arXiv subjects

Mauro Pieroni

Publications and source records attributed to Mauro Pieroni.

At least 19 recordsLinked to original sources

arXiv2026

Detecting Gravitational-Wave Anisotropies with Simulation-Based Inference

Over the last five years, multiple Pulsar Timing Array (PTA) collaborations have reported mounting evidence for a gravitational-wave background (GWB) at nanohertz frequencies. Measuring anisotropies in the sky distribution of the GWB power is one of the most promising ways to identify and characterize its source. These anisotropies are expected to manifest as deviations from the Hellings-Downs (HD) correlations between the timing residuals of different pulsars. Current search strategies include Bayesian methods, which model anisotropies in the timing residuals likelihood, and faster frequentist approaches, which construct correlation estimators from timing residuals and use these to test the isotropic assumption. However, frequentist methods rely on the assumption that correlation estimators are Gaussian-distributed, an assumption that is not justified and that -- as we will show -- severely limits detection sensitivity. In this work, we present a Simulation-Based Inference (SBI) framework that replaces the analytic Gaussian likelihood used in frequentist searches with a neural network classifier trained on synthetic data. This approach captures the non-Gaussian structure of the data and significantly improves performance. Specifically, we find that the probability of $3\sigma$ detection increases by approximately 90% for single-hotspot scenarios and by 200% for double-hotspot scenarios compared to standard frequentist methods.

astro-ph.CO↗

arXiv2026

Are PTA measurements sensitive to gravitational wave non-Gaussianities?

Observing non-Gaussianities in the timing residuals of Pulsar Timing Arrays (PTAs) has recently attracted attention as a potential discriminator between astrophysical and cosmological origins of the observed Gravitational Wave (GW) signal. In this work, we show that even in an idealized signal-dominated setup, after decorrelating the data to avoid spurious detections, statistical tests applied to PTA data cannot distinguish between a Gaussian and a non-Gaussian amplitude distribution of the GWB in a model-agnostic way. In particular, without making strong assumptions on the GW spectrum or the properties of the population, the sensitivity to any distinctive non-Gaussian feature is washed out.

astro-ph.CO↗

arXiv2026

Solving the Cosmic Coincidence Problem: The Locally Pumped Dark Energy Model

We propose the Locally Pumped Dark Energy (LPDE) mechanism in which cosmic acceleration is triggered by the emergence of non-linear dark matter structure. In an effective-field-theory description, coarse-graining over the density contrast profile, whose short-wavelength modes grow during halo formation, induces a shift in the local equilibrium point of a second, sufficiently heavy scalar field $\chi$. At early times, the pump mechanism is negligible and $\chi$ remains fixed at the origin, contributing no DE. As structures form, the equilibrium value of $\chi$ is locally displaced within halos, generating a vacuum energy whose global contribution, in a mean-field picture, is controlled by the halo volume filling factor. If the $\chi$ field is sufficiently heavy, with a Compton wavelength limited by halo scales, its response is localised, and spatial gradients are exponentially suppressed on large scales. After volume-averaging over the halo population, the resulting contribution on large scales behaves as a homogeneous DE component. Using the halo mass function of a fiducial $\Lambda$CDM cosmology, we show that vacuum-energy domination generically emerges at $z\sim\mathcal{O}(1)$, naturally correlating cosmic acceleration with structure formation. For reference, we present an explicit realisation of such a mechanism and show that, by naturally featuring a transient acceleration epoch, it can be in excellent agreement with the most recent cosmological data, including the Dark Energy Spectroscopic Instrument (DESI).

astro-ph.CO↗

arXiv2025

Inflationary Particle Production and the Swampland

We investigate the impact of particle production during inflation in scenarios where an infinite tower of states features a mass scale that decreases exponentially along the inflationary trajectory. Such couplings naturally arise in string effective field theories and are in fact motivated by the Swampland Distance Conjecture (SDC). We show that the corrections to inflationary observables sourced by the tower scale as $(H/\Lambda_{\text{sp}})^{2+p}$, with $H$ being the Hubble scale, $\Lambda_{\text{sp}}$ being the species scale, that is the quantum gravity cut-off, and $p\geq 1$ characterizes the density of states in the tower. As a result, in gravitationally weakly coupled cosmological effective theories, the tower-induced contributions are suppressed relative to the standard single-field predictions, leaving the inflationary phenomenology essentially unchanged. We demonstrate this explicitly across a set of well-motivated inflationary potentials, and we compare the resulting predictions with the most recent observational constraints, including those from the Atacama Cosmology Telescope.

hep-th↗

arXiv2025

Dynamic SBI: Round-free Sequential Simulation-Based Inference with Adaptive Datasets

Simulation-based inference (SBI) is emerging as a new statistical paradigm for addressing complex scientific inference problems. By leveraging the representational power of deep neural networks, SBI can extract the most informative simulation features for the parameters of interest. Sequential SBI methods extend this approach by iteratively steering the simulation process towards the most relevant regions of parameter space. This is typically implemented through an algorithmic structure, in which simulation and network training alternate over multiple rounds. This strategy is particularly well suited for high-precision inference in high-dimensional settings, which are commonplace in physics applications with growing data volumes and increasing model fidelity. Here, we introduce dynamic SBI, which implements the core ideas of sequential methods in a round-free, asynchronous, and highly parallelisable manner. At its core is an adaptive dataset that is iteratively transformed during inference to resemble the target observation. Simulation and training proceed in parallel: trained networks are used both to filter out simulations incompatible with the data and to propose new, more promising ones. Compared to round-based sequential methods, this asynchronous structure can significantly reduce simulation costs and training overhead. We demonstrate that dynamic SBI achieves significant improvements in simulation and training efficiency while maintaining inference performance. We further validate our framework on two challenging astrophysical inference tasks: characterising the stochastic gravitational wave background and analysing strong gravitational lensing systems. Overall, this work presents a flexible and efficient new paradigm for sequential SBI.

astro-ph.IM↗

arXiv2025

Cosmic Variance in Anisotropy Searches at Pulsar Timing Arrays

Recent pulsar timing array (PTA) analyses show evidence for a gravitational wave background (GWB) with angular correlations consistent with the Hellings-Downs curve. Anisotropies are a key discriminator of the origin of this GWB, as they are expected to be at 1--20\% for astrophysical sources, but suppressed for cosmological GWBs. However, contrary to gravitational wave detectors at higher frequencies, PTAs only take a few independent measurements of a GWB and consequently are highly sensitive to cosmic variance, which induces apparent anisotropies in individual realizations of an isotropic GWB. We demonstrate explicitly that statistical inference nevertheless remains robust, i.e., measurements are consistent with the underlying assumption of isotropy. This confirms that searches for anisotropies will be able to robustly discriminate astrophysical from cosmological GWBs. En route, we demonstrate that the maximum multipole constrained by a PTA dataset scales linearly with the number of pulsars $\ell_{\rm max} \sim N_p$.

astro-ph.CO↗

arXiv2025

Simulation-based inference with deep ensembles: Evaluating calibration uncertainty and detecting model misspecification

Simulation-Based Inference (SBI) offers a principled and flexible framework for conducting Bayesian inference in any situation where forward simulations are feasible. However, validating the accuracy and reliability of the inferred posteriors remains a persistent challenge. In this work, we point out a simple diagnostic approach rooted in ensemble learning methods to assess the internal consistency of SBI outputs that does not require access to the true posterior. By training multiple neural estimators under identical conditions and evaluating their pairwise Kullback-Leibler (KL) divergences, we define a consistency criterion that quantifies agreement across the ensemble. We highlight two core use cases for this framework: a) for generating a robust estimate of the systematic uncertainty in parameter reconstruction associated with the training procedure, and b) for detecting possible model misspecification when using trained estimators on real data. We also demonstrate the relationship between significant KL divergences and issues such as insufficient convergence due to, e.g., too low a simulation budget, or intrinsic variance in the training process. Overall, this ensemble-based diagnostic framework provides a lightweight, scalable, and model-agnostic tool for enhancing the trustworthiness of SBI in scientific applications.

astro-ph.IM↗

arXiv2025

Detectability and Parameter Estimation for Einstein Telescope Configurations with GWJulia

Future gravitational-wave (GW) detectors are expected to detect tens of thousands of compact binary coalescences (CBC) per year, depending also on the final detectors layout. For this reason, it is essential to have a fast, reliable tool for forecasting how different detector layouts will affect parameter estimation for these events. The Fisher Information Matrix (FIM) is a common tool for tackling this problem. In this paper, we present a new open source code GWJulia to perform FIM analysis of CBC parameters, i.e., stellar black-hole binaries (BBH), neutron star binaries (BNS), and neutron star-black hole binaries (NSBH). The code is purely written in Julia, making it fast while maintaining a high level of accuracy. We consider a set of case studies to compare different Einstein Telescope (ET) designs. We compare a 10km triangular configuration with two 15km L-shaped detectors with different orientations and temperatures. We discuss also the accuracy of combinations of parameters, which is very informative for cosmology or population studies. Finally, we focus on the detection of golden events and explore how the FIM can guide posterior sampling of GW signals using a novel Hamiltonian Monte Carlo (HMC) sampler. The code is publicly available at https://github.com/andrea-begnoni/GW.jl

astro-ph.CO↗

arXiv2025

Likelihoods for Stochastic Gravitational Wave Background Data Analysis

We present a systematic study of likelihood functions used for Stochastic Gravitational Wave Background (SGWB) searches. By dividing the data into many short segments, one customarily takes advantage of the Central Limit Theorem to justify a Gaussian crosscorrelation likelihood. We show, with a hierarchy of ever more realistic examples, beginning with a single frequency bin and one detector, and then moving to two and three detectors with white and colored signal and noise, that approximating the exact Whittle likelihood by various Gaussian alternatives can induce systematic biases in the estimation of the SGWB parameters. We derive several approximations for the full likelihood and identify regimes where Gaussianity breaks down. We also discuss the possibility of conditioning the full likelihood on fiducial noise estimates to produce unbiased SGWB parameter estimation. We show that for some segment durations and bandwidths, particularly in space-based and pulsar-timing arrays, the bias can exceed the statistical uncertainty. Our results provide practical guidance for segment choice, likelihood selection, and data-compression strategies to ensure robust SGWB inference in current and next-generation gravitational wave detectors.

gr-qc↗

arXiv2025

Constraining primordial curvature perturbations with present and future GW detectors

Primordial scalar curvature perturbations ($\zeta$), typically probed on large cosmological scales via CMB and LSS observations, can be significantly enhanced on smaller scales by various early Universe mechanisms, for instance, non-minimal inflationary models. While decoupled at linear order, scalar and tensor perturbations, i.e., Gravitational Waves (GWs), interact at second order. As a consequence, an enhanced primordial scalar power spectrum $P_\zeta(k)$ can source a sizable stochastic GW background (SGWB). In these proceedings, we briefly review the generation mechanism of such signals, typically referred to as scalar-induced GWs (SIGWs), and discuss the prospects of measuring them with present and future Pulsar Timing Arrays datasets and future GW observatories like the Laser Interferometer Space Antenna LISA.

astro-ph.CO↗

arXiv2025

Forecasting Constraints on SIGW with Future Pulsar Timing Array Observations

Pulsar Timing Arrays are playing a crucial role in the ongoing gravitational wave astronomy revolution. The current evidence for a stochastic gravitational wave background (SGWB) at nHz frequencies offers an opportunity to discover cosmological signals and threatens the observability of other subdominant GWs. We explore prospects to constrain second-order scalar-induced GWs (SIGWs) associated with enhanced curvature perturbations in the primordial universe, forecasting realistic future PTA datasets. We assess how the currently observed signal could eventually limit future capabilities to search for GW relics of primordial phenomena and associated phenomenological consequences such as primordial black hole (PBH) formation. Given the sensitivity of PBH abundance to spectral parameters, measuring it remains a challenge for realistic signals. However, future observation could still rule out nearly subsolar mass PBHs formed through standard formation scenarios in some cases. Future progress in constraining PBH models is expected to stem from theoretical advancements in PBH computations, which should help resolve the tension between different computational methods. The analysis is based on and extends the Python code $\texttt{fastPTA}$.

astro-ph.CO↗

arXiv2025

Reconstructing Primordial Curvature Perturbations via Scalar-Induced Gravitational Waves with LISA

Many early universe scenarios predict an enhancement of scalar perturbations at scales currently unconstrained by cosmological probes. These perturbations source gravitational waves (GWs) at second order in perturbation theory, leading to a scalar-induced gravitational wave (SIGW) background. The LISA detector, sensitive to mHz GWs, will be able to constrain curvature perturbations in a new window corresponding to scales $k \in [10^{10}, 10^{14}] \,{\rm Mpc}^{-1}$, difficult to probe otherwise. In this work, we forecast the capabilities of LISA to constrain the source of SIGWs using different approaches: i) agnostic, where the spectrum of curvature perturbations is binned in frequency space; ii) template-based, modeling the curvature power spectrum based on motivated classes of models; iii) ab initio, starting from first-principles model of inflation featuring an ultra-slow roll phase. We compare the strengths and weaknesses of each approach. We also discuss the impact on the SIGW spectrum of non-standard thermal histories affecting the kernels of SIGW emission and non-Gaussianity in the statistics of the curvature perturbations. Finally, we propose simple tests to assess whether the signal is compatible with the SIGW hypothesis. The pipeline used is built into the SIGWAY code.

astro-ph.CO↗

arXiv2024

Assessing the Impact of Unequal Noises and Foreground Modeling on SGWB Reconstruction with LISA

In the search for stochastic gravitational wave backgrounds (SGWB) of cosmological origin with LISA, it is crucial to account for realistic complications in the noise and astrophysical foreground modeling that may impact the signal reconstruction. To address these challenges, we updated the $\texttt{SGWBinner}$ code to incorporate both variable noise levels across LISA arms and more complex foreground spectral shapes. Our findings suggest that, while moderate variations of the noise amplitudes have a minimal impact, poor foreground modeling (i.e., templates requiring many free parameters) significantly degrades the reconstruction of cosmological signals. This underlines the importance of accurate modeling and subtraction of the astrophysical foregrounds to characterize possible cosmological components. To perform this more challenging analysis, we have integrated the $\texttt{JAX}$ framework, which significantly improves the computational efficiency of the code, in the $\texttt{SGWBinner}$ code, enabling faster Bayesian likelihood sampling and more effective exploration of complex SGWB signals.

gr-qc↗

arXiv2024

The impact of a primordial gravitational wave background on LISA resolvable sources

The existence of a primordial stochastic gravitational wave background (SGWB) is a common prediction in various models of the early Universe. Despite constraints at different frequency ranges and claims of detection in the nHz range by Pulsar Timing Arrays, the amplitude and spectral dependence of the SGWB in the mHz range remain largely unknown. Plausible models of early Universe Physics predict a wide range of SGWB amplitudes, from undetectable to exceeding the constraints from Big Bang Nucleosynthesis. This paper explores the potential impact of a prominent primordial SGWB on LISA's main scientific targets. Our main analyses focuses on Massive Black Hole Binaries (MBHBs). By employing publicly available MBHB population models and state-of-the-art LISA's forecasting pipeline, we analyze the effects of the SGWB on MBHB detections. We find that the decrease in the signal-to-noise ratio induced by a strong primordial GWB can significantly reduce the number of detectable events, compromise the precision of constraints, and even hinder sky localization for some events. We also examine the impact of the SGWB on Stellar Origin Black Hole Binaries (SOBHBs) and Galactic Binaries (GBs), which are fainter sources than MBHBs. Depending on the spectral properties of the SGWB, we conclude that these sources could be either marginally affected or rendered completely undetectable. This largely unexplored aspect raises critical questions about the potential challenges posed by a prominent SGWB to LISA's astrophysical objectives, including MBHBs, SOBHBs, and GBs.

astro-ph.CO↗

arXiv2024

Pulsar timing array sensitivity to anisotropies in the gravitational wave background

Pulsar Timing Array (PTA) observations have recently gathered substantial evidence for the existence of a gravitational wave background in the nHz frequency band. Searching for anisotropies in this signal is key to determining its origin, and in particular to distinguish possible astrophysical from cosmological sources. In this work, we assess the sensitivity of current and future pulsar timing arrays to such anisotropies using the full covariance matrix of pulsar timing delays. While current day pulsar timing arrays can only set mildly informative constraints on the dipole and quadrupole, we show that percent level accuracy for several low multipoles can be achieved in the near future. Moreover, we demonstrate that anisotropies in the gravitational wave background and the Hellings-Downs angular correlation, indicating the presence of GWs, are approximately uncorrelated, and can hence be reconstructed independently. These results can be reproduced with \href{https://github.com/Mauropieroni/fastPTA}{\texttt{fastPTA}}, a publicly available Python code to forecast the constraining power of PTA configurations.

astro-ph.CO↗

arXiv2024

Leveraging Time-Dependent Instrumental Noise for LISA SGWB Analysis

Variations in the instrumental noise of the Laser Interferometer Space Antenna (LISA) over time are expected as a result of e.g. scheduled satellite operations or unscheduled glitches. We demonstrate that these fluctuations can be leveraged to improve the sensitivity to stochastic gravitational wave backgrounds (SGWBs) compared to the stationary noise scenario. This requires optimal use of data segments with downward noise fluctuations, and thus a data analysis pipeline capable of analysing and combining shorter time segments of mission data. We propose that simulation based inference is well suited for this challenge. In an approximate, but state-of-the-art, modeling setup, we show by comparison with Fisher Information Matrix estimates that the optimal information gain can be achieved in practice.

gr-qc↗

arXiv2024

Gravitational waves from inflation in LISA: reconstruction pipeline and physics interpretation

Various scenarios of cosmic inflation enhance the amplitude of the stochastic gravitational wave background (SGWB) at frequencies detectable by the LISA detector. We develop tools for a template-based analysis of the SGWB and introduce a template databank to describe well-motivated signals from inflation, prototype their template-based searches, and forecast their reconstruction with LISA. Specifically, we classify seven templates based on their signal frequency shape, and we identify representative fundamental physics models leading to them. By running a template-based analysis, we forecast the accuracy with which LISA can reconstruct the template parameters of representative benchmark signals, with and without galactic and extragalactic foregrounds. We identify the parameter regions that can be probed by LISA within each template. Finally, we investigate how our signal reconstructions shed light on fundamental physics models of inflation: we discuss their impact for measurements of \emph{e.g.,} ~the couplings of inflationary axions to gauge fields; the graviton mass during inflation; the fluctuation seeds of primordial black holes; the consequences of excited states during inflation, and the presence of small-scale spectral features.

astro-ph.CO↗

arXiv2024

Simulation-based inference for stochastic gravitational wave background data analysis

The next generation of space- and ground-based facilities promise to reveal an entirely new picture of the gravitational wave sky: thousands of galactic and extragalactic binary signals, as well as stochastic gravitational wave backgrounds (SGWBs) of unresolved astrophysical and possibly cosmological signals. These will need to be disentangled to achieve the scientific goals of experiments such as LISA, Einstein Telescope, or Cosmic Explorer. We focus on one particular aspect of this challenge: reconstructing an SGWB from (mock) LISA data. We demonstrate that simulation-based inference (SBI) - specifically truncated marginal neural ratio estimation (TMNRE) - is a promising avenue to overcome some of the technical difficulties and compromises necessary when applying more traditional methods such as Monte Carlo Markov Chains (MCMC). To highlight this, we show that we can reproduce results from traditional methods both for a template-based and agnostic search for an SGWB. Moreover, as a demonstration of the rich potential of SBI, we consider the injection of a population of low signal-to-noise ratio supermassive black hole transient signals into the data. TMNRE can implicitly marginalize over this complicated parameter space, enabling us to directly and accurately reconstruct the stochastic (and instrumental noise) contributions. We publicly release our TMNRE implementation in the form of the code saqqara.

gr-qc↗