most citedExplainable autoencoder for neutron star dense matter parameter estimation

6 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

nucl-th2026

Deep-learning classification of physically admissible nuclear-matter equations of state

Ahmed Abuali, Micheal Kahangirwe, Francesco Di Clemente +3

Thermodynamic stability and causality impose fundamental constraints on the equation of state (EoS) of nuclear matter. Verifying these constraints conventionally requires calculati…

nucl-th2026

Studying the QCD Matter produced in Heavy-Ion Collisions using the MUSES Calculation Engine

Johannes Jahan, Kevin P. Pala, Yumu Yang +42

The equation of state of hot and dense matter is essential for describing heavy-ion collisions at all collision energies. Here, we explore the capabilities of the latest version of…

hep-ph2025

Upper Bound on the Cosmic Baryon Chemical Potential from Lepton-Flavor Asymmetry

Francesco Di Clemente, Alessandro Drago, Lorenzo Formaggio +3

We study the early Universe trajectory around the QCD transition in lepton-flavor-asymmetric cases with small total lepton asymmetry (), while allowing larg…

astro-ph.CO2025★ 1 cited

Cosmic Trajectories calculation with state of the art lattice QCD equation of state

Lorenzo Formaggio, Francesco Di Clemente, Geetika Yadav +2

We compute the full cosmic trajectories of the early Universe across the QCD phase diagram as the plasma cools from MeV to MeV, assuming -equilibrated matter…

physics.comp-ph2025★ 6 cited

Explainable autoencoder for neutron star dense matter parameter estimation

Francesco Di Clemente, Matteo Scialpi, Michał Bejger

We present a physics-informed autoencoder designed to encode the equation of state of neutron stars into an interpretable latent space. In particular the input will be encoded in t…