaries observable by LISA. The method is implemented within the DINGO gravitational-wave parameter-estimation code, extending its application from ground-based detectors to the LISA band. We train a normalizing-flow model using aligned-spin higher-mode waveform models and a low-frequency approximation of the detector response at fixed reference time. After sampling, we importance-sample to the true posterior based on the underlying likelihood and prior. We validate performance on simulated signals spanning the signal-to-noise regimes relevant for LISA observations and benchmark our new DINGO implementation against standard methods. We report robust agreement in the inferred posterior distributions up to signal-to-noise ratios of $\sim\!500$. At higher signal-to-noise ratios of $\sim\!1000$, we observe a reduction in sampling efficiency, while still yielding unbiased and tightly localized posteriors that can be used as a starting point for follow-up with traditional methods. The trained flow can generate 20 thousand posterior samples in less than a minute, establishing DINGO as a promising neural inference framework for rapid full-parameter estimation of massive black-hole binaries in the LISA band. The proposed approach allows for straightforward generalizations, including a time-dependent detector response, non-stationary noise artifacts such as gaps and glitches, and low-latency parameter estimations.

astro-ph.HE↗

arXiv2026

Identification of periodicities with arbitrary shapes in AGN light curves

Massive black hole binaries are expected to be observable as periodic AGN in time-domain photometric surveys. Periodicities may originate from different physical processes, including the intermittent gas feeding of the black holes caused by the time-varying non-axisymmetric binary potential, the Doppler boosting of the flux emitted by individual accretion discs bound to the orbiting BHs, and the gravitational lensing of the accretion disc of one black hole due to the presence of the other. Only the Doppler boost scenario applied to circular binaries with non-modulated accretion predicts a sinusoidal light curve, while in the general case, binary signals are expected to show more complex periodic patterns. Current searches for massive black hole binaries rely on techniques tailored to quasi-sinusoidal light curves, but fail to identify the more complex periodicities predicted. We present an alternative method that leverages Gaussian processes, making use of a generic periodic kernel flexible enough for the identification of arbitrary periodicities in unevenly sampled light curves with realistic quasar noise. We demonstrate that it outperforms previously proposed strategies in identifying general periodicities by analysing mock light curves with different baselines. Specifically, we find that our analysis can detect non-sinusoidal periodicities (e.g., sawtooth-shaped) and retrieves a higher fraction of true periodicities when compared to periodogram analysis or Gaussian processes analysis with less flexible periodic kernels. Furthermore, by comparing the retrieved fraction of periodicities between mock PTF light curves and mock LSST light curves, we find that our analysis is most sensitive to the number of observed cycles. The application of this analysis has the potential to greatly increase the scientific return of current and upcoming large time-domain photometric surveys.

astro-ph.GA↗

arXiv2026

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

Comparing population-synthesis models to the results of hierarchical Bayesian inference in gravitational-wave astronomy requires a careful understanding of the domain of validity of the models fitted to data. This comparison is usually done using the inferred astrophysical distribution: from the data that were collected, one deconvolves selection effects to reconstruct the generating population distribution. In this paper, we demonstrate the benefits of instead comparing observable populations directly. In this approach, the domain of validity of the models is trivially respected, such that only the relevant parameter space regions as predicted by the astrophysical models of interest contribute to the comparison. With this in mind, it can be useful to fit the observed population directly, rather than effectively deconvolving the selection effects only to fold them back in when reconstructing the observable population. We clarify that unbiased inference of the observable compact-binary population is indeed possible. Crucially, this approach still requires incorporating selection effects, but in a manner that differs from the standard implementation. We apply our observable-space reconstruction to LIGO-Virgo-KAGRA data from their third observing run and illustrate its potential by comparing the results to the predictions of a fiducial population-synthesis model.

gr-qc↗

arXiv2025

samsara: A Continuous-Time Markov Chain Monte Carlo Sampler for Trans-Dimensional Bayesian Analysis

Bayesian inference requires determining the posterior distribution, a task that becomes particularly challenging when the dimension of the parameter space is large and unknown. This limitation arises in many physics problems, such as Mixture Models (MM) with an unknown number of components or the inference of overlapping signals in noisy data, as in the Laser Interferometer Space Antenna (LISA) Global Fit problem. Traditional approaches, such as product-space methods or Reversible-Jump Markov Chain Monte Carlo (RJMCMC), often face efficiency and convergence limitations. This paper presents samsara, a Continuous-Time Markov Chain Monte Carlo (CTMCMC) framework that models parameter evolution through Poisson-driven birth, death, and mutation processes. samsara is designed to sample models of unknown dimensionality. By requiring detailed balance through adaptive rate definitions, CTMCMC achieves automatic acceptance of trans-dimensional moves and high sampling efficiency. The code features waiting time weighted estimators, optimized memory storage, and a modular design for easy customization. We validate samsara on three benchmark problems: an analytic trans-dimensional distribution, joint inference of sine waves and Lorentzians in time series, and a Gaussian MM with an unknown number of components. In all cases, the code shows excellent agreement with analytical and Nested Sampling results. All these features push samsara as a powerful alternative to RJMCMC for large- and variable-dimensional Bayesian inference problems.

stat.CO↗

arXiv2025

The first year of LISA Galactic foreground

Galactic white-dwarf binaries play a central role in the inference model for the Laser Interferometer Space Antenna. In this manuscript, we employ the $\texttt{bahamas}$ codebase to characterize, in a global-fit fashion, the reconstruction of the Galactic foreground during the first year of observation. To account for its statistical properties, we represent the data in time--frequency domain, and characterize the effectiveness of multiple approaches, e.g. statistically viable likelihoods, sampling schemes, segmentation widths, and gaps density. Our analysis yields consistent results across, with overwhelming evidence in favor of a non-stationary model in less than a month of data. Moreover, we show robustness against the presence of additional extragalactic foregrounds, and test the suitability of our approximations on the more complex simulated data in the $\textit{Yorsh}$ data challenge.

astro-ph.IM↗

arXiv2025

Muffled Murmurs: Environmental effects in the LISA stochastic signal from stellar-mass black hole binaries

The population of unresolved stellar-mass black hole binaries (sBBHs) is expected to produce a stochastic gravitational-wave background (SGWB) potentially detectable by the Laser Interferometer Space Antenna (LISA). In this work, we compute the imprint of astrophysical environmental effect--such as gas dynamical friction and accretion--on this background. Using the sBBHs population constraints obtained by the LIGO--Virgo--Kagra collaboration, we compute the expected SGWB and develop a phenomenological parametric model that can accurately capture the effect of dynamical friction and accretion. Using our model, we perform Bayesian inference on simulated signals to assess the detectability of these environmental effects. We find that even for large injected values of the Eddington ratio, the effect of accretion in the SGWB is undetectable by LISA. However, LISA will be able to constrain the effect of dynamical friction with an upper bound on the gas density of $ρ\lesssim 7.6 \times 10^{-10} \mathrm{g \, cm^{-3}}$, thus probing the sBBH environment forming in typical thin accretion disks around Active Galactic Nuclei (AGNs). For injected densities of $ρ\sim 10^{-10}-10^{-9} \mathrm{g} \, \mathrm{cm}^{-3}$, dynamical friction effects can be well measured and clearly distinguished from vacuum, with Bayes factors reaching up to $\sim$ 60, even when the Galactic foreground is included.

gr-qc↗

arXiv2025

Variability in the supermassive black hole binary candidate SDSS J2320+0024: No evidence of periodic modulation

Supermassive black hole binaries (SBHBs) are a natural outcome of galaxy mergers, and they are expected to be among the loudest gravitational-wave sources at low frequencies. The source SDSS J2320+0024 was recently proposed as a promising SBHB candidate due to a possible periodicity in its light curve and variability in the MgII emission line. In this work, we reanalysed the optical (g, r, and i bands) light curves of J2320+0024 within the framework of Bayesian model selection. When periodicity was searched for together with red noise, analysis of the g-band light curve reveals a peak in the posterior of the period at ~290 days. The posterior profile is too broad to yield a preference for periodic models over models that include only red noise. Furthermore, the same peak is not present in the analysis of the r-band and i-band light curve. A periodic model without red noise identified a different (~1100 days) periodicity, but this model is statistically significantly disfavoured relative to the other models tested. In summary, we find no significant evidence in favour of a true periodic signal over red-noise variability. Our analysis questions the robustness of the previously proposed periodicity and emphasises the importance of rigorous statistical treatment. While our findings challenge the binary interpretation for J2320+0024, they do not rule it out. A statistically robust joint analysis of the photometric light curves and evolving broad-line profiles would shed further light on the true nature of this object.

astro-ph.GA↗

arXiv2025

Is your stochastic signal really detectable?

Separating a stochastic gravitational wave background (SGWB) from noise is a challenging statistical task. One approach to establishing a detection criterion for the SGWB is using Bayesian evidence. If the evidence ratio (Bayes factor) between models with and without the signal exceeds a certain threshold, the signal is considered detected. We present a formalism to compute the averaged Bayes factor, incorporating instrumental-noise and SGWB uncertainties. As an example, we consider the case of power-law-shaped SGWB in LISA and generate the corresponding Bayesian sensitivity curve. Unlike existing methods in the literature, which typically neglect uncertainties in both the signal and noise, our approach provides a reliable and realistic alternative. This flexible framework opens avenues for more robust stochastic gravitational wave background detection across gravitational-wave experiments.

astro-ph.IM↗

arXiv2025

Test for LISA foreground Gaussianity and stationarity: galactic white-dwarf binaries

Upcoming space-based gravitational-wave detectors will be sensitive to millions and resolve tens of thousands of stellar-mass binary systems at mHz frequencies. The vast majority of these will be double white dwarfs in our Galaxy. The greatest part will remain unresolved, forming an incoherent stochastic foreground signal. Using state-of-the-art Galactic models for the formation and evolution of binary white dwarfs and accurate LISA simulated signals, we introduce a test for foreground Gaussianity and stationarity, building on methods available for ground-based detectors. We explain the observed non-stationarity with a new analytical modulation induced by the LISA constellation motion and the intrinsic anisotropy of the source distribution. By demodulating the foreground signal, we reveal a deviation from Gaussianity in the 2-10 mHz frequency band. Our finding is crucial to design faithful data models: the proposed method serves as a diagnostic and estimation tool to flag and model deviations, respectively. Neglecting them would introduce systematic biases on individual sources and astrophysical foregrounds parameter estimation, ultimately leading to inaccurate interpretation of the LISA data.

astro-ph.HE↗

arXiv2025

Functional inference on deviations from General Relativity

Extensions of general relativity often predict modifications to gravitational waveform morphology that depend functionally on source parameters, such as the masses and spins of coalescing black holes. However, current analyses of strong-field gravity lack robust, data-driven methods to infer such functional dependencies. In this work, we introduce GRANITA, a non-perturbative, theory-agnostic framework to characterize parameter-dependent deviations from general relativity using Gaussian process regression. Leveraging the flexibility of this method, we analyze both simulated data and real events from the LIGO-Virgo-KAGRA public catalog. We demonstrate the ability of our approach to detect and quantify waveform deviations across the parameter space. Furthermore, we show that the method can identify stochastic (non-deterministic) deviations, potentially arising from environmental effects or subdominant unmodeled physics. As gravitational-wave tests of strong gravity advance in precision, our framework provides a principled approach to constrain modified gravity and to mitigate contamination from astrophysical or instrumental systematics.

gr-qc↗

arXiv2025

Accelerating LISA inference with Gaussian processes

Source inference for deterministic gravitational waves is a computationally demanding task in LISA. In a novel approach, we investigate the capability of Gaussian Processes to learn the posterior surface in order to reconstruct individual signal posteriors. We use GPry, which automates this reconstruction through active learning, using a very small number of likelihood evaluations, without the need for pretraining. We benchmark GPry against the cutting-edge nested sampler nessai, by injecting individually three signals on LISA noisy data simulated with Balrog: a white dwarf binary (DWD), a stellar-mass black hole binary (stBHB), and a super-massive black hole binary (SMBHB). We find that GPry needs $\mathcal O(10^{-2})$ fewer likelihood evaluations to achieve an inference accuracy comparable to nessai, with Jensen-Shannon divergence $D_{\scriptscriptstyle \mathrm{JS}} \lesssim 0.01$ for the DWD, and $D_{\scriptscriptstyle \mathrm{JS}} \lesssim 0.05$ for the SMBHB. Lower accuracy is found for the less Gaussian posterior of the stBHB: $D_{\scriptscriptstyle \mathrm{JS}} \lesssim 0.2$. Despite the overhead costs of GPry, we obtain a speed-up of $\mathcal O(10^2)$ for the slowest cases of stBHB and SMBHB. In conclusion, active-learning Gaussian process frameworks show great potential for rapid LISA parameter inference, especially for costly likelihoods, enabling suppression of computational costs without the trade-off of approximations in the calculations.

astro-ph.HE↗

arXiv2025

Bahamas: BAyesian inference with HAmiltonian Montecarlo for Astrophysical Stochastic background

The LISA datastream will be populated by large instrumental and astrophysical noises, both potentially exhibiting long-term non-stationarities. Modelling and inferring on them is a challenging task, central for accurate signal reconstruction. In this paper, we introduce $\texttt{bahamas}$, a codebase designed to characterize noises and stochastic gravitational wave backgrounds (SGWBs) in LISA. $\texttt{bahamas}$ adopts a time-frequency data representation, based on the Short Time Fourier Transform, to accurately describe the signal temporal evolution and accommodate for the presence of data gaps. In addition, $\texttt{bahamas}$ supports a variety of SGWB spectral models proposed in literature, enabling joint inference on them. Posterior sampling leverages No-U-Turn sampling an efficient variant of Hamiltonian Monte Carlo, inheriting the cross-hardware capabilities provided by NumPyro (CPU/GPU/TPU). We benchmark $\texttt{bahamas}$ performances on a simple test case, and present ongoing developments to appear in future releases.

astro-ph.IM↗

arXiv2025

Test for LISA foreground Gaussianity and stationarity: extreme mass-ratio inspirals

Extreme Mass Ratio Inspirals (EMRIs) are key observational targets for the Laser Interferometer Space Antenna (LISA) mission. Unresolvable EMRI signals contribute to the formation of a gravitational wave background (GWB). Characterizing the statistical features of the GWB from EMRIs is of great importance, as EMRIs will ubiquitously affect large segments of the inference scheme. In this work, we apply a frequentist test for GWB Gaussianity and stationarity, exploring three astrophysically-motivated EMRI populations. We construct the resulting signal by combining state-of-the-art EMRI waveforms and a detailed description of the LISA response with time-delay interferometric variables.Depending on the brightness of the GWB, our analysis demonstrates that the resultant EMRI foregrounds show varying degrees of departure from the usual statistical assumptions that the GWBs are both Gaussian and Stationary. If the GWB is non-stationary with non-Gaussian features, this will challenge the robustness of Gaussian-likelihood model, when applied to global inference results, e.g. foreground estimation, background detection, and individual-source parameters reconstruction.

astro-ph.HE↗

arXiv2025

Cyclostationary signals in LISA: a practical application to Milky Way satellites

One of the primary sources of gravitational waves (GWs) anticipated to be detected by the Laser Interferometer Space Antenna (LISA) are Galactic double white dwarf binaries (DWDs). However, most of these binaries will be unresolved, and their GWs will overlap incoherently, creating a stochastic noise known as the Galactic foreground. Similarly, the population of unresolved systems in the Milky Way's (MW) satellites is expected to contribute to a stochastic gravitational wave background (SGWB). Due to their anisotropy and the annual motion of the LISA constellation, both the Galactic foreground and the satellite SGWB fall into the category of cyclostationary processes. Leveraging this property, we develop a purely frequency-based method to study LISA's capability to detect the MW foreground and SGWBs from the most promising MW satellites. We analyze both mock data generated by an astrophysically motivated SGWB spectrum, and realistic ones from a DWD population generated via binary population synthesis. We are able to recover or put constrains on the candidate foregrounds, reconstructing -- in the presence of noise uncertainties -- their sky distribution and spectrum. Our findings highlight the significance of the interplay between the astrophysical spectrum and LISA's sensitivity to detect the satellites' SGWB. Considering an astrophysically motivated prior on the satellite positions improves their detectability, which becomes otherwise challenging in the presence of the Galactic foreground. Furthermore, we explore the potential to observe a hypothetical satellite located behind the Galactic disk. Our results suggest that a Large Magellanic Cloud-like satellite could indeed be observable by LISA.

astro-ph.GA↗

arXiv2025

Stars or gas? Constraining the hardening processes of massive black-hole binaries with LISA

Massive black-hole binaries will be the loudest sources detectable by LISA. These systems are predicted to form during the hierarchical assembly of cosmic structures and coalesce by interacting with the surrounding environment. The hardening phase of their orbit is driven by either stars or gas and encodes distinctive features into the binary black holes that can potentially be reconstructed with gravitational-wave observations. We present a Bayesian framework to assess the likelihood of massive mergers being hardened by either gaseous or stellar interactions. We use state-of-the-art astrophysical models tracking the cosmological evolution of massive black-hole binaries and construct a large number of simulated catalogs of sources detectable by LISA. From these, we select a representative catalog and run both parameter estimation assuming a realistic LISA response as well model comparison capturing selection effects. Our results suggest that, at least within the context of the adopted models, future LISA observations can confidently constrain whether stars or gas are responsible for the binary hardening. We stress that accurate astrophysical modeling of the black-hole spins and the inclusion of subdominant emission modes in the adopted signal might be crucial to avoid systematic biases.

astro-ph.HE↗

arXiv2024

Partial alignment between jets and megamasers: Coherent versus selective accretion

Spins play a crucial role in the appearance, evolution, and occupation fraction of massive black holes (MBHs). To date, observational estimates of MBH spins are scarce, and the assumptions commonly made in such estimates have recently been questioned. Similarly, theoretical models for MBH spin evolution, while reproducing the few observational constraints, are based on possibly oversimplified assumptions. New independent constraints on MBH spins are therefore of primary importance. We present a rigorous statistical analysis of the relative orientation of radio jets and megamaser disks in ten low-redshift galaxies. We find a strong preference for (partial) alignment between jets and megamaser that can be attributed to two different causes: coherent accretion and selective accretion. In the first case the partial alignment is due to an anisotropy in the gas reservoir fueling the growth of MBHs. In the second case the spin-dependent anisotropic feedback allows long-lived accretion only if the orbits of the gas inflows are almost aligned to the MBH equatorial plane. A discussion of the implications of the two accretion scenarios regarding the evolution of MBHs is presented, together with an outlook on future observational tests aiming at discriminating between the two scenarios and checking whether either applies to different redshifts and black hole mass regimes.

astro-ph.GA↗