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Mario Pasquato

Publications and source records attributed to Mario Pasquato.

At least 19 recordsLinked to original sources

Causes of Hot Jupiter Inflation from Causal Discovery

Hot Jupiters often have radii larger than predicted by standard cooling--contraction models, but it remains unclear which process supplies or preserves the extra internal heat. We analyze 328 short-period giant planets with measured $M_p$, $R_p$, $P_{\rm orb}$, and host-star $T_{\rm eff}$ using causal discovery, a statistical framework that asks which observed properties remain directly connected to planet radius after the others are accounted for. As a check, the same pipeline recovers the expected mass--radius connection for a super-Earth control sample. For hot Jupiters, the preferred graph links $R_p$ directly to $P_{\rm orb}$ and $T_{\rm eff}$, but not to $M_p$. Since incident flux increases with $T_{\rm eff}$ and decreases with $P_{\rm orb}$ at fixed stellar properties, this paired dependence is naturally interpreted as a population-level signature of irradiation-regulated inflation. Comparing the graph with analytic radius-excess scalings suggests a comparatively important role for Gold--Soter thermal tides, with kinetic/mechanical heating and ohmic dissipation potentially contributing alongside them. Purely period-controlled gravitational tides are disfavored as the sole explanation because they lack a leading dependence on stellar temperature. Distinguishing thermal tides, kinetic/mechanical heating, ohmic dissipation, and mixed scenarios will require radius-excess measurements that control for incident flux, age, composition, stellar properties, and selection effects. More broadly, this work shows how causal discovery can turn population-level exoplanet data into physically interpretable tests of hot-Jupiter inflation. Causal discovery complements parametric Bayesian population models by testing which observables retain direct conditional dependence on $R_p$ without imposing a specific radius relation, although the modest sample size limits the scope of the inferred graph.

astro-ph.EP

Causal Reversal in the $M_\unicode{x25CF}\unicode{x2013}\sigma_0$ Relation: Implications for High-Redshift Supermassive Black Hole Mass Estimates

The nascent methodology of applying the principles of causal discovery to astrophysical data has produced affirming results about deeply held theories concerning the causal nature behind the observed coevolution of supermassive black holes (SMBHs) with their host galaxies. The key results from observations have demonstrated an apparent causal reversal across different galaxy morphologies$\unicode{x2014}$SMBHs causally influence the evolution of the physical parameters of their spiral galaxy hosts, whereas SMBHs in elliptical galaxies are passive companions that grow in near lockstep with their hosts. To further explore and ascertain insights, it is necessary to utilize galaxy simulations to track the time evolution of the observed causal relations to learn more about the temporal nature of the changing SMBH/galaxy evolutionary directions. We conducted experiments with the NIHAO suite of cosmological zoom-in hydrodynamical simulations to follow the evolution of individual galaxies along with their central SMBH masses ($M_\unicode{x25CF}$) and properties, including central stellar velocity dispersion ($\sigma_0$). We reproduce the causal results from real galaxies, but add clarity by observing that the SMBH/galaxy causal directions are noticeably inverted between the epochs before and after the peak of star formation. The implications for causal reversal of the $M_\unicode{x25CF}\unicode{x2013}\sigma_0$ relation portend larger concerns about the reliability of SMBH masses estimated at high redshifts and presumptions of overmassive black holes at early epochs. Toward this problem, we apply updated causally-informed scaling relations that predict high-$z$ black hole masses that are approximately two orders of magnitude less massive, and thus not overmassive with respect to local $z=0$ SMBH$\unicode{x2013}$galaxy mass ratios.

astro-ph.GA

Two faces of Gaia-Sausage-Enceladus: Mining the chemical abundance space with graph attention networks

Recent studies suggest that chemical abundances hold the key to disentangling halo substructure, providing a more reliable tracer than dynamics alone. We aim to probe the Milky Way stellar halo using high-dimensional chemical abundances from GALAH DR4. By leveraging multiple nucleosynthesis channels in synergy with integrals of motion (IoM), we extract information hidden in the raw abundance space to perform chemical tagging. With a graph attention autoencoder, we reconstruct a dynamics-informed, denoised chemical space and identify coherent stellar substructures by applying ensemble clustering. Our method successfully recovers the three largest globular clusters hidden in the dataset, estimates the in-situ fraction to be approximately 41\%, and chemically characterizes several dynamical halo substructures. Strikingly, stars dynamically associated with Gaia-Sausage-Enceladus (GSE) separate into two chemically distinct clusters. By examining their abundances, energy ($E$) and angular momentum ($L_z$) distributions, together with the metallicity trend with $E$, we connect these clusters to their birthplace within the progenitor by proposing a simple infall scenario: one cluster traces the metal-poor, less evolved outskirts, while the other traces the metal-rich, chemically evolved core.

astro-ph.GA

Stellar flare detection in XMM-Newton with gradient boosted trees

The EXTraS project, based on data collected with the XMM-Newton observatory, provided us with a vast amount of light curves for X-ray sources. For each light curve, EXTraS also provided us with a set of features (https://extras.inaf.it). We extract from the EXTraS database a tabular dataset of 31,832 variable sources by 108 features. Of these, 13,851 sources were manually labeled as stellar flares or non-flares based on direct visual inspection. We employ a supervised learning approach to produce a catalog of stellar flares based on our dataset, releasing it to the community. We leverage explainable AI tools and interpretable features to better understand our classifier. We train a gradient boosting classifier on 80\% of the data for which labels are available. We compute permutation feature importance scores, visualize feature space using UMAP, and analyze some false positive and false negative data points with the help of Shapley additive explanations -- an AI explainability technique used to measure the importance of each feature in determining the classifier's prediction for each instance. On the test set made up of the remainder 20\% of our labeled data, we obtain an accuracy of 97.1\%, with a precision of 82.4\% and a recall of 73.3\%. Our classifier outperforms a simple criterion based on fitting the light curve with a flare template and significantly surpasses a gradient-boosted classifier trained only on model-independent features. False positives appear related to flaring light curves that are not associated with a stellar counterpart, while false negatives often correspond to multiple flares or otherwise peculiar or noisy curves. We apply our trained classifier to currently unlabeled sources, releasing the largest catalog of X-ray stellar flares to date. [abridged]

astro-ph.HE

The hunt for new pulsating ultraluminous X-ray sources: a clustering approach

The discovery of fast and variable coherent signals in a handful of ultraluminous X-ray sources (ULXs) testifies to the presence of super-Eddington accreting neutron stars, and drastically changed the understanding of the ULX class. Our capability of discovering pulsations in ULXs is limited, among others, by poor statistics. However, catalogues and archives of high-energy missions contain information which can be used to identify new candidate pulsating ULXs (PULXs). The goal of this research is to single out candidate PULXs among those ULXs which have not shown pulsations due to an unfavourable combination of factors. We applied an AI approach to an updated database of ULXs detected by XMM-Newton. We first used an unsupervised clustering algorithm to sort out sources with similar characteristics into two clusters. Then, the sample of known PULX observations has been used to set the separation threshold between the two clusters and to identify the one containing the new candidate PULXs. We found that only a few criteria are needed to assign the membership of an observation to one of the two clusters. The cluster of new candidate PULXs counts 85 unique sources for 355 observations, with $\sim$85% of these new candidates having multiple observations. A preliminary timing analysis found no new pulsations for these candidates. This work presents a sample of new candidate PULXs observed by XMM-Newton, the properties of which are similar (in a multi-dimensional phase space) to those of the known PULXs, despite the absence of pulsations in their light curves. While this result is a clear example of the predictive power of AI-based methods, it also highlights the need for high-statistics observational data to reveal coherent signals from the sources in this sample and thus validate the robustness of the approach.

astro-ph.HE

Chaos in violent relaxation dynamics. Disentangling micro- and macro-chaos in numerical experiments of dissipationless collapse

Violent relaxation (VR) is often regarded as the mechanism leading stellar systems to collisionless meta equilibrium via rapid changes in the collective potential. We investigate the role of chaotic instabilities on single particle orbits in leading to nearly-invariant phase-space distributions, aiming at disentangling it from the chaos induced by collective oscillations in the self-consistent potential. We explore as function of the systems size (i.e. number of particles $N$) the chaoticity in terms of the largest Lyapunov exponent of test trajectories in a simplified model of gravitational cold collapse, mimicking a $N-$body calculation via a time dependent smooth potential and a noise-friction process accounting for the discreteness effects. A new numerical method to evaluate effective Lyapunov exponents for stochastic models is presented and tested. We find that the evolution of the phase-space of independent trajectories reproduces rather well what observed in self-consistent $N-$body simulations of dissipationless collapses. The chaoticity of test orbits rapidly decreases with $N$ for particles that remain weakly bounded in the model potential, while it decreases with different power laws for more bound orbits, consistently with what observed in previous self-consistent $N$-body simulations. The largest Lyapunov exponents of ensembles of orbits starting from initial conditions uniformly sampling the accessible phase-space are somewhat constant for $N\lesssim 10^9$, while decreases towards the continuum limit with a power-law trend. Moreover, our numerical results appear to confirm the trend of a specific formulation of dynamical entropy and its relation with Lyapunov time scales.

astro-ph.GA

The 3D morphology of open clusters in the solar neighborhood III: Fractal dimension

We analyze the fractal dimension of open clusters using 3D spatial data from Gaia DR3 for 93 open clusters from Pang et al. (2024) and 127 open clusters from Hunt & Reffert (2024) within 500 pc. The box-counting method is adopted to calculate the fractal dimension of each cluster in three regions: the all-member region, $r \leq r_t$ (inside the tidal radius), and $r > r_t$ (outside the tidal radius). In both the Pang and Hunt catalogs, the fractal dimensions are smaller for the regions $r > r_t$ than those for $r \leq r_t$, indicating that the stellar distribution is more clumpy in the cluster outskirts. We classify cluster morphology based on the fractal dimension via the Gaussian Mixture Model. Our study shows that the fractal dimension can efficiently classify clusters in the Pang catalog into two groups. The fractal dimension of the clusters in the Pang catalog declines with age, which is attributed to the development of tidal tails. This is consistent with the expectations from the dynamical evolution of open clusters. We find strong evidence that the fractal dimension increases with cluster mass, which implies that higher-mass clusters are formed hierarchically from the mergers of lower-mass filamentary-type stellar groups. The transition of the fractal dimension for the spatial distribution of open clusters provides a useful tool to trace the Galactic star forming structures, from the location of the Local Bubble within the solar neighborhood to the spiral arms across the Galaxy.

astro-ph.GA

Beyond Causal Discovery for Astronomy: Learning Meaningful Representations with Independent Component Analysis

We present the first steps toward applying causal representation learning to astronomy. Following up on previous work that introduced causal discovery to the field for the first time, here we solve a long standing conundrum by identifying the direction of the causal relation between supermassive black hole (SMBH) mass and their host galaxy properties. This leverages a score-based causal discovery approach with an exact posterior calculation. Causal relations between SMBHs and their host galaxies are further clarified by Independent Component Analysis (ICA). The astrophysical problem we focus on is one of the most important open issues in the field and one that has not seen a definitive resolution in decades. We consider the space of six physical properties of galaxies, subdivided by morphology: elliptical, lenticular, and spiral, plus SMBH mass. We calculate an exact posterior over the space of directed acyclic graphs for these variables based on a flat prior and the Bayesian Gaussian equivalent score. The nature of the causal relation between galaxy properties and SMBH mass is found to vary smoothly with morphology, with galaxy properties determining SMBH mass in ellipticals and vice versa in spirals. This settles a long-standing debate and is compatible with our theoretical understanding of galaxy evolution. ICA reveals a decreasing number of meaningful Independent Components (ICs) from ellipticals and lenticular to spiral. Moreover, we find that only one IC correlates with SMBH mass in spirals while multiple ones do in ellipticals, further confirming our finding that SMBH mass causes galaxy properties in spirals, but the reverse holds in ellipticals.

astro-ph.GA

Causal Discovery in Astrophysics: Unraveling Supermassive Black Hole and Galaxy Coevolution

Correlation does not imply causation, but patterns of statistical association between variables can be exploited to infer a causal structure (even with purely observational data) with the burgeoning field of causal discovery. As a purely observational science, astrophysics has much to gain by exploiting these new methods. The supermassive black hole (SMBH)--galaxy interaction has long been constrained by observed scaling relations, that is low-scatter correlations between variables such as SMBH mass and the central velocity dispersion of stars in a host galaxy's bulge. This study, using advanced causal discovery techniques and an up-to-date dataset, reveals a causal link between galaxy properties and dynamically-measured SMBH masses. We apply a score-based Bayesian framework to compute the exact conditional probabilities of every causal structure that could possibly describe our galaxy sample. With the exact posterior distribution, we determine the most likely causal structures and notice a probable causal reversal when separating galaxies by morphology. In elliptical galaxies, bulge properties (built from major mergers) tend to influence SMBH growth, while in spiral galaxies, SMBHs are seen to affect host galaxy properties, potentially through feedback in gas-rich environments. For spiral galaxies, SMBHs progressively quench star formation, whereas in elliptical galaxies, quenching is complete, and the causal connection has reversed. Our findings support theoretical models of hierarchical assembly of galaxies and active galactic nuclei feedback regulating galaxy evolution. Our study suggests the potentiality for further exploration of causal links in astrophysical and cosmological scaling relations, as well as any other observational science.

astro-ph.GA

A machine learning framework to generate star cluster realisations

Context. Computational astronomy has reached the stage where running a gravitational N-body simulation of a stellar system, such as a Milky Way star cluster, is computationally feasible, but a major limiting factor that remains is the ability to set up physically realistic initial conditions. Aims. We aim to obtain realistic initial conditions for N-body simulations by taking advantage of machine learning, with emphasis on reproducing small-scale interstellar distance distributions. Methods. The computational bottleneck for obtaining such distance distributions is the hydrodynamics of star formation, which ultimately determine the features of the stars, including positions, velocities, and masses. To mitigate this issue, we introduce a new method for sampling physically realistic initial conditions from a limited set of simulations using Gaussian processes. Results. We evaluated the resulting sets of initial conditions based on whether they meet tests for physical realism. We find that direct sampling based on the learned distribution of the star features fails to reproduce binary systems. Consequently, we show that physics-informed sampling algorithms solve this issue, as they are capable of generating realisations closer to reality.

astro-ph.GA

Variable Star Light Curves in Koopman Space

We present the first application of data-driven techniques for dynamical system analysis based on Koopman theory to variable stars. We focus on light curves of RRLyrae type variables, in the Galactic globular cluster $ω$ Centauri. Light curves are thus summarized by a handful of complex eigenvalues, corresponding to oscillatory or fading dynamical modes. We find that variable stars of the RRc subclass can be summarized in terms of fewer ($\approx 8$) eigenvalues, while RRab need comparatively more ($\approx 12$). This result can be leveraged for classification and reflects the simpler structure of RRc light curves. We then consider variable stars displaying secular variations due to the Tseraskaya-Blazhko effect and find a change in relevant eigenvalues with time, with possible implications for the physical interpretation of the effect.

astro-ph.SR

Classifying binary black holes from Population III stars with the Einstein Telescope: A machine-learning approach

Third-generation (3G) gravitational-wave detectors such as the Einstein Telescope (ET) will observe binary black hole (BBH) mergers at redshifts up to $z\sim 100$. However, an unequivocal determination of the origin of high-redshift sources will remain uncertain because of the low signal-to-noise ratio (S/N) and poor estimate of their luminosity distance. This study proposes a machine-learning approach to infer the origins of high-redshift BBHs. We specifically differentiate those arising from Population III (Pop. III) stars, which probably are the first progenitors of star-born BBH mergers in the Universe, and those originated from Population I-II (Pop. I-II) stars. We considered a wide range of models that encompass the current uncertainties on Pop. III BBH mergers. We then estimated the parameter errors of the detected sources with ET using the Fisher information-matrix formalism, followed by a classification using XGBoost, which is a machine-learning algorithm based on decision trees. For a set of mock observed BBHs, we provide the probability that they belong to the Pop. III class while considering the parameter errors of each source. In our fiducial model, we accurately identify $\gtrsim 10\%$ of the detected BBHs that originate from Pop. III stars with a precision $>90\%$. Our study demonstrates that machine-learning enables us to achieve some pivotal aspects of the ET science case by exploring the origin of individual high-redshift GW observations. We set the basis for further studies, which will integrate additional simulated populations and account for further uncertainties in the population modeling.

astro-ph.HE

Reconstructing Robust Background IFU spectra using Machine Learning

In astronomy, spectroscopy consists of observing an astrophysical source and extracting its spectrum of electromagnetic radiation. Once extracted, a model is fit to the spectra to measure the observables, leading to an understanding of the underlying physics of the emission mechanism. One crucial, and often overlooked, aspect of this model is the background emission, which contains foreground and background astrophysical sources, intervening atmospheric emission, and artifacts related to the instrument such as noise. This paper proposes an algorithmic approach to constructing a background model for SITELLE observations using statistical tools and supervised machine learning algorithms. We apply a segmentation algorithm implemented in photutils to divide the data cube into background and source spaxels. After applying a principal component analysis (PCA) on the background spaxels, we train an artificial neural network to interpolate from the background to the source spaxels in the PCA coefficient space, which allows us to generate a local background model over the entire data cube. We highlight the performance of this methodology by applying it to SITELLE observations obtained of a SIGNALS galaxy, \NGC4449, and the Perseus galaxy cluster of galaxies, NGC 1275. We discuss the physical interpretation of the principal components and noise reduction in the resulting PCA-based reconstructions. Additionally, we compare the fit results using our new background modeling approach to standard methods used in the literature and find that our method better captures the emission from HII regions in NGC 4449 and the faint emission regions in NGC 1275. These methods also demonstrate that the background does change as a function of the position of the datacube.

astro-ph.GA

Quantitatively rating galaxy simulations against real observations with anomaly detection

Cosmological galaxy formation simulations are powerful tools to understand the complex processes that govern the formation and evolution of galaxies. However, evaluating the realism of these simulations remains a challenge. The two common approaches for evaluating galaxy simulations is either through scaling relations based on a few key physical galaxy properties, or through a set of pre-defined morphological parameters based on galaxy images. This paper proposes a novel image-based method for evaluating the quality of galaxy simulations using unsupervised deep learning anomaly detection techniques. By comparing full galaxy images, our approach can identify and quantify discrepancies between simulated and observed galaxies. As a demonstration, we apply this method to SDSS imaging and NIHAO simulations with different physics models, parameters, and resolution. We further compare the metric of our method to scaling relations as well as morphological parameters. We show that anomaly detection is able to capture similarities and differences between real and simulated objects that scaling relations and morphological parameters are unable to cover, thus indeed providing a new point of view to validate and calibrate cosmological simulations against observed data.

astro-ph.GA

Sharpening the dark matter signature in gravitational waveforms II: Numerical simulations with the NbodyIMRI code

Future gravitational wave observatories can probe dark matter by detecting the dephasing in the waveform of binary black hole mergers induced by dark matter overdensities. Such a detection hinges on the accurate modelling of the dynamical friction, induced by dark matter on the secondary compact object in intermediate and extreme mass ratio inspirals. In this paper, we introduce NbodyIMRI, a new publicly available code designed for simulating binary systems within cold dark matter `spikes'. Leveraging higher particle counts and finer timesteps, we validate the applicability of the standard dynamical friction formalism and provide an accurate determination of the maximum impact parameter of particles which can effectively scatter with a compact object, across various mass ratios. We also show that in addition to feedback due to dynamical friction, the dark matter also evolves through a `stirring' effect driven by the time-dependent potential of the binary. We introduce a simple semi-analytical scheme to account for this effect and demonstrate that including stirring tends to slow the rate of dark matter depletion and therefore enhances the impact of dark matter on the dynamics of the binary.

gr-qc

Active learning meets fractal decision boundaries: a cautionary tale from the Sitnikov three-body problem

Chaotic systems such as the gravitational N-body problem are ubiquitous in astronomy. Machine learning (ML) is increasingly deployed to predict the evolution of such systems, e.g. with the goal of speeding up simulations. Strategies such as active Learning (AL) are a natural choice to optimize ML training. Here we showcase an AL failure when predicting the stability of the Sitnikov three-body problem, the simplest case of N-body problem displaying chaotic behavior. We link this failure to the fractal nature of our classification problem's decision boundary. This is a potential pitfall in optimizing large sets of N-body simulations via AL in the context of star cluster physics, galactic dynamics, or cosmology.

astro-ph.GA

The search for the lost attractor

N-body systems characterized by inverse square attractive forces may display a self similar collapse known as the gravo-thermal catastrophe. In star clusters, collapse is halted by binary stars, and a large fraction of Milky Way clusters may have already reached this phase. It has been speculated -- with guidance from simulations -- that macroscopic variables such as central density and velocity dispersion are governed post-collapse by an effective, low-dimensional system of ODEs. It is still hard to distinguish chaotic, low dimensional motion, from high dimensional stochastic noise. Here we apply three machine learning tools to state-of-the-art dynamical simulations to constrain the post collapse dynamics: topological data analysis (TDA) on a lag embedding of the relevant time series, Sparse Identification of Nonlinear Dynamics (SINDY), and Tests of Accuracy with Random Points (TARP).

astro-ph.GA

Causa prima: cosmology meets causal discovery for the first time

In astrophysics, experiments are impossible. We thus must rely exclusively on observational data. Other observational sciences increasingly leverage causal inference methods, but this is not yet the case in astrophysics. Here we attempt causal discovery for the first time to address an important open problem in astrophysics: the (co)evolution of supermassive black holes (SMBHs) and their host galaxies. We apply the Peter-Clark (PC) algorithm to a comprehensive catalog of galaxy properties to obtain a completed partially directed acyclic graph (CPDAG), representing a Markov equivalence class over directed acyclic graphs (DAGs). Central density and velocity dispersion are found to cause SMBH mass. We test the robustness of our analysis by random sub-sampling, recovering similar results. We also apply the Fast Causal Inference (FCI) algorithm to our dataset to relax the hypothesis of causal sufficiency, admitting unobserved confounds. Hierarchical SMBH assembly may provide a physical explanation for our findings.

astro-ph.GA