5 papers
NucleiML: A machine learning framework of ground-state properties of finite nuclei for accelerated Bayesian exploration
Anagh Venneti, Chiranjib Mondal, Sk Md Adil Imam +2
The global behavior of the nuclear equation of state (EoS) is commonly studied using data from finite nuclei (FN), heavy-ion collisions, and astrophysical observations of neutron s…
Influence of Finite-Nuclei Constraints on High-Density Transitions and Neutron Star Properties
Anagh Venneti, Sarmistha Banik, Bijay K Agrawal
We construct posterior distributions of the equation of state (EoS) for matter beyond the inner crust of neutron stars by incorporating finite nuclei (FN) constraints within relati…
Fermionic versus Bosonic Dark Matter in Neutron Stars: A Bayesian Study with Multi-Density Constraints
Payaswinee Arvikar, Sakshi Gautam, Anagh Venneti +1
We perform a comparative Bayesian analysis of fermionic and bosonic dark matter admixed neutron stars (DMANS) by incorporating a comprehensive set of theoretical, experimental, and…
Exploring Fermionic Dark Matter Admixed Neutron Stars in the Light of Astrophysical Observations
Payaswinee Arvikar, Sakshi Gautam, Anagh Venneti +1
We studied the properties of dark matter admixed-neutron stars (DMANS), considering fermionic dark matter (DM) that interacts gravitationally with hadronic matter (HM). Using relat…
Bayesian evaluation of hadron-quark phase transition models through neutron star observables in light of nuclear and astrophysics data
Debanjan Guha Roy, Anagh Venneti, Tuhin Malik +2
We investigate the role of hybrid and nucleonic equations of state (EOSs) within neutron star (NS) interiors using Bayesian inference to evaluate their alignment with recent observ…