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1. New metric improving Bayesian calibration of a multistage approach studying hadron and inclusive jet suppression

   https://doi.org/10.1103/PhysRevC.109.064903  

   Fan, W; Vujanovic, G; Bass, S A; Angerami, A; Arora, R; Cao, S; Chen, Y; Dai, T; Du, L; Ehlers, R; et al (June 2024, Physical Review C)

   We study parton energy-momentum exchange with the quark gluon plasma (QGP) within a multistage approach composed of in-medium Dokshitzer-Gribov-Lipatov-Altarelli-Parisi evolution at high virtuality, and (linearized) Boltzmann transport formalism at lower virtuality. This multistage simulation is then calibrated in comparison with high- $p_T$charged hadrons, $D$mesons, and the inclusive jet nuclear modification factors, using Bayesian model-to-data comparison, to extract the virtuality-dependent transverse momentum broadening transport coefficient $\hat{q}$. To facilitate this undertaking, we develop a quantitative metric for validating the Bayesian workflow, which is used to analyze the sensitivity of various model parameters to individual observables. The usefulness of this new metric in improving Bayesian model emulation is shown to be highly beneficial for future such analyses. Published by the American Physical Society2024 

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2. Hard-photon-triggered jets in $p\text{−}p$ and $A\text{−}A$ collisions

   https://doi.org/10.1103/yyr5-zp16  

   Sirimanna, C; Tachibana, Y; Majumder, A; Angerami, A; Arora, R; Bass, S A; Chen, Y; Datta, R; Du, L; Ehlers, R; et al (June 2025, Physical Review C)

   An investigation of high-transverse-momentum (high- $p_T$) photon-triggered jets in proton-proton ( $p\text{−}p$) and ion-ion ( $A\text{−}A$) collisions at $\sqrt{s_{NN}}=0.2$and $5.02\mathrm{TeV}$is carried out, using the multistage description of in-medium jet evolution. Monte Carlo simulations of hard scattering and energy loss in heavy-ion collisions are performed using parameters tuned in a previous study of the nuclear modification factor ( $R_{AA}$) for inclusive jets and high- $p_T$hadrons. We obtain a good reproduction of the experimental data for photon-triggered jet $R_{AA}$, as measured by the ATLAS detector, the distribution of the ratio of jet to photon $p_T$( $X_{J\gamma }$), measured by both CMS and ATLAS, and the photon-jet azimuthal correlation as measured by CMS. We obtain a moderate description of the photon-triggered jet $I_{AA}$, as measured by STAR. A noticeable improvement in the comparison is observed when one goes beyond prompt photons and includes bremsstrahlung and decay photons, revealing their significance in certain kinematic regions, particularly at $X_{J\gamma }>1$. Moreover, azimuthal angle correlations demonstrate a notable impact of bremsstrahlung photons on the distribution, emphasizing their role in accurately describing experimental results. This work highlights the success of the multistage model of jet modification to straightforwardly predict (this set of) photon-triggered jet observables. This comparison, along with the role played by bremsstrahlung photons, has important consequences on the inclusion of such observables in a future Bayesian analysis. Published by the American Physical Society2025 

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3. Transverse energy-energy correlators in the color-glass condensate at the electron-ion collider

   https://doi.org/10.1103/PhysRevD.109.094012  

   Kang, Zhong-Bo; Penttala, Jani; Zhao, Fanyi; Zhou, Yiyu (May 2024, Physical Review D)

   We investigate the transverse energy-energy correlators (TEEC) in the small- $x$regime at the upcoming Electron-Ion Collider (EIC). Focusing on the back-to-back production of electron-hadron pairs in both $ep$and $eA$collisions, we establish a factorization formula given in terms of the hard function, quark distributions, soft functions, and TEEC jet functions, where the gluon saturation effect is incorporated. Numerical results for TEEC in both $ep$and $eA$collisions are presented, together with the nuclear modification factor $R_A$. Our analysis reveals that TEEC observables in deep inelastic scattering provide a valuable approach for probing gluon saturation phenomena. Our findings underscore the significance of measuring TEEC at the EIC, emphasizing its efficacy in advancing our understanding of gluon saturation and nuclear modifications in high-energy collisions. Published by the American Physical Society2024 

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4. One-jettiness DIS event shape at ${\mathrm{N}}^3\mathrm{LL}+\mathcal{O}\left({\alpha }_s^2\right)$

   https://doi.org/10.1103/PhysRevD.110.014045  

   Cao, Haotian; Kang, Zhong-Bo; Liu, Xiaohui; Mantry, Sonny (July 2024, Physical Review D)

   We present results for the ${\tau }_1$and ${\tau }_{1a}$1-jettiness global event shape distributions, for deep inelastic scattering (DIS), at the ${\mathrm{N}}^3\mathrm{LL}+\mathcal{O}\left({\alpha }_s^2\right)$level of accuracy. These event-shape distributions quantify and characterize the pattern of final state radiation in electron-nucleus collisions. They can be used as a probe of nuclear structure functions, as nuclear medium effects in jet production, and for a precision extraction of the QCD strong coupling. The results presented here, along with the corresponding numerical codes, can be used for analyses with HERA data, in Electron-Ion Collider (EIC) simulation studies, and for eventual comparison with real EIC data. Published by the American Physical Society2024 

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5. 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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