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1. General mass variable flavor number scheme for $Z$ boson production in association with a heavy quark at hadron colliders

   https://doi.org/10.1103/PhysRevD.110.114030  

   Guzzi, Marco; Nadolsky, Pavel; Reina, Laura; Wackeroth, Doreen; Xie, Keping (December 2024, Physical Review D)

   We present a methodology to streamline implementation of massive-quark radiative contributions in calculations with a variable number of active partons in proton-proton collisions. The methodology introduces and heavy-quark parton distribution functions (PDFs) to implement calculations in the Aivazis–Collins–Olness–Tung (ACOT) factorization scheme and its simplified realization in various processes up to the next-to-the-next-to-leading order in the QCD coupling strength. Interpolation tables for bottom-quark subtraction and residual distributions for CT18 NLO and NNLO PDF ensembles are provided in the common LHAPDF6 format. A numerical calculation of $Z$-boson production with at least one $b$jet at the Large Hadron Collider beyond the lowest order in QCD is considered for illustration purposes. Published by the American Physical Society2024 

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2. Quantum maximum entropy closure for small flavor coherence

   https://doi.org/10.1103/PhysRevD.111.063022  

   Froustey, Julien; Kneller, James P; McLaughlin, Gail C (March 2025, Physical Review D)

   Quantum angular moment transport schemes are an important avenue toward describing neutrino flavor mixing phenomena in dense astrophysical environments such as supernovae and merging neutron stars. Successful implementation will require new closure relations that go beyond those used in classical transport. In this paper, we derive the first analytic expression for a quantum M1 closure, valid in the limit of small flavor coherence, based on the maximum entropy principle. We verify that the resulting closure relation has the appropriate limits and characteristic speeds in the diffusive and free-streaming regimes. We then use this new closure in a moment linear stability analysis to search for fast flavor instabilities in a binary neutron star merger simulation and find better results as compared with previously designed, , semiclassical closures. Published by the American Physical Society2025 

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3. Neutrino many-body flavor evolution: The full Hamiltonian

   https://doi.org/10.1103/PhysRevD.110.123028  

   Cirigliano, Vincenzo; Sen, Srimoyee; Yamauchi, Yukari (December 2024, Physical Review D)

   We study neutrino flavor evolution in the quantum many-body approach using the full neutrino-neutrino Hamiltonian, including the usually neglected terms that mediate nonforward scattering processes. Working in the occupation number representation with plane waves as single-particle states, we explore the time evolution of simple initial states with up to $N=10$neutrinos. We discuss the time evolution of the Loschmidt echo, one body flavor and kinetic observables, and the one-body entanglement entropy. For the small systems considered, we observe “thermalization” of both flavor and momentum degrees of freedom on comparable time scales, with results converging towards expectation values computed within a microcanonical ensemble. We also observe that the inclusion of nonforward processes generates a faster flavor evolution compared to the one induced by the truncated (forward) Hamiltonian. Published by the American Physical Society2024 

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4. Bayesian inference analysis of jet quenching using inclusive jet and hadron suppression measurements

   https://doi.org/10.1103/PhysRevC.111.054913  

   Ehlers, R; Chen, Y; Mulligan, J; Ji, Y; Kumar, A; Mak, S; Jacobs, P M; Majumder, A; Angerami, A; Arora, R; et al (May 2025, Physical Review C)

   The Collaboration reports a new determination of the jet transport parameter $\hat{q}$in the quark-gluon plasma (QGP) using Bayesian inference, incorporating all available inclusive hadron and jet yield suppression data measured in heavy-ion collisions at the BNL Relativistic Heavy Ion Collider (RHIC) and the CERN Large Hadron Collider (LHC). This multi-observable analysis extends the previously published Bayesian inference determination of $\hat{q}$, which was based solely on a selection of inclusive hadron suppression data. is a modular framework incorporating detailed dynamical models of QGP formation and evolution, and jet propagation and interaction in the QGP. Virtuality-dependent partonic energy loss in the QGP is modeled as a thermalized weakly coupled plasma, with parameters determined from Bayesian calibration using soft-sector observables. This Bayesian calibration of $\hat{q}$utilizes active learning, a machine-learning approach, for efficient exploitation of computing resources. The experimental data included in this analysis span a broad range in collision energy and centrality, and in transverse momentum. In order to explore the systematic dependence of the extracted parameter posterior distributions, several different calibrations are reported, based on combined jet and hadron data; on jet or hadron data separately; and on restricted kinematic or centrality ranges of the jet and hadron data. Tension is observed in comparison of these variations, providing new insights into the physics of jet transport in the QGP and its theoretical formulation. Published by the American Physical Society2025 

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5. Isolating Unisolated Upsilons with Anomaly Detection in CMS Open Data

   https://doi.org/10.1103/vvv3-5kkl  

   Gambhir, Rikab; Mastandrea, Radha; Nachman, Benjamin; Thaler, Jesse (July 2025, Physical Review Letters)

   We present the first study of anti-isolated Upsilon decays to two muons ( $\mathrm{\Upsilon }\to {\mu }^{+}{\mu }^{-}$) in proton-proton collisions at the Large Hadron Collider. Using a machine learning (ML)-based anomaly detection strategy, we “rediscover” the $\mathrm{\Upsilon }$in 13 TeV CMS Open Data from 2016, despite overwhelming anti-isolated backgrounds. We elevate the signal significance to $6.4\sigma$using these methods, starting from $1.6\sigma$using the dimuon mass spectrum alone. Moreover, we demonstrate improved sensitivity from using an ML-based estimate of the multifeature likelihood compared to traditional “cut-and-count” methods. This is the first ever detection of anti-isolated Upsilons, which can be useful in the study of heavy-flavor fragmentation in quantum chromodynamics. Our Letter demonstrates that it is possible and practical to find real signals in experimental collider data using ML-based anomaly detection, and we distill a readily accessible benchmark dataset from the CMS Open Data to facilitate future anomaly detection developments. Published by the American Physical Society2025 

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