We employ the Zubarev approach of the statistical operator to investigate the enhancement of the low-<math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>p</mi><mi>T</mi></msub></math> region of pion spectra, introducing an effective pion chemical potential, to describe the overpopulation of low-energy pion states. We test a corresponding freeze-out approach by analyzing the transverse-momentum spectra of identified particles measured recently with high precision by the ALICE Collaboration in <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>Pb</mi><mo>+</mo><mi>Pb</mi></mrow></math> collisions at CERN LHC. A blast-wave model and a blast-wave-based particle generator, coupled to a hadronic transport model, are utilized. Bayesian inference methods are applied to extract the most probable sets of thermodynamic parameters at the chemical freeze-out hypersurface. Both models for the overpopulated pion states, the hadronic transport model and the thermal model with a nonzero pion chemical potential, provide a satisfactory description of the observed pion spectra. However, both approaches contain approximations which can be improved within a systematic nonequilibrium approach. We demonstrate that the introduction of a nonequilibrium pion chemical potential offers an efficient alternative to the conventional explanation of the low-<math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>p</mi><mi>T</mi></msub></math> enhancement, typically attributed to resonance decays with subsequent thermalization. A similar discussion also holds for the kaon spectra.

Oleksandr Vitiuk

David Blaschke

Benjamin Dönigus

Gerd Röpke

Nonequilibrium phenomenology of identified particle spectra in heavy-ion collisions at energies available at the CERN Large Hadron Collider

Recently, a theoretical framework was set up in Miramontes et al. (Eur Phys J C 85:1055, 2025), which allows for the symmetry-preserving inclusion of full quark-gluon vertices in the description of the meson dynamics. In the present work, we develop a special truncation within this approach, which leads to a tractable set of functional equations that satisfy the fundamental chiral Ward-Takahashi identities. Specifically, in the chiral limit that we consider, this truncation allows us to simplify considerably the quark-gluon Schwinger–Dyson equation, without significant loss of quantitative accuracy. Importantly, this implies a substantial reduction of complexity of the renormalized Bethe–Salpeter equation: it is composed by a pair of one-loop diagrams that contain the full quark-gluon vertex, and a single two-loop diagram that is instrumental for the masslessness of the pion in the chiral limit. A detailed numerical analysis reveals that the incorporation of the aforementioned two-loop diagram is instrumental for the corresponding eigenvalue to reach unity. The key relation between the quark mass function and the pion wave function is shown to be satisfied to within the numerical precision of the loop integrals, which is at the level of about one percent or better. The field-theoretic ingredients required for the extension of this analysis beyond the chiral limit are briefly discussed.

China

M. Ferreira

Spain

A. Miramontes

J. Morgado

J. Papavassiliou

Germany

J. Pawlowski

Pion physics with dressed quark-gluon vertices

We investigate the collider signatures of a maximally flavor-violating $ U(1)_{L_\mu-L_\tau} $ model, where a new gauge boson $ Z^\prime $ and scalar triplets induce lepton flavor-changing interactions in the $\textit{μ}$-$\textit{τ}$ sector. Focusing on four-lepton final states at multi-TeV lepton colliders, we conduct a detailed analysis of cross sections, asymmetries, and polarization effects. We show that the signal cross section is highly sensitive to $ m_{Z^\prime} $ and the effective parameters $ \tilde{g}/m_{Z^\prime} $ , whereas it remains largely insensitive to the triplet Yukawa couplings within the phenomenologically allowed region. The forward-backward asymmetry exhibits a characteristic monotonic dependence on $ m_{Z^\prime} $ , and beam polarization can significantly suppress the Standard Model backgrounds while enhancing new physics contributions. We find that, in the phenomenologically allowed parameter space, the predicted observables remain highly sensitive to the underlying model parameters. These results show that multi-lepton final states are powerful probes of the $ U(1)_{L\mu - L_\tau} $ framework and can offer valuable guidance for future searches at muon and electron-positron colliders.

Jianing Qin

Fei Huang

Honglei Li

Zhi-Long Han

Jin Sun

Collider probes of four-lepton final states in maximally flavor-violating ${U(1)_{L_\mu-L_\tau}} $ model * Supported by National Natural Science Foundation of China (12505112, 11805081), Shandong Province Natural Science Foundation (ZR2024QA138, ZR2022MA056) and State Key Laboratory of Dark Matter Physics, University of Jinan Disciplinary Cross-Convergence Construction Project 2024 (XKJC- 202404) and Jin Sun is supported by IBS under the project code, (IBS-R018-D1)

We present an application of physics-informed neural networks (PINNs) to the study of <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><mi>SU</mi><mo stretchy="false">(</mo><msub><mi>N</mi><mi>c</mi></msub><mo stretchy="false">)</mo></math> lattice gauge theories. Our method enables the learning of eigenfunctions and eigenvalues at arbitrary gauge couplings, smoothly moving from the analytically known strong-coupling regime toward weaker couplings. By encoding the Schrödinger equation and the symmetries of the eigenstates directly into the loss function, the network performs an unsupervised exploration of the spectrum. We validate the approach on the single-plaquette U(1) and SU(2) pure-gauge theories, showing that the PINNs successfully reproduce the hierarchy of energy levels and their corresponding wave functions.

Switzerland

Simone Romiti

<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><mrow><mi>SU</mi><mo stretchy="false">(</mo><mi>N</mi><mo stretchy="false">)</mo></mrow></math> lattice gauge theories with physics-informed neural networks

We present a deep learning pipeline to perform a model-independent, likelihood-free search for anomalous (i.e., non-background) events in the proposed next-generation multi-ton scale liquid xenon-based direct detection experiment, DARWIN. We train an anomaly detector comprising a variational autoencoder (VAE) and a classifier on high-dimensional simulated detector response data and construct a 1D anomaly score to reject the background-only hypothesis in the presence of an excess of non-background-like events. We use simulated validation data to determine the power of the method to reject the background-only hypothesis in the presence of a WIMP dark matter signal, without any model-dependent assumption about the nature of the signal. We show that our neural networks learn relevant features of the events from low-level, high-dimensional detector outputs, avoiding lossy and computationally expensive compression into lower-dimensional observables. Our approach is complementary to the usual likelihood-based analysis, in that it reduces the reliance on many of the corrections and cuts that are traditionally part of the analysis chain, with the potential of achieving higher accuracy and significant reduction of analysis time. We envisage the methodology presented in this work augmenting or complementing likelihood-based and other data-driven methods currently utilized in the DARWIN (and in the future, XLZD) analysis pipeline.

Netherlands

J. Aalbers

Japan

K. Abe

M. Adrover

France

S. Maouloud

L. Althueser

United States

D. Amaral

B. Andrieu

Italy

E. Angelino

D. Antón Martin

Serbia

Bosnia and Herzegovina

B. Antunovic

E. Aprile

M. Babicz

D. Bajpai

M. Balzer

Australia

E. Barberio

L. Baudis

M. Bazyk

N. Bell

L. Bellagamba

R. Biondi

Y. Biondi

A. Bismark

C. Boehm

K. Boese

R. Braun

Israel

A. Breskin

S. Brommer

United Kingdom

A. Brown

G. Bruni

R. Budnik

C. Cai

C. Capelli

A. Chauvin

A. Chavez

A. Colijn

Sweden

J. Conrad

J. Cuenca-García

V. D’Andrea

L. Garcia

M. Decowski

A. Deisting

C. Donato

P. Gangi

S. Diglio

M. Doerenkamp

G. Drexlin

K. Eitel

A. Elykov

R. Engel

A. Ferella

C. Ferrari

H. Fischer

T. Flehmke

M. Flierman

K. Fujikawa

W. Fulgione

C. Fuselli

P. Gaemers

R. Gaior

M. Galloway

F. Gao

N. Garroum

R. Giacomobono

F. Girard

R. Glade-Beucke

F. Glück

L. Grandi

J. Grigat

R. Größle

H. Guan

M. Guida

P. Gyorgy

R. Hammann

V. Hannen

S. Hansmann-Menzemer

N. Hargittai

A. Higuera

C. Hils

K. Hiraoka

L. Hoetzsch

N. Hood

M. Iacovacci

Y. Itow

J. Jakob

R. James

F. Joerg

F. Kahlert

Y. Kaminaga

M. Kara

P. Kavrigin

S. Kazama

M. Keller

P. Kharbanda

B. Kilminster

M. Kleifges

M. Klute

M. Kobayashi

D. Koke

A. Kopec

B. Krosigk

Model-independent searches of new physics in DARWIN with deep learning

The recent low-energy electron recoil (ER) results reported by the LUX-ZEPLIN (LZ) experiment have established the most stringent constraints to date on new physics scenarios, specifically for solar axion-like particles with keV-scale masses and mirror dark matter. Motivated by this enhanced sensitivity and the resulting restrictive limits, our present work focuses on probing light mediator models via elastic neutrino-electron scattering induced by solar neutrinos. We specifically consider two broad classes of new physics scenarios; (i) universal light mediator models consistent with Lorentz invariance, including scalar, vector, and tensor interactions, and (ii) anomaly-free leptophilic <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><mi>U</mi><mo stretchy="false">(</mo><mn>1</mn><msup><mo stretchy="false">)</mo><mo>′</mo></msup></math> gauge extensions featuring a new vector mediator associated with the <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><msub><mi>L</mi><mi>e</mi></msub><mo>−</mo><msub><mi>L</mi><mi>μ</mi></msub></math>, <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><msub><mi>L</mi><mi>e</mi></msub><mo>−</mo><msub><mi>L</mi><mi>τ</mi></msub></math>, <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><msub><mi>L</mi><mi>μ</mi></msub><mo>−</mo><msub><mi>L</mi><mi>τ</mi></msub></math>, and <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><msub><mi>L</mi><mi>e</mi></msub><mo>+</mo><mn>2</mn><msub><mi>L</mi><mi>μ</mi></msub><mo>+</mo><mn>2</mn><msub><mi>L</mi><mi>τ</mi></msub></math> symmetries. By incorporating contributions from these interactions into the neutrino-electron scattering cross section and utilizing the most precise solar neutrino flux predictions, we analyze the latest LZ ER datasets. We report novel constraints on the coupling-mass parameter space for these models. Furthermore, we contextualize our findings by comparing them with established bounds from various laboratory, cosmological, and astrophysical sources. Our analysis demonstrates that LZ data provide significantly improved limits in previously unconstrained regions of the parameter space.

Türkiye

Mehmet Demirci

M. Fauzi Mustamin

Solar neutrino probes of light new physics: Updated limits from LUX-ZEPLIN experiment

Deep learning has achieved remarkable success in jet classification tasks, yet a key challenge remains: understanding what these models learn and how their features relate to known QCD observables. Improving interpretability is essential for building robust and trustworthy machine learning tools in collider physics. To address this challenge, we systematically investigate equivariant and IRC-safe graph neural networks for jet classification. Using simulated jet datasets, we compare IRC-safe architectures with inbuilt E(2) and O(2) equivariance in the rapidity-azimuth plane against IRC-safe and -unsafe baselines in terms of classification performance, robustness to soft emissions, and latent representation structures. Our analysis shows that IRC-safe and symmetry-aware networks are more stable across training instances and distribute their latent variance across multiple interpretable directions. By regressing Energy Flow Polynomials onto the leading principal components, we establish a direct correspondence between learned representations and established IRC-safe jet observables. These results demonstrate that embedding symmetry and safety constraints not only improves robustness but also grounds network representations in known QCD structures, providing a principled approach toward interpretable deep learning in collider physics.

India

Partha Konar

Vishal Ngairangbam

Michael Spannowsky

Deepanshu Srivastava

Stable and interpretable jet physics with IRC-safe equivariant feature extraction

Graph neural networks have recently shown strong promise for event reconstruction tasks in Liquid Argon Time Projection Chambers, yet their performance remains limited for underrepresented classes of particles, such as Michel electrons. In this work, we investigate physics-informed strategies to improve semantic segmentation within the NuGraph2 architecture. We explore three complementary approaches: (i) enriching the input representation with context-aware features derived from detector geometry and track continuity, (ii) introducing auxiliary decoders to capture class-level correlations, and (iii) incorporating energy-based regularization terms motivated by Michel electron energy distributions. Experiments on MicroBooNE public datasets show that physics-inspired feature augmentation yields the largest gains, particularly boosting Michel electron precision and recall by disentangling overlapping latent space regions. In contrast, auxiliary decoders and energy-regularization terms provided limited improvements, partly due to the hit-level nature of NuGraph2, which lacks explicit particle- or event-level representations. Our findings highlight that embedding physics context directly into node-level inputs is more effective than imposing task-specific auxiliary losses, and suggest that future hierarchical architectures such as NuGraph3, with explicit particle- and event-level reasoning, will provide a more natural setting for advanced decoders and physics-based regularization. The code for this work is publicly available on Github at .

Vitor Grizzi

M. Voetberg

V. Hewes

Giuseppe Cerati

Hadi Meidani

NuGraph2 with context-aware inputs: physics-inspired improvements in semantic segmentation

We develop diffusion models for simulating lattice gauge theories, where stochastic quantization is explicitly incorporated as a physical condition for sampling. We demonstrate the applicability of this novel sampler to U(1) gauge theory in two spacetime dimensions and find that a model trained at a small inverse coupling constant can be extrapolated to larger inverse coupling regions without encountering the topological freezing problem. Additionally, the trained model can be employed to sample configurations on different lattice sizes without requiring further training. The exactness of the generated samples is ensured by incorporating Metropolis-adjusted Langevin dynamics into the generation process. Furthermore, we demonstrate that this approach enables more efficient sampling of topological quantities compared to traditional algorithms such as Hybrid Monte Carlo and Langevin simulations.

Qianteng Zhu

Gert Aarts

Wei Wang

Kai Zhou

Lingxiao Wang

Physics-conditioned diffusion models for lattice gauge theory

Extended Higgs sectors are often introduced to explain phenomena beyond the standard model (BSM). The existence of multiple scalar fields may cause the Landau pole below the Planck scale. In this case, the low-energy theory may be replaced by an asymptotic-free gauge theory. In this paper, we consider an SU(2) gauge theory with confinement as such an ultraviolet theory of the extended Higgs sectors. We investigate the relation between scalar particle contents at the low energy and group theoretical properties of fundamental fermions of the gauge theory. We find that particle contents of various extended Higgs sectors previously proposed to explain the BSM problems are deduced by each charge assignment of flavor symmetry of the fundamental fermions of the SU(2) gauge symmetry. Our findings may provide a new picture for the ultraviolet completion of the extended Higgs sectors.

Shinya Kanemura

Yushi Mura

Tetsuo Shindou

Classification of Higgs sectors from group theoretical properties of UV gauge theories