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DOE OSTI2026

Monte Carlo Event Generation with Continuous Normalizing Flows

We apply continuous normalizing flows trained with the flow matching method to the problem of phase-space sampling in Monte Carlo event generation for high-energy collider physics. Focusing on lepton-pair and top-quark pair production with multiple jets, the two computationally most expensive processes at the Large Hadron Collider, we train helicity-conditioned continuous normalizing flows to remap the random numbers used in matrix element evaluation. Compared to standard methods, we achieve unweighting efficiency improvements by factors of up to 184 and 25 for the two processes at their respective highest jet number, at the cost of an increased evaluation time. When combining the advantages of continuous normalizing flows with the fast evaluation times of coupling-layer-based flows, using the RegFlow approach, we find parton-level unweighted event generation walltime gains of about a factor of 10 at the highest jet numbers. These substantial gains highlight the promise of samplers based on machine learning for next-generation collider experiments.

Bothmann, Enrico [CERN; Gottingen U.] (ORCID:00000↗

DOE OSTI2020

A Proposed Forward Silicon Tracker for the Future Electron-Ion Collider and Associated Physics Studies

The future Electron-Ion Collider (EIC) will explore several fundamental questions in a broad Bjorken-x (x BJ ) and Q 2 phase space. Heavy flavor and jet products are ideal probes to precisely study the tomography of nucleon/nuclei structure, help solve the proton spin puzzle and understand the hadronizaton processes in vacuum or in the QCD medium. Due to the asymmetric collisions at the EIC, most of the final state hadrons are produced in the nucleon/nuclei beam going (forward) direction. A silicon vertex/tracking is critical to precisely measure these forward hadrons at the EIC. Details of different conceptual designs of the proposed Forward Silicon Tracker (FST) and the relevant detector performance are presented in this technical note. The associated heavy flavor and jet studies with the evaluated FST performance are discussed as well.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Learning from many collider events at once

There have been a number of recent proposals to enhance the performance of machine learning strategies for collider physics by combining many distinct events into a single ensemble feature. To evaluate the efficacy of these proposals, we study the connection between single-event classifiers and multievent classifiers under the assumption that collider events are independent and identically distributed. We show how one can build optimal multievent classifiers from single-event classifiers, and we also show how to construct multievent classifiers such that they produce optimal single-event classifiers. This is illustrated for a Gaussian example as well as for classification tasks relevant for searches and measurements at the Large Hadron Collider. We extend our discussion to regression tasks by showing how they can be phrased in terms of parametrized classifiers. Empirically, we find that training a single-event (per-instance) classifier is more effective than training a multievent (per-ensemble) classifier, as least for the cases we studied, and we relate this fact to properties of the loss function gradient in the two cases. While we did not identify a clear benefit from using multievent classifiers in the collider context, we speculate on the potential value of these methods in cases involving only approximate independence, as relevant for jet substructure studies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

QCD Theory Meets Information Theory

We present a novel technique to incorporate precision calculations from quantum chromodynamics into fully differential particle-level Monte Carlo simulations. By minimizing an information-theoretic quantity subject to constraints, our reweighted Monte Carlo incorporates systematic uncertainties absent in individual Monte Carlo predictions, achieving consistency with the theory input in precision and its estimated systematic uncertainties. Our method can be applied to arbitrary observables known from precision calculations, including multiple observables simultaneously. It generates strictly positive weights, thus offering a clear path to statistically powerful and theoretically precise computations for current and future collider experiments. As a proof of concept, we apply our technique to event-shape observables at electron-positron colliders, leveraging existing precision calculations of thrust. Our analysis highlights the importance of logarithmic moments of event shapes, which have not been previously studied in the collider physics literature.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Report of the Instrumentation Frontier Working Group for Snowmass 2021

Detector instrumentation is at the heart of scientific discoveries. Cutting edge technologies enable US particle physics to play a leading role worldwide. This report summarizes the current status of instrumentation for High Energy Physics (HEP), the challenges and needs of future experiments and indicates high priority research areas. The Snowmass Instrumentation Frontier studies detector technologies and Research and Development (R&D) needed for future experiments in collider physics, neutrino physics, rare and precision physics and at the cosmic frontier. It is divided into more or less diagonal areas with some overlap among a few of them. We lay out five high-level key messages that are geared towards ensuring the health and competitiveness of the US detector instrumentation community, and thus the entire particle physics landscape.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Report of the Instrumentation Frontier Working Group for Snowmass 2021

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Smart pixel sensors Towards on-sensor filtering of pixel clusters with deep learning

High granularity silicon pixel sensors are at the heart of energy frontier particle physics collider experiments. At an collision rate of 40\,MHz, these detectors create massive amounts of data. Signal processing that handles data incoming at those rate and intelligently reduces the data within the pixelated region of the detector \textit{at rate} will enhance physics performance and enable physics analyses that are not currently possible. Using the shape of charge clusters deposited in an array of small pixels, the physical properties of the traversing particle can be extracted with locally customized neural networks. In this first work, we present a neural network that can be embedded into the on-sensor readout and filter out hits from low momentum tracks, reducing the detector's data volume by 54.4-75.4\%. The network is designed and simulated as a custom readout integrated circuit with 28\,nm CMOS technology and is expected to operate at less than 300\,$\mu W$ with an area of less than 0.2\,mm$^2$.

43 PARTICLE ACCELERATORS↗

DOE OSTI2026

Efficient many-jet event generation with flow matching

We apply for the first time, to the best of our knowledge, the flow matching method to the problem of phase-space sampling for event generation in high-energy collider physics. By training the model to remap the random numbers used to generate the momenta and helicities of the scattering matrix elements as implemented in the portable partonic event generator pepper, we find substantial efficiency improvements in the studied processes. We focus our study on the highest final-state multiplicities in Drell-Yan and top-antitop pair production used in simulated samples for the Large Hadron Collider, which computationally are the most relevant ones. We find that the unweighting efficiencies improve by factors of 184 and 25, respectively, when compared to the standard approach of using a vegas-based optimization. We also compare continuous normalizing flows trained with flow matching against the previously studied normalizing flows based on coupling layers and find that the former leads to better results, faster training and a better scaling behavior across the studied multiplicity range, while the latter evaluate faster. When combining the advantages of both methods using the regflow approach, we find parton-level unweighted event generation walltime gains of about a factor of 10 at the highest final-state multiplicities.

Bothmann, E. [CERN; Gottingen U.] (ORCID:000000016↗

DOE OSTI2025

Warm to cold correlations and other test results of the MQXFA Low-Beta Quadrupole magnets for the High-Luminosity Large Hadron Collider

The production of the MQXFA low-beta quadrupole magnets for the High Luminosity LHC (HL-LHC) is quite advanced with about 20 magnets cold tested in vertical cryostat. The MQXFA magnets are fabricated by the US HL-LHC Accelerator Upgrade Project (AUP) and are being used in the Q1 and Q3 Inner Triplet (IT) elements of the HL-LHC, whereas CERN is fabricating similar magnets for Q2a and Q2b IT elements. These large-aperture, high-gradient quadrupole magnets are first-of-the-kind accelerator magnets introducing Nb_3Sn conductor to High Energy Physics colliders. At this point of MQXFA production there is sufficient data to look at warm to cold correlations for the HL-LHC and for future use of Nb_3Sn magnets in particle accelerators. Examples of warm-cold correlations discussed in this paper are: field quality (including the use of magnetic shims for low-order harmonics correction), magnetic length, magnetic axis and magnetic field angle. A summary of main test data will also be presented and discussed.

Kurian, F. [Brookhaven]↗

DOE OSTI2024

Moment extraction using an unfolding protocol without binning

Deconvolving (“unfolding”) detector distortions is a critical step in the comparison of cross-section measurements with theoretical predictions in particle and nuclear physics. However, most existing approaches require histogram binning while many theoretical predictions are at the level of statistical moments. We develop a new approach to directly unfold distribution moments as a function of another observable without having to first discretize the data. Our moment unfolding technique uses machine learning and is inspired by Boltzmann weight factors and generative adversarial networks (GANs). We demonstrate the performance of this approach using jet substructure measurements in collider physics. With this illustrative example, we find that our moment unfolding protocol is more precise than bin-based approaches and is as or more precise than completely unbinned methods.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Transversely pumped laser driven particle accelerator

We present a novel acceleration scheme capable of accelerating electrons and ions in an underdense plasma. Transversely Pumped Acceleration (TPA) uses multiple arrays of counter-propagating laser beamlets that focus onto a central acceleration axis. Tuning the injection timing and the spacing between the adjacent beamlets allows for precise control over the position and velocity of the intersection point of the counter-propagating beam arrays. This results in an accelerating structure that propagates orthogonal to the direction of laser propagation. We present the theory that sets the injection timing of the incoming pulses to accelerate electrons and ions with a tunable phase velocity plasma wave. Simulation results are also presented which demonstrate 1.12 GeV proton beams accelerated in 3.6 mm of plasma and electron acceleration gradients on the order of 1 TeV/m in a scheme that circumvents dephasing. This work has potential applications as a compact accelerator for medical physics and high energy physics colliders.

Laser-produced plasmas↗

DOE OSTI2026

Neural Scaling Laws for Jet Generation

Recently observed empirical scaling laws describe the performance of foundation-type models as three independent key quantities -- dataset size, compute, and model parameters -- are modified. Extracting these scaling laws informs the training of large complex models for which the tuning of hyperparameters in traditional ways is not feasible. This work for the first time explores if scaling laws can also be observed for the task of particle jet generation -- both relevant as a pre-training objective for foundation models and as in-situ simulation by itself. We indeed replicate the key logarithmic scaling law behavior for model-size scaling. Beyond studying the next token prediction validation loss of the generative model, we also study the sliced Wasserstein distance of five physical quantities that are not immediately available to the model during training. Our study shows that this quantity is monotonically related to the next token prediction validation loss, meaning that this loss is indeed a good proxy for the physics performance. For the scaling with dataset size and compute, we observe substantially weaker scaling behavior of both the loss and the sliced Wasserstein distance. We analyze this behavior by introducing the concept of a learnable window, and argue that autoregressive next token prediction on jet constituents exhibits comparatively rapid saturation relative to language-model studies. We discuss possible origins of this behavior, including the stochastic nature of QCD radiation and differences between generative and supervised learning tasks in collider physics.

Amram, Oz [Fermilab]↗

DOE OSTI2023

Making digital objects FAIR in high energy physics: An implementation for Universal FeynRules Output (UFO) models

Research in the data-intensive discipline of high energy physics (HEP) often relies on domain-specific digital contents. Reproducibility of research relies on proper preservation of these digital objects. This paper reflects on the interpretation of principles of Findability, Accessibility, Interoperability, and Reusability (FAIR) in such context and demonstrates its implementation by describing the development of an end-to-end support infrastructure for preserving and accessing Universal FeynRules Output (UFO) models guided by the FAIR principles. UFO models are custom-made python libraries used by the HEP community for Monte Carlo simulation of collider physics events. Our framework provides simple but robust tools to preserve and access the UFO models and corresponding metadata in accordance with the FAIR principles.

Neubauer, Mark S.↗

DOE OSTI2023

Codebase release 2.0 for UFOManager

Neubauer, Mark S.↗

DOE OSTI2024

Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models

Abstract We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high energy physics (HEP) scientific data. This work provides a novel scheme to perform masked modeling based pre-training to learn permutation invariant functions on sets. More generally, this work provides a step towards building large foundation models for HEP that can be generically pre-trained with self-supervised learning and later fine-tuned for a variety of down-stream tasks. In MPM, particles in a set are masked and the training objective is to recover their identity, as defined by a discretized token representation of a pre-trained vector quantized variational autoencoder. We study the efficacy of the method in samples of high energy jets at collider physics experiments, including studies on the impact of discretization, permutation invariance, and ordering. We also study the fine-tuning capability of the model, showing that it can be adapted to tasks such as supervised and weakly supervised jet classification, and that the model can transfer efficiently with small fine-tuning data sets to new classes and new data domains.

Heinrich, Lukas (ORCID:0000000240487584)↗

DOE OSTI2020

Confronting Dark Matter with the Multiwavelength Sky (Final Technical Report)

The existence of dark matter is a key clue to new fundamental physics, yet its nature and non-gravitational interactions remain mysterious. In the course of this five-year project (spanning July 2015 - July 2020), we have developed novel analysis techniques to separate dark matter signals from astrophysical backgrounds in the inner Galaxy, and measured hitherto-unknown properties of the Fermi Bubbles and the Galactic Center excess in gamma-rays from the inner Milky Way. We have improved predictions for the spatial distribution and energy spectra of photon signals from dark matter, and set new limits from astrophysical and cosmological datasets on dark matter inhabiting complex dark sectors, multi-particle dark matter annihilation, and tiny primordial black holes. We have successfully adapted effective field theory techniques from collider physics to make high-precision predictions for signals from heavy weakly-interacting DM, allowing us to essentially exclude the possibility that DM is a thermal wino, and have discovered novel theoretical and observational implications for dark matter bound states and self-interactions. We have developed a comprehensive public code package for modeling the effects of exotic energy injections, such as from dark matter annihilation and decay, on the ionization and temperature history of the early cosmos, and have used these tools to set robust, powerful and broadly-applicable constraints on models of light dark matter.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

TESLA's High Gradient March: Thirty years anniversary of TESLA Technology Collaboration (TTC)

In 2020, the TTC (TESLA Technology Collaboration) celebrates 30 years of collaborative efforts on SRF technologies. The first international TESLA workshop on a TeV Energy Superconducting Linear Accelerator was held at Cornell University on July 23-26, 1990. The purpose of the meeting was to work on a parameter list for a superconducting high energy physics collider in the Teravolt energy region and to explore ideas on how to increase the gradients and lower the costs for the accelerator structures. It was clear from the beginning that the needed technological progress would require a large international collaboration. This meeting was indeed the origin of a long preparation effort toward a future ILC (International Linear Collider), which eventually led to the realization of the TTF (TESLA Test Facility, since 2005 operated as the DESY user facility FLASH) and the European XFEL also at DESY, and created the conditions supporting the proposals for several large SRF-based research facilities like SNS, LCLS-II, ESS, PIP-II and SHINE, as well as a growing number of smaller facilities around the world. Highlights of this 30-year international collaboration are presented and celebrated.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Elsa: enhanced latent spaces for improved collider simulations

Abstract Simulations play a key role for inference in collider physics. We explore various approaches for enhancing the precision of simulations using machine learning, including interventions at the end of the simulation chain (reweighting), at the beginning of the simulation chain (pre-processing), and connections between the end and beginning (latent space refinement). To clearly illustrate our approaches, we use W + jets matrix element surrogate simulations based on normalizing flows as a prototypical example. First, weights in the data space are derived using machine learning classifiers. Then, we pull back the data-space weights to the latent space to produce unweighted examples and employ the Latent Space Refinement ( Laser ) protocol using Hamiltonian Monte Carlo. An alternative approach is an augmented normalizing flow, which allows for different dimensions in the latent and target spaces. These methods are studied for various pre-processing strategies, including a new and general method for massive particles at hadron colliders that is a tweak on the widely-used RamboOnDiet mapping. We find that modified simulations can achieve sub-percent precision across a wide range of phase space.

Nachman, Benjamin (ORCID:0000000310240932)↗