arXiv subjects

Bernardo M. O. Fraga

Publications and source records attributed to Bernardo M. O. Fraga.

7 recordsLinked to original sources

arXiv2025

Symbolic Regression Is All You Need: From Simulations to Scaling Laws in Binary Neutron Star Mergers

Gravitational wave sources with electromagnetic counterparts have highlighted the need for predictive, interpretable models linking the parameters of compact binary systems to post-merger remnants and mass outflows. In this work, we explore AI-driven symbolic regression (SR) frameworks to derive updated analytical relations for disk ejecta mass in binary neutron star mergers, trained on state-of-the-art numerical relativity simulations. Our method reveals a set of compact equations that outperform existing fitting formulae across multiple statistical metrics while remaining physically interpretable. Notably, SR also enables alternative predictor sets (e.g., $\{M_1,M_2,\tildeΛ\}$) that match or exceed the accuracy of models relying solely on compactness of the lightest neutron star ($C_1$), enabling new parameter constraints from electromagnetic observations. Unlike traditional black-box machine learning models, these closed-form expressions generalize robustly to regions of the parameter space not represented in the training data, offering a physics-informed tool for multimessenger observations and constraints on the neutron star equation of state.

astro-ph.HE↗

arXiv2024

Kilonova Spectral Inverse Modelling with Simulation-Based Inference: An Amortized Neural Posterior Estimation Analysis

Kilonovae represent a category of astrophysical transients, identifiable as the electromagnetic observable counterparts associated with the coalescence events of binary systems comprising neutron stars and neutron star-black hole pairs. They act as probes for heavy-element nucleosynthesis in astrophysical environments. These studies rely on inference of the physical parameters (e.g., ejecta mass, velocity, composition) that describe kilonovae based on electromagnetic observations. This is a complex inverse problem typically addressed with sampling-based methods such as Markov-chain Monte Carlo (MCMC) or nested sampling algorithms. However, repeated inferences can be computationally expensive due to the sequential nature of these methods. This poses a significant challenge to ensuring the reliability and statistical validity of the posterior approximations and, thus, the inferred kilonova parameters themselves. We present a novel approach: Simulation-Based Inference (SBI) using simulations produced by KilonovaNet. Our method employs an ensemble of Amortized Neural Posterior Estimation (ANPE) with an embedding network to directly predict posterior distributions from simulated spectral energy distributions (SEDs). We take advantage of the quasi-instantaneous inference time of ANPE to demonstrate the reliability of our posterior approximations using diagnostics tools, including coverage diagnostic and posterior predictive checks. We further test our model with real observations from AT2017gfo, the only kilonova with multi-messenger data, demonstrating agreement with previous likelihood-based methods while reducing inference time down to a few seconds. The inference results produced by ANPE appear to be conservative and reliable, paving the way for testable and more efficient kilonova parameter inference.

astro-ph.HE↗

arXiv2021

The LSST-DESC 3x2pt Tomography Optimization Challenge

This paper presents the results of the Rubin Observatory Dark Energy Science Collaboration (DESC) 3x2pt tomography challenge, which served as a first step toward optimizing the tomographic binning strategy for the main DESC analysis. The task of choosing an optimal tomographic binning scheme for a photometric survey is made particularly delicate in the context of a metacalibrated lensing catalogue, as only the photometry from the bands included in the metacalibration process (usually riz and potentially g) can be used in sample definition. The goal of the challenge was to collect and compare bin assignment strategies under various metrics of a standard 3x2pt cosmology analysis in a highly idealized setting to establish a baseline for realistically complex follow-up studies; in this preliminary study, we used two sets of cosmological simulations of galaxy redshifts and photometry under a simple noise model neglecting photometric outliers and variation in observing conditions, and contributed algorithms were provided with a representative and complete training set. We review and evaluate the entries to the challenge, finding that even from this limited photometry information, multiple algorithms can separate tomographic bins reasonably well, reaching figures-of-merit scores close to the attainable maximum. We further find that adding the g band to riz photometry improves metric performance by ~15% and that the optimal bin assignment strategy depends strongly on the science case: which figure-of-merit is to be optimized, and which observables (clustering, lensing, or both) are included.

astro-ph.IM↗

arXiv2021

Deep Learning Blazar Classification based on Multi-frequency Spectral Energy Distribution Data

Blazars are among the most studied sources in high-energy astrophysics as they form the largest fraction of extragalactic gamma-ray sources and are considered prime candidates for being the counterparts of high-energy astrophysical neutrinos. Their reliable identification amid the many faint radio sources is a crucial step for multi-messenger counterpart associations. As the astronomical community prepares for the coming of a number of new facilities able to survey the non-thermal sky at unprecedented depths, from radio to gamma-rays, machine learning techniques for fast and reliable source identification are ever more relevant. The purpose of this work was to develop a deep learning architecture to identify blazar within a population of AGN based solely on non-contemporaneous spectral energy distribution information, collected from publicly available multi-frequency catalogues. This study uses an unprecedented amount of data, with SEDs for $\approx 14,000$ sources collected with the Open Universe VOU-Blazars tool. It uses a convolutional long-short term memory neural network purposefully built for the problem of SED classification, which we describe in detail and validate. The network was able to distinguish blazars from other types of AGNs to a satisfying degree (achieving a ROC area under curve of $0.98$), even when trained on a reduced subset of the whole sample. This initial study does not attempt to classify blazars among their different sub-classes, or quantify the likelihood of any multi-frequency or multi-messenger association, but is presented as a step towards these more practically-oriented applications.

astro-ph.HE↗

arXiv2014

Critical configurations for a system of semidegenerate fermions

We study an isothermal system of semi-degenerate self-gravitating fermions in general relativity. Such systems present mass density solutions with a central degenerate core, a plateau and a tail which follows a power law behaviour $r^{-2}$. The different solutions are governed by the free parameters of the model: the degeneracy and temperature parameters at the center, and the particle mass $m$. We then analyze in detail the free parameter space for a fixed $m$ in the keV regime, by studying the one-parameter sequences of equilibrium configurations up to the critical point, which is represented by the maximum in a central density ($ρ_0$) Vs. core mass ($M_c$) diagram. We show that for fully degenerate cores, the known expression for the critical core mass $M_c^{cr}\propto m_{pl}^3/m^2$ is obtained, while instead for low degenerate cores, the critical core mass increases showing the temperature effects in a non linear way. The main result of this work is that when applying this theory to model the distribution of dark matter in galaxies from the very center up to the outer halos, we do not find any critical core-halo configuration of self-gravitating fermions, which be able to explain the super massive dark object in their centers together with an outer halo simultaneously.

astro-ph.GA↗

arXiv2014

Self-gravitating system of semidegenerate fermions as central objects and dark matter halos in galaxies

We propose a unified model for dark matter haloes and central galactic objects as a self-gravitating system of semidegenerated fermions in thermal equilibrium. We consider spherical symmetry and then we solve the equations of gravitational equilibrium using the Fermi integrals in a dimensionless manner, obtaining the density profile and velocity curve. We also obtain scaling laws for the observables of the system and show that, for a wide range of our parameters, our model is consistent with the so called universality of the surface density of dark matter.

astro-ph.CO↗

arXiv2007

Cosmic acceleration from interaction of ordinary fluids

Cosmological models with two interacting fluids, each satisfying the strong energy condition, are studied in the framework of classical General Relativity. If the interactions are phenomenologically described by a power law in the scale factor, the two initial interacting fluids can be equivalently substituted by two non interacting effective fluids, where one of them may violate the strong energy condition and/or have negative energy density. Analytical solutions of the Friedmann equations of this general setting are obtained and studied. One may have, depending on the scale where the interaction becomes important, non singular universes with early accelerated phase, or singular models with transition from decelerated to accelerated expansion at large scales. Among the first, there are bouncing models where contraction is stopped by the interaction. In the second case, one obtains dark energy expansion rates without dark energy, like $Λ$CDM or phantomic accelerated expansions without cosmological constant or phantoms, respectively.

gr-qc↗