26 citations · 76 across the 8 of their papers we have counts for
9 papers
Set Transformer inference of the neutron star equation of state from stellar observations
Márcio Ferreira, Valéria Carvalho, Michał Bejger +1
We develop a permutation-invariant Set Transformer to reconstruct the equation of state (EoS) of dense matter from variable-size, unordered sets of neutron star (NS) observations.…
NS-UNO: Neutron Star EoS Inference from an Unconstrained Number of Observations
Valéria Carvalho, Márcio Ferreira, Michał Bejger +1
Future multimessenger observations of neutron stars (NS) are expected to substantially increase both the number and precision of astrophysical constraints on the equation of state…
Neural Posterior Estimation of Neutron Star Equations of State
Valéria Carvalho, Márcio Ferreira, Michał Bejger +1
We present a simulation-based inference (SBI) framework to constrain the neutron star (NS) equation of state (EoS) from astrophysical observations of masses, radii and tidal deform…
Detecting Hyperons in neutron stars -- a machine learning approach
Valéria Carvalho, Márcio Ferreira, Constança Providência
We present a neural network classification model for detecting the presence of hyperonic degrees of freedom in neutron stars. The models take radii and/or tidal deformabilities as…
Assessing the joint effect of temperature and magnetic field on the neutron star equation of state
Luigi Scurto, Valéria Carvalho, Helena Pais +1
In this work, we study the effect of strong magnetic fields on the equation of state (EoS) of warm, homogeneous, Neutron Star (NS) matter in beta equilibrium. NS matter is describe…
Detecting the third family of compact stars with normalizing flows
Valéria Carvalho, Márcio Ferreira, Constança Providência +1
We explore the anomaly detection framework based on Normalizing Flows (NF) models introduced in \cite{PhysRevC.106.065802} to detect the presence of a large (destabilising) dense m…
From NS observations to nuclear matter properties: a machine learning approach
Valéria Carvalho, Márcio Ferreira, Constança Providência
This study is devoted to the inference problem of extracting the nuclear matter properties directly from a set of mass-radius observations. We employ Bayesian neural networks (BNNs…
Decoding Neutron Star Observations: Revealing Composition through Bayesian Neural Networks
Valéria Carvalho, Márcio Ferreira, Tuhin Malik +1
We exploit the great potential offered by Bayesian Neural Networks (BNNs) to directly decipher the internal composition of neutron stars (NSs) based on their macroscopic properties…
Extracting nuclear matter properties from the neutron star matter equation of state using deep neural networks
Márcio Ferreira, Valéria Carvalho, Constança Providência
The extraction of the nuclear matter properties from neutron star (NS) observations is nowadays an important issue, in particular, the properties that characterize the symmetry ene…