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1. The development of thermodynamically consistent and physics-informed equation-of-state model through machine learning

   https://doi.org/10.1063/5.0192447  

   Hinz, J; Yu, Dayou; Pandey, Deep Shankar; Sapkota, Hitesh; Yu, Qi; Mihaylov, D I; Karasiev, V V; Hu, S X (June 2024, APL Machine Learning)

   Ab initio molecular dynamics (AIMD) simulations have become an important tool used in the construction of equations of state (EOS) tables for warm dense matter. Due to computational costs, only a limited number of system state conditions can be simulated, and the remaining EOS surface must be interpolated for use in radiation-hydrodynamic simulations of experiments. In this work, we develop a thermodynamically consistent EOS model that utilizes a physics-informed machine learning approach to implicitly learn the underlying Helmholtz free-energy from AIMD generated energies and pressures. The model, referred to as PIML-EOS, was trained and tested on warm dense polystyrene producing a fit within a 1% relative error for both energy and pressure and is shown to satisfy both the Maxwell and Gibbs–Duhem relations. In addition, we provide a path toward obtaining thermodynamic quantities, such as the total entropy and chemical potential (containing both ionic and electronic contributions), which are not available from current AIMD simulations. 

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2. Review of Particle Physics

   https://doi.org/10.1093/ptep/ptaa104  

   Zyla, P A; Barnett, R M; Beringer, J; Dahl, O; Dwyer, D A; Groom, D E; Lin, C -J; Lugovsky, K S; Pianori, E; Robinson, D J; et al (August 2020, Progress of Theoretical and Experimental Physics)

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   Abstract The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 3,324 new measurements from 878 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on High Energy Soft QCD and Diffraction and one on the Determination of CKM Angles from B Hadrons. The Review is divided into two volumes. Volume 1 includes the Summary Tables and 98 review articles. Volume 2 consists of the Particle Listings and contains also 22 reviews that address specific aspects of the data presented in the Listings. The complete Review (both volumes) is published online on the website of the Particle Data Group (pdg.lbl.gov) and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is available in print and as a web version optimized for use on phones as well as an Android app. 

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3. Review of Particle Physics

   https://doi.org/10.1093/ptep/ptac097  

   Workman, R L; Burkert, V D; Crede, V; Klempt, E; Thoma, U; Tiator, L; Agashe, K; Aielli, G; Allanach, B C; Amsler, C; et al (August 2022, Progress of Theoretical and Experimental Physics)

   Abstract The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143 new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on Machine Learning, and one on Spectroscopy of Light Meson Resonances. The Review is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings. The complete Review (both volumes) is published online on the website of the Particle Data Group (pdg.lbl.gov) and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is available in print, as a web version optimized for use on phones, and as an Android app. 

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4. Quick Guides for Use of the CompOSE Data Base

   https://doi.org/10.3390/particles5030028  

   Dexheimer, Veronica; Mancini, Marco; Oertel, Micaela; Providência, Constança; Tolos, Laura; Typel, Stefan (September 2022, Particles)

   We present a combination of two quick guides aimed at summarizing relevant information about the CompOSE nuclear equation of state repository. The first is aimed at nuclear physicists and describes how to provide standard equation of state tables. The second quick guide is meant for users and describes the basic procedures to obtain customized tables with equation of state data. Several examples are included to help providers and users to understand and benefit from the CompOSE database. 

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5. Finite-temperature effects in dynamical spacetime binary neutron star merger simulations: validation of the parametric approach

   https://doi.org/10.1093/mnras/stac2450  

   Raithel, Carolyn A; Espino, Pedro; Paschalidis, Vasileios (September 2022, Monthly Notices of the Royal Astronomical Society)

   ABSTRACT Parametric equations of state (EoSs) provide an important tool for systematically studying EoS effects in neutron star merger simulations. In this work, we perform a numerical validation of the M*-framework for parametrically calculating finite-temperature EoS tables. The framework, introduced by Raithel et al., provides a model for generically extending any cold, β-equilibrium EoS to finite temperatures and arbitrary electron fractions. In this work, we perform numerical evolutions of a binary neutron star merger with the SFHo finite-temperature EoS, as well as with the M*-approximation of this same EoS, where the approximation uses the zero-temperature, β-equilibrium slice of SFHo and replaces the finite-temperature and composition-dependent parts with the M*-model. We find that the approximate version of the EoS is able to accurately recreate the temperature and thermal pressure profiles of the binary neutron star remnant, when compared to the results found using the full version of SFHo. We additionally find that the merger dynamics and gravitational wave signals agree well between both cases, with differences of $$\lesssim 1\!-\!2\,{\textrm{per cent}}$$ introduced into the post-merger gravitational wave peak frequencies by the approximations of the EoS. We conclude that the M*-framework can be reliably used to probe neutron star merger properties in numerical simulations. 

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