arXiv subjects

Lorenzo Speri

Publications and source records attributed to Lorenzo Speri.

At least 19 recordsLinked to original sources

arXiv2026

Quantifying the Scientific Potential of Intermediate and Extreme Mass Ratio Inspirals with the Laser Interferometer Space Antenna

The Laser Interferometer Space Antenna (LISA) will enable precision studies of Extreme and Intermediate Mass Ratio Inspirals (EMRIs/IMRIs), providing unique probes of astrophysical environments of galactic nuclei and strong-field gravity. Using a fully relativistic pipeline across primary masses $m_1 \in [5\times10^4, 10^7]\,M_\odot$ and secondary masses $m_2 \in [1, 10^4]\,M_\odot$, we map instrumental performance directly to detection horizons and parameter measurement precision. EMRIs with $m_1 = 10^7\,M_\odot$ and $m_2 \sim 1\,M_\odot$ are the most sensitive to instrument degradation, with redshift horizons at $z \sim 0.01$, while IMRIs are the least sensitive to degradation and reach redshifts $z \sim 1-3$. All prograde systems considered achieve sub-percent spin precision within three months of observation. The full 4.5-year mission increases the horizon of systems with $m_1 = 10^7\,M_\odot$ and $m_2 \sim 1\,M_\odot$ by a factor of $\sim 4$ and improves sky localization by one to two orders of magnitude reaching $ < 10\,\mathrm{deg}^2$. IMRI detection is robust against degradation, but their parameter estimation is more vulnerable due to fewer cycles in band. With the full baseline, EMRI observations constrain scalar dipole emission and Kerr quadrupole deviations below ground-based bounds by one to two orders of magnitude. We release the accompanying software and an interactive website to enable the community to rapidly quantify the scientific potential of EMRIs and IMRIs.

astro-ph.IM↗

arXiv2026

An explicit and differentiable Wilson-Daubechies-Meyer transform for gravitational-wave data analysis

The Wilson-Daubechies-Meyer (WDM) time-frequency transform has been widely used in gravitational-wave astronomy, yet a self-contained, mathematically explicit reference for practitioners remains lacking. This is especially true for those wishing to adopt the transform in modern Python and JAX inference workflows. We present wdm_transform, an open-source Python package implementing the WDM wavelet-packet time-frequency transform, and document its mathematical foundations, statistical properties, and practical implementation for gravitational-wave data analysis. The package supplies NumPy and JAX backends, both transforms (forward and inverse) validated to floating-point precision, with the JAX backend enabling GPU-accelerated transforms of million-point data streams in tens of milliseconds. As a worked example, we verify that the WDM-domain likelihood reproduces frequency-domain posteriors for a resolved LISA galactic binary under a shared stationary noise model, confirming numerical equivalence of the two representations in that controlled setting. This work paves the way for systematic optimisation of WDM tilings, a particularly promising direction for the non-stationary noise, stochastic backgrounds, and data gaps anticipated in future detectors, and for direct comparisons with alternative time-frequency representations needed to meet the challenges of future gravitational-wave data analysis.

gr-qc↗

arXiv2026

Impact of Spacecraft Orbit Uncertainties and Velocity Mismodeling on the LISA Gravitational-Wave Response

The Laser Interferometer Space Antenna (LISA) is a space-based gravitational wave observatory that consists of three spacecraft in a near-equilateral triangular formation. The spacecraft orbits are typically assumed to be perfectly known in LISA data analysis studies, but in reality, the orbit determination process introduces uncertainties in the spacecraft positions and velocities. In this work, we investigate how these uncertainties propagate into the LISA detector output and the impact of neglecting the spacecraft velocities. We quantify these errors in the knowledge of the LISA response using mismatches and discuss the implications for gravitational wave data analysis. We find that spacecraft orbit uncertainties impact the LISA response knowledge at high frequencies with worst mismatch below $10^{-7}$. The effect of neglecting the spacecraft velocities is largest at frequencies around $10^{-4}$ Hz with mismatches of order $10^{-4}$. For a galactic binary with frequency $10^{-4}$ Hz and SNR=200 observed for one year, we find that neglecting the spacecraft velocities in the response leads to less than 1-$σ$ biases in the parameter estimates. This work provides the first characterization of how errors in the LISA gravitational wave response propagate from gravitational wave strain through detector output to estimated parameters.

gr-qc↗

arXiv2026

Sequential simulation-based inference for extreme mass ratio inspirals

Extreme mass-ratio inspirals pose a difficult challenge in terms of both search and parameter estimation for upcoming space-based gravitational-wave detectors such as LISA. Their signals are long and of complex morphology, meaning they carry a large amount of information about their source, but their waveforms are expensive to compute and they occupy a vast and multi-modal parameter space. We explore how sequential simulation-based inference methods, specifically truncated marginal neural ratio estimation, could offer solutions to some of the challenges surrounding extreme-mass-ratio inspiral data analysis. We show that this method can efficiently narrow down the volume of the complex 11-dimensional search parameter space by a factor of $10^6-10^7$ and provide 1-dimensional marginal proposal distributions for non-spinning extreme-mass-ratio inspirals. We discuss the current limitations of this approach and place it in the broader context of a global strategy for future space-based gravitational-wave data analysis.

gr-qc↗

arXiv2026

Systematic errors in fast relativistic waveforms for Extreme Mass Ratio Inspirals

Accurate modeling of \gls{EMRIs} is essential for extracting reliable information from future space-based gravitational wave observatories. Fast waveform generation frameworks adopt an offline/online architecture, where expensive relativistic computations (e.g. self-force and black hole perturbation theory) are performed offline, and waveforms are generated rapidly online via interpolation across a multidimensional parameter space. In this work, we investigate potential sources of error that result in systematic bias in these relativistic waveform models, focusing on radiation-reaction fluxes. Two key sources of systematics are identified: (i) the intrinsic inaccuracy of the flux data, for which we focus on the truncation of the multipolar mode sum, and (ii) interpolation errors from transitioning to the online stage. We quantify the impact of mode-sum truncation and analyze interpolation errors by using various grid structures and interpolation schemes. For circular orbits in Kerr spacetime with spins larger than $a \geq 0.9$, we find that $\ell_{\text{max}} \geq 30$ is required for the necessary accuracy. We also develop an efficient Chebyshev interpolation scheme, achieving the desired accuracy level with significantly fewer grid points compared to spline-based methods. For circular orbits in Kerr spacetimes, we demonstrate via Bayesian studies that interpolating the flux to a maximum global relative error that is equal to the small mass ratio is sufficient for parameter estimation purposes. For 4-year long quasi-circular EMRI signals with SNRs $= \mathcal{O}(100)$ and mass-ratios $10^{-4}-10^{-6}$, a global relative error of $10^{-6}$ yields mismatches $<10^{-3}$ and negligible parameter estimation biases.

gr-qc↗

arXiv2026

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

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 analysis challenge due to the signals' complexity and length. We present a semi-coherent, time-frequency search strategy for detecting EMRI harmonics without relying on full waveform templates. We perform an injection and search campaign of single mildly-eccentric equatorial EMRIs in stationary Gaussian noise. The detection statistic is constructed solely from the EMRI frequency evolution, which is modeled phenomenologically using a Singular Value Decomposition basis. The pipeline and the detection statistic are implemented in time-frequency, enabling efficient searches over one year of data in approximately one hour on a single GPU. The search pipeline achieves 94% detection probability at $\mathrm{SNR} = 30$ for a false-alarm probability of $10^{-2}$, recovering the frequency evolution of the dominant harmonic to 1% relative error. By mapping the EMRI parameters consistent with the recovered frequency evolution, we show that the semi-coherent detection statistic enables a sub-percent precision estimation of the EMRI intrinsic parameters. These results establish a computationally efficient framework for constructing EMRI proposals for the LISA global fit.

gr-qc↗

arXiv2026

Probing fundamental physics with Extreme Mass Ratio Inspirals: a full Bayesian inference for scalar charge

Extreme Mass Ratio Inspirals (EMRIs) are key sources for the future space-based gravitational wave detector LISA, and are considered promising probes of fundamental physics. Here, we present the first complete Bayesian analysis of EMRI signals in theories with an additional massless scalar, which could arise in an extension of General Relativity or of the Standard Model of Particle Physics. We develop a waveform model accurate at adiabatic order for equatorial eccentric orbits around spinning black holes. Using full Bayesian inference, we forecast LISA's ability to probe the presence of new fundamental fields with EMRI observations.

gr-qc↗

arXiv2025

SlotFlow: Amortized Trans-Dimensional Inference with Slot-Based Normalizing Flows

Inferring the number of distinct components contributing to an observation, while simultaneously estimating their parameters, remains a long-standing challenge across signal processing, astrophysics, and neuroscience. Classical trans-dimensional Bayesian methods such as Reversible Jump Markov Chain Monte Carlo (RJMCMC) provide asymptotically exact inference but can be computationally expensive. Instead, modern deep learning provides a faster alternative to inference but typically assume fixed component counts, sidestepping the core challenge of trans-dimensionality. To address this, we introduce SlotFlow, a deep learning architecture for trans-dimensional amortized inference. The architecture processes time-series observations, which we represent jointly in the frequency and time domains through parallel encoders. A classifier produces a distribution over component counts K, and its MAP estimate specifies the number of slots instantiated. Each slot is parameterized by a shared conditional normalizing flow trained via permutation-invariant Hungarian matching. On sinusoidal decomposition with up to 10 overlapping components and Gaussian noise, SlotFlow achieves 99.85% cardinality accuracy and well-calibrated parameter posteriors, with systematic biases well below one posterior standard deviation. Direct comparison with RJMCMC shows close agreement in amplitude and phase, with Wasserstein distances $W_2 < 0.01$ and $< 0.03$, indicating that shared global context captures inter-component structure despite a factorized posterior. Frequency posteriors remain centered but exhibit 2-3x broader intervals, consistent with an encoder bottleneck in retaining long-baseline phase coherence. The method delivers a $\sim 10^6\times$ speedup over RJMCMC, suggesting applicability to time-critical workflows in gravitational-wave astronomy, neural spike sorting, and object-centric vision.

astro-ph.IM↗

arXiv2025

The Fast and the Frame-Dragging: Efficient waveforms for asymmetric-mass eccentric equatorial inspirals into rapidly-spinning black holes

Observations of gravitational-wave signals emitted by compact binary inspirals provide unique insights into their properties, but their analysis requires accurate and efficient waveform models. Intermediate- and extreme-mass-ratio inspirals (I/EMRIs), with mass ratios $q \gtrsim 10^2$, are promising sources for future detectors such as the Laser Interferometer Space Antenna (LISA). Modelling waveforms for these asymmetric-mass binaries is challenging, entailing the tracking of many harmonic modes over thousands to millions of cycles. The FastEMRIWaveforms (FEW) modelling framework addresses this need, leveraging precomputation of mode data and interpolation to rapidly compute adiabatic waveforms for eccentric inspirals into zero-spin black holes. In this work, we extend FEW to model eccentric equatorial inspirals into black holes with spin magnitudes $|a| \leq 0.999$. Our model supports eccentricities $e < 0.9$ and semi-latus recta $p < 200$, enabling the generation of long-duration IMRI waveforms, and produces waveforms in $\sim 100$ ms with hardware acceleration. Characterising systematic errors, we estimate that our model attains mismatches of $\sim 10^{-5}$ (for LISA sensitivity) with respect to error-free adiabatic waveforms over most of parameter space. We find that kludge models introduce errors in signal-to-noise ratios (SNRs) as great as $^{+60\%}_{-40\%}$ and induce marginal biases of up to $\sim 1σ$ in parameter estimation. We show LISA's horizon redshift for I/EMRI signals varies significantly with $a$, reaching a redshift of $3$ ($15$) for EMRIs (IMRIs) with only minor $(\sim10\%)$ dependence on $e$ for an SNR threshold of 20. For signals with SNR $\sim 50$, spin and eccentricity-at-plunge are measured with uncertainties of $δa \sim 10^{-7}$ and $δe_f \sim 10^{-5}$. This work advances the state-of-the-art in waveform generation for asymmetric-mass binaries.

gr-qc↗

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

Rapid Construction of Joint Pulsar Timing Array Datasets: The Lite Method

The International Pulsar Timing Array (IPTA)'s second data release (IPTA DR2) combines decades of observations of 65 millisecond pulsars from 7 radio telescopes. IPTA datasets should be the most sensitive datasets to nanohertz gravitational waves (GWs), but take years to assemble, often excluding valuable recent data. To address this, we introduce the IPTA "Lite" analysis, where a Figure of Merit is used to select an optimal PTA dataset to analyze for each pulsar, enabling immediate access to new data and preliminary results prior to full combination. We test the capabilities of the Lite analysis using IPTA DR2, finding that "DR2 Lite" can be used to detect the common red noise process with an amplitude of $A = 4.8^{+1.8}_{-1.8} \times 10^{-15}$ at $γ= 13/3$. This amplitude is slightly large in comparison to the combined analysis, and likely biased high as DR2 Lite is more sensitive to systematic errors from individual pulsars than the full dataset. Furthermore, although there is no strong evidence for Hellings-Downs correlations in IPTA DR2, we still find the full dataset is better at resolving Hellings-Downs correlations than DR2 Lite. Alongside the Lite analysis, we also find that analyzing a subset of pulsars from IPTA DR2, available at a hypothetical "early" stage of combination (EDR2), yields equally competitive results as the full dataset. Looking ahead, the Lite method will enable rapid synthesis of the latest PTA data, offering preliminary GW constraints before the superior full dataset combinations are available.

astro-ph.HE↗

arXiv2025

Dissecting the nanoHz gravitational wave sky: frequency-correlated anisotropy induced by eccentric supermassive black hole binaries

Revealing the nature of the nanoHz gravitational wave (GW) signal recently reported by Pulsar Timing Arrays (PTAs) collaborations around the world is the next goal of low-frequency GW astronomy. The signal likely originates from the incoherent superposition of GWs emitted by a cosmological population of supermassive black hole binaries (SMBHBs). Those binaries can be highly eccentric and/or strongly coupled to their nuclear environment, resulting in an attenuation of the overall GW signal at low frequencies. In this paper, we propose to use the correlation properties of the distributed GW power in the sky across the frequency spectrum as a smoking gun for eccentric SMBHBs thus allowing to break the spectral degeneracy between eccentricity and environmental effect. The simple underlying idea is that, contrary to circular binaries, eccentric ones emit a broadband spectrum thus resulting in similar sky maps at different frequencies. We first demonstrate the applicability of this simple concept on sky maps constructed directly from the theoretical sky distribution of the GWB power induced by realistic populations of SMBHBs. We then demonstrate the viability of this analysis on simulated SKA-like PTA data. By statistically comparing sky maps reconstructed from hundreds injected circular and highly eccentric SMBHB populations, we find that eccentricity can be detected at $3σ$ in more than $50\%$ of cases.

astro-ph.GA↗

arXiv2025

The implications of stochastic gas torques for asymmetric binaries in the LISA band

Gravitational waves from asymmetric mass-ratio black-hole binaries carry unique information about their astrophysical environment. For instance, the Laser Interferometer Space Antenna (LISA) could potentially measure the amplitude and slope of gas torques in binaries embedded in the accretion disks of Active Galactic Nuclei, helping differentiate competing accretion disk models. However, this relies on simplified analytic models, which do not account for the stochastic variability of torques seen in hydrodynamic simulations. In this work, we use hydrodynamic simulations to create gravitational waveforms for extreme and intermediate mass-ratio inspirals in the LISA band. We then analyze these simulated waveforms using simpler templates that assume analytic torques, without stochastic time variability. By performing realistic Bayesian parameter estimation, we find no bias at 90% confidence in the binary parameters; however, estimates of accretion disk parameters, such as torque amplitude and slope, may be biased. Typically, the posterior distribution is centered around the average value of the torques, but when stochastic variability is large, the posterior can indicate no torques, even though they are present in the simulation. Our results suggest that while simplified analytic torque models work well for estimating binary parameters, caution is needed when using them to infer properties of the accretion disk. This work moves towards a more realistic assessment of one of the LISA science objectives, i.e., probing the properties of the astrophysical environments of black holes.

gr-qc↗

arXiv2025

Searching for extreme mass ratio inspirals in LISA: from identification to parameter estimation

The Laser Interferometer Space Antenna (LISA) is a planned space-based observatory designed to detect gravitational waves (GWs) within the millihertz frequency range. LISA is anticipated to observe the inspiral of compact objects into black holes at the centers of galaxies, so called extreme-mass-ratio inspirals (EMRIs). However, the extraction of these long-lived complex signals is challenging due to the large size and multimodality of the search space. In this study, we introduce a new search strategy that allows us to find EMRI signals in noisy data from wide priors all the way to performing parameter estimation. This work is an important step in understanding how to extract EMRIs from future LISA data.

gr-qc↗

arXiv2025

Impact of relativistic waveforms in LISA's science objectives with extreme-mass-ratio inspirals

Extreme-Mass-Ratio Inspirals (EMRIs) are one of the key targets for future space-based gravitational wave detectors, such as LISA. The scientific potential of these sources can only be fully realized with fast and accurate waveform models. In this work, we extend the \textsc{FastEMRIWaveform} (\texttt{FEW}) framework by providing fully relativistic waveforms at adiabatic order for circular, equatorial orbits in Kerr spacetime, for mass ratios up to $10^{-3}$. We investigate the significance of including relativistic corrections in the waveform for both vacuum and non-vacuum environments. Specifically, we develop relativistic non-vacuum EMRI waveforms including two different environmental effects in the EMRI waveforms: power-law migration torques, and superradiance scalar clouds. For EMRIs in vacuum, we find that non-relativistic waveforms incorrectly estimate the predicted source's horizon redshift by approximately $35\%$ error. Our analysis shows that incorporating relativistic corrections enhances constraints on accretion disks, modeled through power-law torques, and improves the constraints on disk parameter estimates (error $\simeq 8\%$), representing a significant improvement over previous estimates. Additionally, we assess the evidence for models in a scenario where ignoring the accretion disk biases the parameter estimation (PE), reporting a $\log_{10}$ Bayes factor of $1.1$ in favor of the accretion disk model. In a fully relativistic setup, we also estimate the parameters of superradiant scalar clouds with relative errors $\simeq 0.3\%$ for the scalar cloud's mass. These results demonstrate that incorporating relativistic effects is essential for LISA science objectives with EMRIs.

gr-qc↗

arXiv2025

Constraining accretion physics with gravitational waves from eccentric extreme-mass-ratio inspirals

We study the evolution of eccentric, equatorial extreme-mass-ratio inspirals (EMRIs) immersed in the accretion disks of active galactic nuclei. We find that single gravitational-wave observations from these systems could provide measurements with ~ 10 % relative precision of, simultaneously, the disk viscosity and mass accretion rate of the central supermassive black hole. This is possible when the EMRI transitions, within the observation time, from supersonic to subsonic motion relative to the disk gas, for eccentricities e > ~ 0.025-0.1. The estimate of the accretion rate would assist in the identification of the EMRI's host galaxy, or the observation of a direct electromagnetic counterpart, improving the chances of using these sources as cosmological sirens. Our work highlights the rich phenomenology of binary evolution in astrophysical environments and the need to improve the modelling and analysis of these systems for future gravitational-wave astronomy.

gr-qc↗

arXiv2024

Systematics in tests of general relativity using LISA massive black hole binaries

Our current understanding is that an environment - mainly consisting of gas or stars - is required to bring massive black hole binaries (MBHBs) with total redshifted mass $M_z\sim[10^{4},10^7]~{\rm M}_\odot$ to the LISA band from parsec separation. Even in the gravitational wave (GW) dominated final inspiral, realistic environments can non-negligibly speed up or slow down the binary evolution, or leave residual, measurable eccentricity in the LISA band. Despite this fact, most of the literature does not consider environmental effects or orbital eccentricity in modelling GWs from near-equal mass MBHBs. Considering either a circular MBHB embedded in a circumbinary disc or a vacuum eccentric binary, we explore if ignoring either secular gas effects (migration and accretion) or eccentric corrections to the GW waveform can mimic a failure of General Relativity (GR). We use inspiral-only aligned-spin 3.5 post-Newtonian waveforms, a complete LISA response model, and Bayesian inference to perform a parameterized test of GR. For a four-year LISA observation of an MBHB with $M_z=10^{5}~{\rm M}_\odot$, primary-to-secondary mass ratio $q=8$, and component BHs' dimensionless spins $χ_{1,2}=0.9$ at redshift $z=1$, even a moderate gas-disc imprint (Eddington ratio ${\rm f}_{\rm Edd}\sim0.1$) or low initial eccentricity ($e_0\sim10^{-2.5}$) causes a false violation of GR in several PN orders. However, correctly modelling either effect can mitigate systematics while avoiding significant biases in vacuum circular systems. The adoption of LISA makes it urgent to consider gas imprints and eccentricity in waveform models to ensure accurate inference for MBHBs.

astro-ph.GA↗

arXiv2024

Impact of Correlations on the Modeling and Inference of Beyond Vacuum-GR Effects in Extreme-Mass-Ratio Inspirals

In gravitational-wave astronomy, extreme-mass-ratio-inspiral (EMRI) sources for the upcoming LISA observatory have the potential to serve as high-precision probes of astrophysical environments in galactic nuclei, and of potential deviations from general relativity (GR). Such ``beyond vacuum-GR'' effects are often modeled as perturbations to the evolution of vacuum EMRIs under GR. Previous studies have reported unprecedented constraints on these effects by examining the inference of one effect at a time. However, a more realistic analysis would require the simultaneous inference of multiple beyond vacuum-GR effects. The parameters describing such effects are generally significantly correlated with each other and the vacuum EMRI parameters. We explicitly show how these correlations remain even if any modeled effect is absent in the actual signal, and how they cause inference bias when any effect in the signal is absent in the analysis model. This worsens the overall measurability of the whole parameter set, challenging the constraints found by previous studies, and posing a general problem for the modeling and inference of beyond vacuum-GR effects in EMRIs.

gr-qc↗