Very recently, the two-photon decay width of the <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>η</mi><mi>b</mi></msub></math> meson was computed with lattice QCD methods. This decay has not yet been measured. The knowledge of this width allows for the calculation of the <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>η</mi><mi>b</mi></msub></math> production cross section through photon-photon interactions in ultraperipheral (UPC) <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>P</mi><mi>b</mi><mtext>−</mtext><mi>P</mi><mi>b</mi></mrow></math> collisions. In this work we present this calculation, which is the first application of the lattice result. Since UPCs are gaining increasing attention of the heavy-ion community, we take the opportunity to perform a comprehensive study of the different ways of defining ultraperipheral collisions and of the different ways to treat the equivalent photon flux.

Brazil

C.N. Azevedo

F.C. Sobrinho

F.S. Navarra

Production of <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>η</mi><mi>b</mi></msub></math> in ultraperipheral Pb-Pb collisions

Jet reconstruction in an ultrarelativistic heavy-ion collision suffers from a notoriously large, fluctuating thermal background. Traditional background‐subtraction methods struggle to remove this soft background while preserving the jet's hard substructure. In this Letter, we present DeepSub, a machine-learning-based approach for full-event background‐subtraction. DeepSub utilizes a model based on Swin Transformer layers to denoise jet images and disentangle hard jets from the heavy-ion background. DeepSub significantly outperforms existing subtraction techniques by reproducing key jet observables such as jet <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>p</mi><mi mathvariant="normal">T</mi></msub></math> and mass, and substructure observables such as girth and the energy correlation function, at the subpercent to percent level. As such, DeepSub paves the way for precision heavy-ion measurements in hitherto inaccessible kinematic regimes.

United States

Umar Sohail Qureshi

Raghav Kunnawalkam Elayavalli

Deep image reconstruction for background subtraction in heavy-ion collisions

Determining the transport properties of quark-gluon plasma is one of the most important aspects of relativistic heavy ion collision studies. Field-theoretical calculations of transport coefficients such as the shear and bulk viscosities require Kubo formulas, which in turn require real-time correlation functions of stress-energy tensors. Consequently, knowing the analytic structure of these correlation functions is essential in any such studies. Using the energy-conservation laws and the results from gravity-hydrodynamics analysis, we determine the low-frequency and low-wavenumber analytic structures of all stress-energy correlation functions in the rest frame of the medium. By comparing with the diffusion and sound spectra from the second-order and third-order relativistic hydrodynamics, various new Kubo formulas are derived in the limit where the zero-frequency limit is taken first. We also show that the meaning of the Kubo formulas for relaxation times can change when higher-order terms are added to the hydrodynamics. A subtle issue of taking the zero frequency and zero wave0number limits when using skeleton diagrams is addressed as well.

Canada

Sangyong Jeon

Poland

Alina Czajka

South Korea

Juhee Hong

Analytic structure of stress-energy response functions and new Kubo formulas

In the present work, we examine the production of <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi mathvariant="normal">Ξ</mi><mo>(</mo><mn>1530</mn><mo>)</mo></mrow></math> in the <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msup><mi>K</mi><mo>−</mo></msup><mi>p</mi><mo>→</mo><msup><mi>K</mi><mo>+</mo></msup><mi mathvariant="normal">Ξ</mi><msup><mrow><mo>(</mo><mn>1530</mn><mo>)</mo></mrow><mo>−</mo></msup></mrow></math> and <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msup><mi>K</mi><mo>−</mo></msup><mi>p</mi><mo>→</mo><msup><mi>K</mi><mn>0</mn></msup><mi mathvariant="normal">Ξ</mi><msup><mrow><mo>(</mo><mn>1530</mn><mo>)</mo></mrow><mn>0</mn></msup></mrow></math> reactions utilizing an effective Lagrangian approach. To accurately fit the cross sections for both processes, we include nine <math xmlns="http://www.w3.org/1998/Math/MathML"><mi mathvariant="normal">Λ</mi></math> and <math xmlns="http://www.w3.org/1998/Math/MathML"><mi mathvariant="normal">Σ</mi></math> hyperons and their resonances in both <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>s</mi></math>- and <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>u</mi></math>-channel processes. Considering the discrepancy of the measured cross sections for <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msup><mi>K</mi><mo>−</mo></msup><mi>p</mi><mo>→</mo><msup><mi>K</mi><mo>+</mo></msup><mi mathvariant="normal">Ξ</mi><msup><mrow><mo>(</mo><mn>1530</mn><mo>)</mo></mrow><mo>−</mo></msup></mrow></math> within the range <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msqrt><mi>s</mi></msqrt><mo>=</mo><mrow><mo>[</mo><mn>2.087</mn><mo>,</mo><mn>2.168</mn><mo>]</mo></mrow><mspace width="4pt"></mspace><mi>GeV</mi></mrow></math>, we employ two distinct fitting strategies: a uniform weighting scheme (Model A) and a different weighting approach (Model B). A comparative analysis suggests that Model A yields a superior global agreement with experimental data compared to Model B. Beyond fitting the cross sections, we also estimate the individual contributions from various intermediate states. Our results reveal that the cross section arising from the <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi mathvariant="normal">Σ</mi><mo>(</mo><mn>1193</mn><mo>)</mo></mrow></math> intermediate process is dominant. Furthermore, we predict different cross sections for <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msup><mi>K</mi><mo>−</mo></msup><mi>p</mi><mo>→</mo><msup><mi>K</mi><mo>+</mo></msup><mi mathvariant="normal">Ξ</mi><msup><mrow><mo>(</mo><mn>1530</mn><mo>)</mo></mrow><mo>−</mo></msup></mrow></math> and <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msup><mi>K</mi><mo>−</mo></msup><mi>p</mi><mo>→</mo><msup><mi>K</mi><mn>0</mn></msup><mi mathvariant="normal">Ξ</mi><msup><mrow><mo>(</mo><mn>1530</mn><mo>)</mo></mrow><mn>0</mn></msup></mrow></math> at several representative center-of-mass energies, providing testable predictions for forthcoming J-PARC experiments.

China

Quan-Yun Guo

Jing Liu

Peiwen Wu

Dian-Yong Chen

Production of <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi mathvariant="normal">Ξ</mi><mo>(</mo><mn>1530</mn><mo>)</mo></mrow></math> in the <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msup><mi>K</mi><mo>−</mo></msup><mi>p</mi></mrow></math> scattering process

The detection of the chiral magnetic effect (CME) in relativistic heavy-ion collisions remains challenging due to substantial background contributions that obscure the expected signal. In this Letter, we present a novel machine learning approach for constructing optimized observables that significantly enhance CME detection capabilities. By parametrizing generic observables constructed from flow harmonics and optimizing them to maximize the signal-to-background ratio, we systematically develop CME-sensitive measures that outperform conventional methods. Using simulated data from the anomalous viscous fluid dynamics framework, our machine learning observables demonstrate up to 90% higher sensitivity to CME signals compared to traditional <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>γ</mi></math> and <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>δ</mi></math> correlators, while maintaining minimal background contamination. The constructed observables provide physical insight into optimal CME detection strategies and offer a promising path forward for experimental searches of the CME at the BNL Relativistic Heavy Ion Collider and the CERN Large Hadron Collider.

Japan

Yuji Hirono

Kazuki Ikeda

Dmitri E. Kharzeev

Ziyi Liu

Shuzhe Shi

Optimal observables for the chiral magnetic effect from machine learning

We present a novel deep generative framework that uses probabilistic diffusion models for ultrafast, event-by-event simulations of heavy-ion collision output. This new framework is trained on ultrarelativistic quantum molecular dynamics (UrQMD) cascade data to generate a full collision event output containing 26 distinct hadron species. The output is represented as a point cloud, where each point is defined by a particle's momentum vector and its corresponding species information. Our architecture integrates a normalizing flow-based condition generator that encodes global event features into a latent vector, and a diffusion model that synthesizes a point cloud of particles based on this condition. A detailed description of the model and an in-depth analysis of its performance is provided. The conditional point-cloud diffusion model learns to generate realistic output particles of collision events which successfully reproduce the UrQMD distributions for multiplicity, momentum, and rapidity of each hadron type. The flexible point-cloud representation of the event output preserves full event-level granularity, enabling direct application to inverse problems and parameter estimation tasks while also making it easily adaptable for accelerating any event-by-event model calculation or detector simulation.

Germany

Manjunath Omana Kuttan

Kai Zhou

Jan Steinheimer

Horst Stoecker

Ultrafast, event-by-event heavy-ion simulations for next-generation experiments

A novel point-cloud diffusion model for relativistic heavy-ion collisions, capable of ultrafast generation of complete, event-by-event collision output, is introduced. When trained on ultrarelativistic quantum molecular dynamics cascade simulations, the model generates realistic collision event output containing 26 distinct hadron species, as a list of particle momentum vectors along with their particle ID. From solving inverse problems to accelerating model calculations or detector simulations, the model can be a promising general-purpose tool for heavy-ion collisions beneficial to both theoretical studies and experimental applications.

Toward a foundation model for heavy-ion collision experiments based on point-cloud diffusion

We construct an equation of state describing cold and dense matter in the core of neutron stars which includes an admixture of fermionic dark matter and incorporates nucleon effective masses derived from the relativistic Brueckner-Hartree-Fock (BHF) many-body approach within a relativistic mean-field model. Such a BHF-informed mixed-model approach increases stellar compactness, with mass-radius configurations that are consistent with smaller, lighter pulsars. The model displays the expected nonmonotonic behavior of sound speed hinted at by neutron-star data and is closer to the conformal bound at maximum mass. We find that the model displays tension with bounds on heavier pulsars, suggesting that the hypothesis of an aggregated dark component in neutron stars needs further critical study.

India

Arijit Das

Prashanth Jaikumar

France

Adarsh Karekkat

Tanumoy Mandal

Effects of a Brueckner-Hartree-Fock–corrected effective mass on speed of sound, conformality, and observables of dark matter–admixed neutron stars

Dark photons, the hypothetical gauge bosons associated with an additional <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>U</mi><msup><mrow><mo>(</mo><mn>1</mn><mo>)</mo></mrow><mo>′</mo></msup></mrow></math> symmetry, can couple to standard model particles through a small kinetic mixing parameter <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>ɛ</mi></math> with the ordinary photon. This mechanism provides a portal between the dark sector and visible matter. In this study, we present a procedure to derive theoretical upper bounds on the kinetic mixing parameter <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msup><mi>ɛ</mi><mn>2</mn></msup><mrow><mo>(</mo><msub><mi>M</mi><mi>U</mi></msub><mo>)</mo></mrow></mrow></math> by analyzing dilepton spectra from heavy-ion collisions across a broad energy range, from SIS to LHC energies. Our analysis is based on the microscopic parton-hadron-string dynamics (PHSD) transport approach, which successfully reproduces the measured dilepton spectra in <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>p</mi><mo>+</mo><mi>p</mi></mrow><mo>,</mo><mo> </mo><mrow><mi>p</mi><mo>+</mo><mi>A</mi></mrow></math>, and <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>A</mi><mo>+</mo><mi>A</mi></mrow></math> collisions across the same energy range. Besides the dilepton channels resulting from interactions and decays of standard model particles (such as mesons and baryons), the PHSD has been extended to include the decay of hypothetical dark photons into dileptons, <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>U</mi><mo>→</mo><msup><mi>e</mi><mo>+</mo></msup><msup><mi>e</mi><mo>−</mo></msup></mrow></math>. The production of these dark photons occurs via Dalitz decays of <math xmlns="http://www.w3.org/1998/Math/MathML"><msup><mi>π</mi><mn>0</mn></msup><mo>,</mo><mo> </mo><mi>η</mi><mo>,</mo><mo> </mo><mi>ω</mi><mo>,</mo><mo> </mo><msup><mi>η</mi><mo>′</mo></msup></math>, and <math xmlns="http://www.w3.org/1998/Math/MathML"><mi mathvariant="normal">Δ</mi></math> resonances; direct decays of <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>ρ</mi><mo>,</mo><mo> </mo><mi>ω</mi></math>, and <math xmlns="http://www.w3.org/1998/Math/MathML"><mi>ϕ</mi></math>; the kaon mode <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msup><mi>K</mi><mo>+</mo></msup><mo>→</mo><msup><mi>π</mi><mo>+</mo></msup><mi>U</mi></mrow></math>; and thermal <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>q</mi><mover accent="true"><mi>q</mi><mo>¯</mo></mover></mrow></math> annihilation in the quark-gluon plasma. Our results show that high-precision measurements of dilepton spectra in heavy-ion collisions provide a sensitive and competitive probe of dark photons in the MeV to multi-GeV mass range. Furthermore, we quantify the experimental accuracy required to constrain the remaining viable parameter space of kinetic mixing in dark photon scenarios.

A.W. Romero Jorge

E. Bratkovskaya

T. Song

L. Sagunski

Constraints on kinetic mixing of dark photons from dilepton spectra

We present an updated deep neural network model for inclusive electron–carbon scattering. Using the bootstrap model [B. E. Kowal et al., <a href="http://dx.doi.org/10.1103/PhysRevC.110.025501">Phys. Rev. C 110, 025501 (2024)</a>], we incorporate recent experimental data, as well as older measurements in the deep inelastic scattering region, to derive a reoptimized posterior model. We examine the impact of these new inputs on model predictions and associated uncertainties. Finally, we evaluate the resulting cross-section predictions in the kinematic range relevant to the Hyper-Kamiokande and DUNE experiments.

Beata E. Kowal

Krzysztof M. Graczyk

Artur M. Ankowski

Rwik Dharmapal Banerjee

Jose L. Bonilla

Hemant Prasad

Jan T. Sobczyk

Re-optimization of a deep neural network model for electron-carbon scattering using new experimental data