Overview on Efforts for a Second Detector at the Electron-Ion Collider (EIC)
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73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
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Blueprints for Training Information Bottlenecks for Collider Analyses
Dimensionality reduction is a crucial aspect of data analysis in high energy physics, even if accompanied by information loss. Several methods, including histogram- and kernel-based analyses, are only computationally feasible for low-dimensional data. Furthermore, simulation models used in HEP can often only be validated for low-dimensional data. We provide several blueprints for using machine learning to create low-dimensional data representations (continuous event variables and discrete classification labels) for use in signal discovery and parameter estimation tasks. We also describe how to design the learned representation to facilitate a) searches with unknown model parameters and b) validation of simulation models in data control regions.
43 PARTICLE ACCELERATORS↗
CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters
High-precision calorimeter simulation at current and future colliders imposes rapidly growing computational demands, motivating the development of machine-learning surrogates for traditional Monte Carlo tools such as Geant4. Flow matching and diffusion-based generative models have become leading approaches for high-dimensional fast simulation because of their sample quality, but typically require ${\cal O}(100)$ function evaluations at inference and often rely on auxiliary networks to constrain global observables, compromising streamlined end-to-end generation. We introduce a unified framework that improves the balance between speed, shower quality, and physics fidelity. The method combines: (i) an average velocity field integrator that enables sampling in one or a few evaluations; (ii) a learned generative prior in shower space, constructed from data rather than random noise; and (iii) physics-guided loss terms that impose inductive biases on key observables during training. These elements are training time regularizers, preserving end-to-end inference with no additional cost. With only one or a few evaluation steps, the model achieves shower quality competitive with state-of-the-art flow and diffusion approaches, tested on several public high granularity calorimeter datasets. The results demonstrate inter-layer shower structure consistent with the underlying physics, providing a strong candidate for future fast simulation workflows.
Jiang, Cheng [Edinburgh U.]↗
RF Frontiers for Particle Physics, the US View
In this talk I provide an update on the RF research for future colliders in the U.S., through the prism of Snowmass and P5 report. During Snowmass, we considered various applications of RF technology to the proposed future colliders and other accelerator- and non-accelerator-based experiments. P5 narrowed down the choices of future machines. The colliders include circular and linear $e^+ e^-$ Higgs factories, and longer-term options such as muon and hadron high energy colliders. I will start with Snowmass and P5 recommendations. As it is impossible to cover all possible RF R&D topics, I will discuss only three critical topics relevant to future colliders: efficiency of RF power sources, cold normal conducting RF, and cavities for ionization channel of muon collider. Progress on SRF accelerating cavities for future colliders will be covered in a separate talk. The choice of topics reflects my preference and in some cases ignorance, which I think is inevitable when one tries to cover such a broad subject.
43 PARTICLE ACCELERATORS↗
Energy Independence of the Collins Asymmetry in 𝑝 ↑ 𝑝 Collisions
The STAR experiment reports new, high-precision measurements of the transverse single-spin asymmetries for 𝜋 ± within jets, namely the Collins asymmetries, from transversely polarized 𝑝 ↑ 𝑝 collisions at $\sqrt{s}$ = 510 GeV. The energy-scaled distribution of jet transverse momentum, 𝑥 T = 2𝑝 T, jet /$\sqrt{s}$, shows a remarkable consistency for Collins asymmetries of 𝜋 ± in jets between $\sqrt{s}$ = 200 GeV and 510 GeV. This indicates that the Collins asymmetries are nearly energy independent, with, at most, a very weak scale dependence in 𝑝 ↑ 𝑝 collisions. These results extend to high-momentum scales (𝑄 2 ≤ 3400 GeV 2 ) and enable unique tests of evolution and universality in the transverse-momentum-dependent formalism, thus providing important constraints for the Collins fragmentation functions.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Light right-handed smuons at the LHC: Natural dark matter and g μ − 2 in an unexplored realm of the phenomenological MSSM
We introduce an in-depth study of an unprobed phenomenological minimal supersymmetric Standard Model region offering a natural solution to dark matter. Since the 1980s this region has been referred to as the “bulk,” consisting of sub 200 GeV neutralinos and right-handed smuons. The bulk satisfies recent muon g μ − 2 measurements and sustains consistency with all presently operating SUSY experiments and LHC constraints. Initial ingress into the bulk will arrive soon via the LUX-ZEPLIN 1000-day experiment. The ATLAS Collaboration at the LHC has confirmed that observation of these light right-handed smuon events can occur at the ongoing LHC Run 3 and forthcoming High-Luminosity LHC. Moreover, the future FCC-ee and CEPC circular colliders should handily observe the events. Published by the American Physical Society 2025
Yin, Xiangwei (ORCID:0000000193823206)↗
Review of Particle Physics - Scalar Mesons below 1 GeV
The summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,717 new measurements from 869 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. Most of the 120 reviews are updated, including many that are heavily revised. The 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 (both volumes) is published online on the website of the Particle Data Group () and in a journal. Volume 1 is available in print as the . A 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. The 2024 edition of the Review of Particle Physics should be cited as: S. Navas et al. (Particle Data Group), Phys. Rev. D 110, 030001 (2024)© 20242024
Navas, S. [Universidad de Granada]↗
Interpreting and Accelerating Transformers for Jet Tagging
Attention-based transformers are ubiquitous in machine learning applications from natural language processing to computer vision. In high energy physics, one central application is to classify collimated particle showers in colliders based on the particle of origin, known as jet tagging. In this work, we study the interpretatbility and prospects for acceleration of Particle Transformer (ParT), a state-of-the-art model, leverages particle-level attention to improve jet-tagging performance. We analyzing ParT's attention maps and particle-pair correlations in the eta-phi plane, revealing intriguing features, such as a binary attention pattern that identifies critical substructure in jets. These insights enhance our understanding of the model's internal workings and learning process and hint at ways to improve its efficiency. Along these lines, we also explore low-rank attention, attention alternatives, and dynamic quantization to accelerate transformers for jet tagging. With quantization, we achieve a 50% reduction in model size and a 10% increase in inference speed without compromising accuracy. These combined efforts enhance both the performance and the interpretability of transformers in high-energy physics, opening avenues for more efficient and physics-driven model designs.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Coherent frequency combs from electrons colliding with a laser pulse
Highly coherent and powerful light sources capable of generating soft x-ray frequency combs are essential for high precision measurements and rigorous tests of fundamental physics. In this work, we derive the analytical conditions required for the emission of coherent radiation from an electron beam colliding with a laser pulse, modeled as a plane wave. These conditions are applied in a series of numerical simulations, where we show that a soft x-ray frequency comb can be produced if the electrons are regularly-spaced and sufficiently monoenergetic. High quality beams of this kind may be produced in the near future from laser-plasma interactions or linear accelerators. Furthermore, we highlight the advantageous role of employing few-cycle laser pulses in relaxing the stringent monoenergeticity requirements for coherent emission. The conditions derived here can also be used to optimize coherent emission in other frequency ranges, such as the terahertz domain.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Physics with high-luminosity proton-nucleus collisions at the LHC
The physics case for the operation of high-luminosity proton-nucleus (pA) collisions at the CERN LHC is reviewed. The collection of $\mathcal{O}$(1–10 pb −1 ) of proton-lead (pPb) collisions at the LHC will provide unique physics opportunities in a broad range of topics including proton and nuclear parton distribution functions (PDFs and nPDFs), generalised parton distributions (GPDs), transverse momentum dependent PDFs (TMDs), low-x quantum chromodynamics and parton saturation, hadron spectroscopy, baseline studies for quark-gluon plasma and parton collectivity, double and triple parton scatterings, photon–photon collisions, and physics beyond the Standard Model; which are not otherwise as clearly accessible by exploiting data from any other colliding system at the LHC. This report summarises the accelerator aspects of high-luminosity pA operation at the LHC, as well as each of the physics topics outlined above, including the relevant experimental measurements that motivate much larger pA datasets than collected to date.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
iDDS: intelligent distributed dispatch and scheduling for workflow orchestration
The intelligent distributed dispatch and scheduling (iDDS) service is a versatile workflow orchestration system designed for large-scale, distributed scientific computing. iDDS extends traditional workload and data management by integrating data-aware execution, conditional logic, and programmable workflows, enabling automation of complex and dynamic processing pipelines. Originally developed for the ATLAS experiment at the large hadron collider, iDDS has evolved into an experiment-agnostic platform that supports both template-driven workflows and a Function-as-a-Task model for Python-based orchestration. This paper presents the architecture and core components of iDDS, highlighting its scalability, modular message-driven design, and integration with systems such as PanDA and Rucio. We demonstrate its versatility through real-world use cases: fine-grained tape resource optimization for ATLAS, orchestration of large Directed Acyclic Graph (DAG) workflows for the Rubin Observatory, distributed hyperparameter optimization for machine learning applications, active learning for physics analyses, and AI-assisted detector design at the electron–ion collider. By unifying workload scheduling, data movement, and adaptive decision-making, iDDS reduces operational overhead and enables reproducible, high-throughput workflows across heterogeneous infrastructures. We conclude with current challenges and future directions, including interactive, cloud-native, and serverless workflow support.
97 MATHEMATICS AND COMPUTING↗
Electron Cooling in NICA Acceleration Complex
The Nuclotron-based Ion Collider fAcility (NICA) is under assembling at JINR. NICA will provide colliding beams for study of hot strongly interacting baryonic matter and spin physics. The NICA injection complex includes a tandem of 2 superconducting synchrotrons: Booster (up to 600 MeV/u) and Nuclotron (up 3.9 GeV/u fully stripped heavy ions). Since the start of injection complex commissioning in 2020 its four Runs were carried out. To the present time the beams of He, Fe, C and Xe were accelerated. Booster electron cooling was first used for cooling of continuous Fe¹⁴⁺ beam at the energy of 3.2 MeV/u in Run II. In the last Run we demonstrated longitudinal cooling of Xe²⁸⁺ in the presence of RF voltage at the Booster injection energy. This regime is required for accumulation in Booster the intensity required for Collider operation. The cooling enabled a 2 times intensity increase of slow extracted beam from Nuclotron. Optimization of Booster electron cooling for beam accumulation will be carried out in the next Run. Another (high voltage) electron cooling system will be used in the NICA Collider rings for ion accumulation in a barrier bucket and consecutive bunch formation.
43 PARTICLE ACCELERATORS↗
Enhancements and Deployment of the TDAQ System for the Mu2e Experiment
The Real Time Processing Systems Division at Fermilab has deployed new features to the Off-The-Shelf Data Acquisition framework (otsdaq) for the Mu2e experiment. The Mu2e experiment will search for the coherent neutrino-less conversion of a muon into an electron in the field of an aluminum nucleus with a sensitivity improvement of 10,000 times over existing limits. Such a charged lepton flavor-violating reaction probes new physics at a scale unavailable at present or planned high-energy colliders. The Mu2e Trigger and Data Acquisition (TDAQ) system uses otsdaq as its online Data Acquisition System (DAQ) framework. otsdaq integrates the artdaq and art frameworks for event transfer, filtering, and processing. otsdaq is a web-based DAQ software suite focusing on flexibility and scalability and provides a multi-user interface accessible through a web browser. artdaq handles the entire data stream, which is read over the peripheral component interconnect express (PCIe) bus to a software filter algorithm that selects events combined with the data flux coming from a cosmic-ray veto (CRV) system. Detector front-ends are configured through the PCIe bus by customized otsdaq plugins. The otsdaq slow controls infrastructure has been further developed using the experimental physics and industrial control system (EPICS) open-source platform for monitoring, controlling, alarming, and archiving. The detector control system (DCS) for Mu2e has been integrated into otsdaq. The production TDAQ and DCS system has been deployed at the experimental hall and is being debugged and optimized for experiment operations. We report on the feature enhancements and deployment of otsdaq for Mu2e.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Semi-supervised permutation invariant particle-level anomaly detection
The development of analysis methods to distinguish potential beyond the Standard Model phenomena in a model-agnostic way can significantly enhance the discovery reach in collider experiments. However, the typical machine learning (ML) algorithms employed for this task require fixed length and ordered inputs that break the natural permutation invariance in collision events. To address this, a semi-supervised anomaly detection tool is presented that takes a variable number of particle-level inputs and leverages a signal model to encode this information into a permutation invariant, event-level representation via supervised training with a Particle Flow Network (PFN). Data events are then encoded into this representation and given as input to an autoencoder for unsupervised ANomaly deTEction on particLe flOw latent sPacE (ANTELOPE), classifying anomalous events based on a low-level and permutation invariant input modeling. Performance of the ANTELOPE architecture is evaluated on simulated samples of hadronic processes in a high energy collider experiment, showing good capability to distinguish disparate models of new physics.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Foundation model framework for all tasks involving jet physics
Foundation models use large datasets to build an effective representation of data that can be deployed on diverse downstream tasks. Previous research developed the omnilearn foundation model for jet physics, using unique properties of particle physics, and showed that it could significantly advance discovery potential across collider experiments. This paper introduces a major upgrade, resulting in the omnilearned framework. This framework has three new elements: (1) updates to the model architecture and training, (2) using over 1 × 10 9 jets used for training, and (3) providing well-documented software for accessing all datasets and models. We demonstrate omnilearned with three representative tasks: top-quark jet tagging with the community delphes-based benchmark dataset, b tagging with ATLAS full simulation, and anomaly detection with CMS experimental data. In each case, omnilearned is the state of the art, further expanding the discovery potential of past, current, and future collider experiments.
Bhimji, Wahid [Lawrence Berkeley National Laborato↗
Advancements in Accelerator Physics and System Developments for the EIC
The development and enhancement of the Electron-Ion Collider (EIC) are founded on extensive research and development efforts aimed at achieving rigorous performance goals. This document outlines the crucial research and development (R&D) efforts that underpin the various systems and components of the EIC, including the polarized electron source and linear accelerator (linac) systems. Achieving the collider’s operational benchmarks relies on meticulously enhancing these components to optimize performance.
43 PARTICLE ACCELERATORS↗
Recent improvement of spallation cross section calculations, applicable to cosmic ray physics
About half of the cosmic ray nuclei heavier than helium have suffered nuclear transformations while colliding with the interstellar gas. The semiempirical calculations developed for calculating the still unmeasured cross sections had errors of about 25-50 per cent, depending on the target mass interval. Recent precise measurements have permitted significant improvements in the calculation of cross sections. Several such improvements are discussed. A status report on work-in-progress is presented on a general modification of the spallation equation parameters.
Silberberg, R.↗
Celeritas Midterm SciDAC Report
Celeritas is a new Monte Carlo (MC) code that helps satisfy the increasing demand for high energy physics (HEP) detector simulation, using Graphics Processing Unit (GPU) hardware on high performance computing (HPC) systems to model Large Hadron Collider (LHC) experiments and beyond. This report details the project’s progress midway through its SciDAC funding period, highlighting the first complete implementation of standard electromagnetic (EM) physics on GPUs, initial results for performance and scalability on Leadership Computing Facilities (LCFs), and preliminary integration into the CMS and ATLAS experiments. By integrating HEP domain knowledge with expertise in MC transport, Celeritas has catalyzed a shift in the HEP community’s perception of GPU platforms as the future for HPC simulations.
97 MATHEMATICS AND COMPUTING↗