arXiv subjects

Massimo Meneghetti

Publications and source records attributed to Massimo Meneghetti.

At least 19 recordsLinked to original sources

arXiv2026

Host Dependence and Line-of-Sight Effects on Galaxy-Galaxy Strong Lensing in Clusters

The cross section for galaxy-galaxy strong lensing (GGSL) events in galaxy clusters has repeatedly been found to be higher in observations than in cosmological simulations. We revisit this discrepancy using updated simulation methodology and investigate the dependence of the GGSL probability, $P_{\rm GGSL}$, on host-cluster lensing properties, baryonic physics, and correlated and uncorrelated line-of-sight structure. We find that correlated material within $\sim 35$ cMpc of the cluster along the line of sight enhances $P_{\rm GGSL}$ by a few percent for typical systems and by up to $\sim15\%$ for the most efficient lenses. At fixed cluster mass, dark-matter-only simulations yield GGSL probabilities up to an order of magnitude lower than hydrodynamical simulations. We also find a strong dependence on the host-cluster Einstein radius, with an approximate scaling $P_{\rm GGSL} \propto \theta_{\rm E}^{2}$. Matching simulated and observed clusters in both mass and Einstein radius seems to reduce the discrepancy relative to previous comparisons. However, our analysis does not clearly resolve the GGSL discrepancy, as the inferred tension depends strongly on the field-of-view definition: square fields are approximately consistent with simulations, while cluster member-bounded fields yield observed probabilities a factor of $\sim 2-3$ higher. Until observations and simulations share a matched selection function and matched measurement methodology, the residual tension cannot be cleanly attributed to either astrophysics or cosmology.

astro-ph.CO↗

arXiv2026

Imprints of Mass Accretion History on Galaxy Cluster Morphology

Variations in dynamical states of galaxy clusters can introduce biases and scatter in observable-mass relations. The dynamical state of a cluster is an emergent feature of its mass accretion history (MAH), it is therefore useful to constrain the MAH of the cluster. In this work, we characterize 305 massive clusters from The300 project by connecting features from their projected stellar distributions to their mass accretion histories (MAH). As a baseline, we first correlate host dark matter halo dynamical state indicators at $z=0$ with their MAH via the Spearman rank correlation coefficient $\rho_{\mathrm{sp}}$. Both substructure mass fraction and center-of-mass offset measurements correlate strongly with the MAH measured between $0.1\lesssim z\lesssim 1$. We repeat this exercise with morphological measurements of projected stellar density maps, many of which exhibit moderate correlation strength with different times in the MAH. Broadly, core morphological measurements ($r \leq 30\,\mathrm{kpc}$) correlate better with early-time MAH. Core-excised ($50\,\mathrm{kpc} \leq r \leq 1\,\mathrm{Mpc}$) morphological measurements correlate better with late-time MAH. We further quantify the MAH prediction power of both traditional dynamical state indicators and morphological parameters using Multivariable Conditional Abundance Matching (MultiCAM). MultiCAM employs simple rank-ordering operations, making it straightforward to translate to observed datasets. We find reasonable ($\rho_{\mathrm{sp}} \geq 0.6$) performance for predictions of the mass fraction between $1\lesssim z\lesssim 0.1$, though with notable information loss when using projected quantities. In one example application of our methodology, we use the coefficients of the MultiCAM models to select subsamples of galaxy clusters that have accreted more (or less) of their $z = 0$ mass budget over a given time frame.

astro-ph.CO↗

arXiv2026

Dynamical models of cluster members to probe the total mass properties of cluster subhalos. I. A comparison with parametric strong lensing models

In this series of papers, we present dynamical models of cluster members in strong lensing (SL) galaxy clusters to independently probe the persistent discrepancy reported between SL models and cosmological simulations, in terms of total mass properties for the cluster subhalos. In this work, we focused our study on early-type galaxies within Abell 2744 ($z=0.309$) and MACS J0416.1-2403 ($z=0.397$). We took advantage of deep MUSE spectroscopic data, complemented with HFF photometry. We used a pipeline based on spectral fitting to perform kinematic measurements of the LOS velocity dispersion profiles of 109 cluster members. We modeled the galaxies assuming a dPIE total mass density distribution and a Jaffe stellar mass density distribution. From the models, we inferred the values of the central stellar velocity dispersion, $\sigma_0$, and the truncation radius, $r_t$, for the galaxies in our sample. We found that $\sigma_0$ is accurately recovered for all of the cluster members, while $r_t$ is reliably measured for a fraction of galaxies in our sample, with sufficiently extended radial kinematic coverage. Our dynamical models predicted LOS velocity dispersion profiles that fit the measured ones better than those inferred from SL models. We then exploited the $\sigma_0$ measurements obtained from the dynamical models to calibrate the Faber-Jackson scaling relations for the cluster members in both galaxy clusters. When comparing our relations to those obtained in previous kinematics and SL works, we found systematically higher normalization and compatible slope and scatter values. We conclude that our dynamical measurements of $\sigma_0$ and $r_t$, along with calibrated scaling relations, are more robust than previous kinematic estimates which are biased by not taking into account the effects of the PSF, and should therefore be adopted as improved initial prescriptions in future SL models.

astro-ph.GA↗

arXiv2026

A Consistent Implementation of Cluster Strong Lensing in Cosmological Simulation Light Cones

Galaxy cluster strong gravitational lensing plays a central role in precision cosmology, yet robust theoretical predictions have lagged behind an abundance of high-quality strong lensing observations. This shortfall reflects both a mismatch between the geometry of the strong-lensing problem and standard cubic simulation boxes, and the fundamental tension between simulation volume and resolution. Consequently, many current forecasts adopt hybrid approaches that extract individual lenses from simulations and combine them with analytic or observed source populations positioned near caustics. These methods often omit correlated and/or uncorrelated line-of-sight (LoS) structure, or include it in ways that do not preserve correlations across redshift. Here we present a fully simulation-based procedure that generates strong-lensing images directly from particle data, drawing the lens, source, and all intervening resolved objects self-consistently from the simulated large-scale structure. Our approach combines a structure-preserving remapping of the simulation volume into a lensing-appropriate geometry with multi-plane ray tracing, enabling the use of uniform simulation boxes that resolve both cluster-scale primary lenses and high-redshift source galaxies. We demonstrate the method by generating example light cones and images using IllustrisTNG data, then use these results to conservatively quantify the impact of LoS structure on image configurations and critical-curve morphology. We find that uncorrelated LoS structure can shift the relative positions of lensed images by several arcseconds, introduces a $\sim 6\%$ scatter in the area of a cluster's primary critical curve, and changes the total critical area within 100$^{\prime\prime}$ of the cluster potential minimum by $16^{+20\%}_{-14\%}$ at a source plane redshift of $z_s=4$.

astro-ph.CO↗

arXiv2026

A Quantum Genetic Algorithm with application to Cosmological Parameters Estimation

An Amplitude-Encoded Quantum Genetic Algorithm (AEQGA) has been developed to minimize $\chi^2$ functions of different cosmological probes (Supernovae Type Ia, Baryon Acoustic Oscillations, Cosmic Microwave Background Radiation), to find the best-fit value for two cosmological parameters, namely the Hubble Constant and the density matter content of the Universe today. Our main aim is to pave the way to testing the adoption of quantum optimization in the inference of the cosmological parameters that describe the universe evolution. AEQGA computes the merit function classically, and then uses a quantum circuit to entangle the population and perform crossover and mutation operations. The results show consistency with the isocontours of the objective functions. We then tested the general behavior of AEQGA as a function of its hyperparameters and compared it with a second quantum genetic algorithm found in the literature as well as with classical algorithms, finding consistent results.

astro-ph.CO↗

arXiv2025

The AIDA-TNG project: dark matter profiles and concentrations in alternative dark matter models

In the standard Cold Dark Matter (CDM) scenario, the density profiles of dark matter haloes are well described by analytical models linking their concentration to halo mass. Alternative scenarios, such as warm dark matter (WDM) and self-interacting dark matter (SIDM), modify the inner structure of haloes and predict different profile shapes and central slopes. We employ the AIDA-TNG simulations to investigate how alternative dark matter physics and baryonic processes jointly shape the internal structure of haloes. Using dark-matter-only and full-physics runs, we measure the dark matter density profiles of haloes spanning six orders of magnitude in mass, from 10^9.5 Msun to 10^14.5 Msub, and characterise them with multiple analytical models. We provide the distribution of the best-fitting parameters, as well as the concentration-mass relation in WDM and SIDM. The Einasto profile well reproduces the inner flattening produced in WDM models, both in the collisionless and in the full-physics runs. In SIDM dark-matter-only runs, haloes are better described by explicitly cored profiles, with core sizes that depend on mass and on the self-interaction model. When baryons are included, the differences between CDM and SIDM decrease, and such large dark-matter cores no longer form because adiabatic contraction in the baryon-dominated region counteracts self-interactions. Nevertheless, the coupling between baryons and self-interactions induces a broader range of inner slopes, including cases that are steeper than CDM at Milky Way masses. Alternative dark matter physics thus leaves clear signatures in the inner halo structure, even if baryons significantly reshape these differences. Our results are useful for future studies that need to predict the properties of haloes in multiple dark matter models.

astro-ph.CO↗

arXiv2025

Image simulations of highly magnified clumpy galaxies

We present ClumPyLen, a Python-based simulator designed to produce realistic mock observations of strongly lensed, high-redshift, clumpy star-forming galaxies. The tool models galaxy components such as disks, bulges, and spiral arms using S\'ersic profiles, and it populates them with stellar clumps whose properties are sampled from physically motivated distributions. ClumPyLen includes the effects of gravitational lensing through user-provided deflection angle maps and simulates realistic observational conditions by accounting for instrumental effects, Point-Spread-Function convolution, sky background, and photon noise. The simulator can support a wide range of filters and instruments; here we focus on HST/ACS, HST/WFC3-IR, and JWST/NIRCam. We demonstrate the capabilities of the code through two examples, including a detailed simulation of the z = 6.145 source Cosmic Archipelago lensed by MACS J0416.1-2403. The simulated images closely match the morphology and limiting magnitudes of real observations. ClumPyLen is designed to explore the detectability of stellar clumps in terms of mass and size, especially in the low-mass regime, and it allows the study of clump blending effects. Thanks to its modular design, the code is highly adaptable to a wide range of scientific goals, including lensing studies, galaxy evolution, and the generation of synthetic datasets for machine learning or forward modeling applications.

astro-ph.GA↗

arXiv2025

Addressing prior dependence in hierarchical Bayesian modeling for PTA data analysis I: Methodology and implementation

Complex inference tasks, such as those encountered in Pulsar Timing Array (PTA) data analysis, rely on Bayesian frameworks. The high-dimensional parameter space and the strong interdependencies among astrophysical, pulsar noise, and nuisance parameters introduce significant challenges for efficient learning and robust inference. These challenges are emblematic of broader issues in decision science, where model over-parameterization and prior sensitivity can compromise both computational tractability and the reliability of the results. We address these issues in the framework of hierarchical Bayesian modeling by introducing a reparameterization strategy. Our approach employs Normalizing Flows (NFs) to decorrelate the parameters governing hierarchical priors from those of astrophysical interest. The use of NF-based mappings provides both the flexibility to realize the reparametrization and the tractability to preserve proper probability densities. We further adopt i-nessai, a flow-guided nested sampler, to accelerate exploration of complex posteriors. This unified use of NFs improves statistical robustness and computational efficiency, providing a principled methodology for addressing hierarchical Bayesian inference in PTA analysis.

astro-ph.IM↗

arXiv2025

Addressing prior dependence in hierarchical Bayesian modeling for PTA data analysis II: Noise and SGWB inference through parameter decorrelation

Pulsar Timing Arrays (PTA) provide a powerful framework to measure low-frequency gravitational waves, but accuracy and robustness of the results are challenged by complex noise processes that must be accurately modeled. Standard PTA analyses assign fixed uniform noise priors to each pulsar, an approach that can introduce systematic biases when combining the array. To overcome this limitation, we adopt a hierarchical Bayesian modeling strategy in which noise priors are parametrized by higher-level hyperparameters. To mitigate the sensitivity of the inferred parameters to the choice of noise hyperprior, we introduce a reparametrization of the hierarchical model based on the orthogonal projection of hyperparameters onto the physical parameter subspace. The transformation is implemented through Normalizing Flows (NFs), which provide an invertible, tractable representation and preserve shrinkage and inter-pulsar information pooling in the reparametrized model. We also employ i-nessai, a flow-guided nested sampler, to efficiently explore the resulting higher-dimensional parameter space. We apply our method to a minimal 3-pulsar case study, performing a simultaneous inference of noise and stochastic gravitational wave background (SGWB) parameters. Despite the limited dataset, the results consistently show that the reparametrized hierarchical treatment constrains the noise parameters more tightly and partially alleviates the red-noise-SGWB degeneracy, while the orthogonal reparametrization further enhances parameter independence without affecting the correlations intrinsic to the power-law modeling of the physical processes involved.

astro-ph.IM↗

arXiv2025

Quantum Markov Chain Monte Carlo for Cosmological Functions

We present an implementation of Quantum Computing for a Markov Chain Monte Carlo method with an application to cosmological functions, to derive posterior distributions from cosmological probes. The algorithm proposes new steps in the parameter space via a quantum circuit whose resulting statevector provides the components of the shift vector. The proposed point is accepted or rejected via the classical Metropolis-Hastings acceptance method. The advantage of this hybrid quantum approach is that the step size and direction change in a way independent of the evolution of the chain, thus ideally avoiding the presence of local minima. The results are consistent with analyses performed with classical methods, both for a test function and real cosmological data. The final goal is to generalize this algorithm to test its application to complex cosmological computations.

astro-ph.CO↗

arXiv2025

The Application of Quantum Fourier Transform in Cosmic Microwave Background Data Analysis

The Cosmic Microwave Background (CMB) data analysis and the map-making process rely heavily on the use of spherical harmonics. For suitable pixelizations of the sphere, the (forward and inverse) Fourier transform plays a crucial role in computing all-sky map from spherical harmonic expansion coefficients -- or from angular power spectrum -- and vice versa. While the Fast Fourier Transform (FFT) is traditionally employed in these computations, the Quantum Fourier Transform (QFT) offers a theoretical advantage in terms of computational efficiency and potential speedup. In this work, we study the potential advantage of using the QFT in this context by exploring the substitution of the FFT with the QFT within the \textit{healpy} package. Performance evaluations are conducted using the Aer simulator. Our results indicate that QFT exhibits potential advantages over FFT that are particularly relevant at high-resolution. However, classical-to-quantum data encoding overhead represents a limitation to current efficiency. In this work, we adopted amplitude encoding, due to its efficiency on encoding maximum data to minimum number of qubits. We identify data encoding as a potential significant bottleneck and discuss its impact on quantum speedup. Future improvements in quantum encoding strategies and algorithmic optimizations could further enhance the feasibility of QFT in CMB data analysis.

astro-ph.IM↗

arXiv2025

Numerical limits in the integration of Vlasov-Poisson equation for Cold Dark Matter

The Vlasov-Poisson systems of equations (VP) describes the evolution of a distribution of collisionless particles under the effect of a collective-field potential. VP is at the basis of the study of the gravitational instability of cosmological density perturbations in Dark-Matter (DM), but its range of application extends to other fields, such as plasma physics. In the case of Cold Dark Matter, a single velocity is associated with each fluid-element (or particle) , the initial condition presents a stiff discontinuity. This creates problems such as diffusion or negative distribution function when a grid based method is used to solve VP. In this work we want to highlight this problem, focusing on the technical aspects of this phenomenon. By comparing different finite volume methods and a spectral method we observe that, while all integration schemes preserve the invariants of the system (e.g, energy), the physical observable of interest, i.e., the density, is not correctly reproduced. We thus compare the density obtained with the different Eulerian integration schemes with the result obtained from a reference N-body method. We point out that the most suitable method to solve the VP system for a self-gravitating system is a spectral method.

physics.comp-ph↗

arXiv2025

Enhanced strong-lensing model of MACS~J0138.0$-$2155 based on new JWST and VLT/MUSE observations

We present a new parametric strong lensing analysis of the galaxy cluster MACS J0138.0-2155 at z = 0.336, the first known to show two multiply-imaged supernova (SN) siblings, SN Requiem and SN Encore at z= 1.949. We exploit HST and JWST multiband imaging in synergy with new MUSE spectroscopy to develop an improved lens mass model. We include 84 cluster members (of which ~60% are spectroscopically confirmed) and two perturber galaxies along the line of sight. Our observables consist of 23 spectroscopically confirmed multiple images from 8 background sources, spanning a fairly wide redshift range, from 0.767 to 3.420. To accurately characterise the sub-halo mass component, we calibrate the Faber-Jackson scaling relation based on the stellar kinematics of 14 bright cluster galaxies. We build several lens models, with different cluster total mass parametrisations, to assess the statistical and systematic uncertainties on the predicted values of the position and magnification of the observed and future multiple images of SN Requiem and SN Encore. Our reference best-fit lens model reproduces the observed positions of the multiple images with a root-mean-square offset of 0".36, and the multiple image positions of the SNe and their host galaxy with a remarkable mean precision of only 0".05. We measure a projected total mass of $M(<60~ \rm kpc) = 2.89_{-0.03}^{+0.04} \times 10^{13} M_{\odot}$, consistent with that independently derived from the Chandra X-ray analysis. We demonstrate the reliability of the new lens model by reconstructing the extended surface-brightness distribution of the multiple images of the host galaxy. The discrepancy between our model-predicted magnification values with those from previous studies, critical for understanding the intrinsic properties of the SNe and their host galaxy, underscores the need to combine cutting-edge observations with detailed lens modelling.

astro-ph.GA↗

arXiv2025

Benchmarking Quantum Convolutional Neural Networks for Signal Classification in Simulated Gamma-Ray Burst Detection

This study evaluates the use of Quantum Convolutional Neural Networks (QCNNs) for identifying signals resembling Gamma-Ray Bursts (GRBs) within simulated astrophysical datasets in the form of light curves. The task addressed here focuses on distinguishing GRB-like signals from background noise in simulated Cherenkov Telescope Array Observatory (CTAO) data, the next-generation astrophysical observatory for very high-energy gamma-ray science. QCNNs, a quantum counterpart of classical Convolutional Neural Networks (CNNs), leverage quantum principles to process and analyze high-dimensional data efficiently. We implemented a hybrid quantum-classical machine learning technique using the Qiskit framework, with the QCNNs trained on a quantum simulator. Several QCNN architectures were tested, employing different encoding methods such as Data Reuploading and Amplitude encoding. Key findings include that QCNNs achieved accuracy comparable to classical CNNs, often surpassing 90\%, while using fewer parameters, potentially leading to more efficient models in terms of computational resources. A benchmark study further examined how hyperparameters like the number of qubits and encoding methods affected performance, with more qubits and advanced encoding methods generally enhancing accuracy but increasing complexity. QCNNs showed robust performance on time-series datasets, successfully detecting GRB signals with high precision. The research is a pioneering effort in applying QCNNs to astrophysics, offering insights into their potential and limitations. This work sets the stage for future investigations to fully realize the advantages of QCNNs in astrophysical data analysis.

astro-ph.HE↗

arXiv2025

The Three Hundred Project hydrodynamical simulations: Hydrodynamical weak-lensing cluster mass biases and richnesses using different hydro models

The mass of galaxy clusters estimated from weak-lensing observations is affected by projection effects, leading to a systematic underestimation compared to the true cluster mass, varying with both mass and redshift. The magnitude depends on the criteria used to select clusters and the spatial scale over which their mass is measured. We leverage hydrodynamical simulations of galaxy clusters carried out with GadgetX and GIZMO-SIMBA as part of the Three Hundred project. We used them to quantify weak-lensing mass biases with respect also to the results from dark matter-only simulations. We also investigate how the biases propagate into the richness-mass relation. We aim to shed light on the effect of the presence of baryons on the weak-lensing mass bias and also whether this bias depends on the galaxy formation recipe; we seek to model the richness-mass relation that can be used as guidelines for observational experiments for cluster cosmology. We produced weak-lensing simulations of random projections to model the expected excess surface mass density profile of clusters up to redshift $z=1$. We then estimated the observed richness by counting the number of galaxies in a cylinder and correcting by projected contaminants. We derived the weak-lensing mass-richness relation and found consistency across hydrodynamical simulations. The intercept parameter of the relation is independent of redshift but varies with the minimum of the stellar mass to define the richness. At the same time, the slope is relatively constant up to $z=0.55$. The scatter in observed richness at a fixed weak-lensing mass increases linearly with redshift at a fixed stellar mass cut. As expected, we observed that the scatter in richness at a given true mass is smaller than at a given weak-lensing mass. Our results for the weak-lensing mass-richness relation align well with SDSS redMaPPer cluster analyses. [Abridged]

astro-ph.CO↗

arXiv2024

Galaxy-galaxy strong lensing cross-section with fuzzy dark matter model

The galaxy-galaxy strong lensing (GGSL) cross-section in observed galaxy clusters has been reported to be more than an order of magnitude higher than the theoretical prediction by the standard cold dark matter (CDM) model. In this study, we focus on the fuzzy dark matter (FDM) model and study the GGSL cross-section numerically and analytically. We find that FDM subhalos can produce larger cross-sections than the CDM subhalos due to the presence of the soliton core. The maximum cross-section is obtained when the core radius is about the same as the size of the critical curve. The peak ratio of the cross-sections between the FDM subhalos and the CDM subhalos is about two when including the baryon distribution, indicating that the FDM with any masses might not produce the expected observed cross-section.

astro-ph.CO↗

arXiv2024

The next step in galaxy cluster strong lensing: modeling the surface brightness of multiply-imaged sources

Overcoming both modeling and computational challenges, we present, for the first time, the extended surface-brightness distribution model of a strongly-lensed source in a complex galaxy-cluster-scale system. We exploit the high-resolution Hubble Space Telescope (HST) imaging and extensive Multi Unit Spectroscopic Explorer spectroscopy to build an extended strong-lensing model, in a full multi-plane formalism, of SDSS J1029+2623, a lens cluster at $z = 0.588$ with three multiple images of a background quasar ($z = 2.1992$). Going beyond typical cluster strong-lensing modeling techniques, we include as observables both the positions of 26 pointlike multiple images from seven background sources, spanning a wide redshift range between 1.02 and 5.06, and the extended surface-brightness distribution of the strongly-lensed quasar host galaxy, over $\sim78000$ HST pixels. In addition, we model the light distribution of seven objects, angularly close to the strongly-lensed quasar host, over $\sim9300$ HST pixels. Our extended lens model reproduces well both the observed intensity and morphology of the quasar host galaxy in the HST F160W band (with a 0''.03 pixel scale). The reconstructed source shows a single, compact, and smooth surface-brightness distribution, for which we estimate an intrinsic magnitude of 23.3 $\pm$ 0.1 in the F160W band and a half-light radius of (2.39 $\pm$ 0.03) kpc. The increased number of observables enables the accurate determination of the total mass of line-of-sight halos lying angularly close to the extended arc. This work paves the way for a new generation of galaxy cluster strong-lens models, where additional, complementary lensing observables are directly incorporated as model constraints.

astro-ph.GA↗

arXiv2024

The galaxy-galaxy strong lensing cross section and the internal distribution of matter in {\Lambda}CDM substructure

Strong gravitational lensing offers a powerful probe of the detailed distribution of matter in lenses, while magnifying and bringing faint background sources into view. Observed strong lensing by massive galaxy clusters, which are often in complex dynamical states, has also been used to map their dark matter substructures on smaller scales. Deep high resolution imaging has revealed the presence of strong lensing events associated with these substructures, namely galaxy-scale sub-halos. However, an inventory of these observed galaxy-galaxy strong lensing (GGSL) events is noted to be discrepant with state-of-the-art {\Lambda}CDM simulations. Cluster sub-halos appear to be over-concentrated compared to their simulated counterparts yielding an order of magnitude higher value of GGSL. In this paper, we explore the possibility of resolving this observed discrepancy by redistributing the mass within observed cluster sub-halos in ways that are consistent within the {\Lambda}CDM paradigm of structure formation. Lensing mass reconstructions from data provide constraints on the mass enclosed within apertures and are agnostic to the detailed mass profile within them. Therefore, as the detailed density profile within cluster sub-halos currently remains unconstrained by data, we are afforded the freedom to redistribute the enclosed mass. We investigate if rearranging the mass to a more centrally concentrated density profile helps alleviate the GGSL discrepancy. We report that refitting cluster sub-halos to the ubiquitous {\Lambda}CDM-motivated Navarro-Frenk-White profile, and further modifying them to include significant baryonic components, does not resolve this tension. A resolution to this persisting GGSL discrepancy may require more careful exploration of alternative dark matter models.

astro-ph.CO↗