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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Quantum Algorithms for Collider Physics (Final Report)

The goal of this project was to unite powerful analysis techniques in high-energy physics (HEP) with cutting-edge advances in quantum information science (QIS). Almost every event at the Large Hadron Collider (LHC) involves jets, collimated sprays of particles that are copiously produced in proton-proton collisions. Current LHC algorithms to identify and classify jets are constrained by the limits of classical computation. Quantum algorithms could fundamentally change how collider data is analyzed, by speeding up existing classical algorithms and by enabling new quantum jet representations. By exploiting the capabilities of quantum computation, this research confronted the challenge of data analysis in collider physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Studies of the weak interaction in atomic systems: towards measurements of atomic parity non-conservation in francium

Abstract Tests of the standard model of particle physics should be carried out over the widest possible range of energies. Here we present our plans and progress for an atomic parity non-conservation experiment using the heaviest alkali, francium ( Z = 87), which has no stable isotope. Low-energy tests of this kind have sensitivity complementary to higher energy searches, e.g. at the large hadron collider.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Novel cross section ratios as possible signals of saturation in ultraperipheral collisions

We propose new cross section ratios in ultraperipheral A + A and p + A collisions (UPCs) as a potential experimental signal of saturation physics. We consider the ratio R 1 of elastic vector meson photoproduction cross section to the inclusive hadron or jet photoproduction cross section. The ratio can be measured in the γ + A and γ + p collisions taking place in the UPCs. We label the ratios R 1 ( A ) and R 1 ( p ) , respectively. Constructing the double ratio R UPC ( A ) = R 1 ( A ) / R 1 ( p ) , and performing a small- x calculation both in the quasiclassical approximation and by including small- x evolution, we observe that R UPC ( A ) exhibits a markedly different dependence on the nuclear atomic number inside and outside the saturation region. This result indicates that R UPC ( A ) and R 1 ( A ) measurements in UPCs may help in the experimental searches for the evidence of saturation physics. Published by the American Physical Society 2024

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Toward an event-level analysis of hadron structure using differential programming

Reconstructing the internal properties of hadrons in terms of fundamental quark and gluon de- grees of freedom is a central goal in nuclear and particle physics. This effort lies at the core of major experimental programs, such as the Jefferson Lab 12 GeV program and the upcoming Electron-Ion Collider. A primary challenge is the inherent inverse problem: converting large-scale observational data from collision events into the fundamental QCD-defined densities that characterize the micro- scopic structure of hadronic systems. Recent advances in AI and machine learning have opened new avenues for addressing this challenge using deep learning techniques. A particularly promising direction is the integration of complex theoretical calculations and experimental simulations into a unified framework capable of reconstructing these densities directly from event-level information. In this document, we introduce a key algorithm called LOITS, which enables differentiable program- ming within such a framework, facilitating the use of AI/ML techniques to solve the inverse problem of QCF reconstruction at the event level.

Braga, Kevin [College of William and Mary, William↗
DOE OSTI2020

Detector and Physics Performance at a Muon Collider

A muon collider represents the ideal machine to reach very high center-of-mass energies and luminosities by colliding elementary particles. This is the result of the low level of beamstrahlung and synchrotron radiation compared to linear or circular electron-positron colliders. In contrast with other lepton machines, the design of a detector for a multi-TeV muon collider requires detailed knowledge of the interaction region due to the significant backgrounds created by muon beam decays in the collider ring. The physics reach can be properly evaluated only when the detector performance in such an environment is determined. In this work, the backgrounds generated by muon beams of 750 GeV are characterized and the performance of the tracking system and the calorimeter detector is illustrated. Solutions to minimize the effect of the beam-induced backgrounds are discussed and applied to obtain track and jet reconstruction performance. The μ + μ – → $Hν\bar{ν} → b\bar{b} ν\bar{ν}$ process is fully simulated and reconstructed to demonstrate that physics measurements are possible in this harsh environment. The measurement precision for the Higgs boson coupling to $b\bar{b}$ is evaluated for $\sqrt{s}$=1.5, 3, and 10 TeV and compared to other proposed machines.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network

The ATLAS experiment at the Large Hadron Collider explores the use of modern neural networks for a multi-dimensional calibration of its calorimeter signal defined by clusters of topologically connected cells (topo-clusters). The Bayesian neural network (BNN) approach not only yields a continuous and smooth calibration function that improves performance relative to the standard calibration but also provides uncertainties on the calibrated energies for each topo-cluster. The results obtained by using a trained BNN are compared to the standard local hadronic calibration and to a calibration provided by training a deep neural network. The uncertainties predicted by the BNN are interpreted in the context of a fractional contribution to the systematic uncertainties of the trained calibration. They are also compared to uncertainty predictions obtained from an alternative estimator employing repulsive ensembles.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Anisotropic flow and flow fluctuations of identified hadrons in Pb–Pb collisions at $ \sqrt{s_{\textrm{NN}}}$ = 5.02 TeV

The first measurements of elliptic flow of π ± , K ± , p+$\bar{p}$, K$_{S}^{0}$, Λ+$\bar{Λ}$, $\phi$, Ξ – +$\barΞ$ + , and Ω – +$\barΩ$ + using multiparticle cumulants in Pb–Pb collisions at $\sqrt{s_{NN}}$= 5.02 TeV are resented. Results obtained with two- (v 2 {2}) and four-particle cumulants (v 2 {4}) are shown as a function of transverse momentum, p T , for various collision centrality intervals. Combining the data for both v 2 {2} and v 2 {4} also allows us to report the first measurements of the mean elliptic flow, elliptic flow fluctuations, and relative elliptic flow fluctuations for various hadron species. These observables probe the event-by-event eccentricity fluctuations in the initial state and the contributions from the dynamic evolution of the expanding quark–gluon plasma. The characteristic features observed in previous p T -differential anisotropic flow measurements for identified hadrons with two-particle correlations, namely the mass ordering at low p T and the approximate scaling with the number of constituent quarks at intermediate p T , are similarly present in the four-particle correlations and the combinations of v 2 {2} and v 2 {4}. In addition, a particle species dependence of flow fluctuations is observed that could indicate a significant contribution from final state hadronic interactions. The comparison between experimental measurements and CoLBT model calculations, which combine the various physics processes of hydrodynamics, quark coalescence, and jet fragmentation, illustrates their importance over a wide p T range.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Fundamental limit of jet tagging

Identifying the origin of high-energy hadronic jets (jet tagging) has been a critical benchmark problem for machine learning in particle physics. Jets are ubiquitous at colliders and are complex objects that serve as prototypical examples of collections of particles to be categorized. Over the last decade, machine learning-based classifiers have replaced classical observables as the state of the art in jet tagging. Increasingly complex machine learning models are leading to increasingly more effective tagger performance. Our goal is to address the question of convergence—are we getting close to the fundamental limit on jet tagging or is there still potential for computational, statistical, and physical insights for further improvements? We address this question using state-of-the-art generative models to create a realistic, synthetic dataset with a known jet tagging optimum. Various state-of-the-art taggers are deployed on this dataset, showing that there is a significant gap between their performance and the optimum. Our dataset and software are made public to provide a benchmark task for future developments in jet tagging and other areas of particle physics.

Artificial intelligence↗
DOE OSTI2024

Beam dynamics driven design of powerful energy recovery linac for experiments

Powerful ERL for experiments (PERLE) is a novel energy recovery linac (ERL) test facility [1], designed to validate choices for a 50 GeV ERL foreseen in the design of the Large Hadron Electron Collider and the Future Circular Collider and to host dedicated nuclear and particle physics experiments. Its main goal is to demonstrate the high current, continuous wave, multipass operation with superconducting cavities at 802 MHz. With very high beam power (10 MW), PERLE offers an opportunity for controllable study of every beam dynamic effect of interest in the next generation of ERLs and becomes a “stepping stone” between the present state-of-the-art 1 MW ERLs and the future 100 MW scale applications. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Bayesian inference analysis of jet quenching using inclusive jet and hadron suppression measurements

The JETSCAPE 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 JETSCAPE Bayesian inference determination of $\hat{q}$, which was based solely on a selection of inclusive hadron suppression data. jetscape 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.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Foundations of automatic feature extraction at LHC–point clouds and graphs

Abstract Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron Collider (LHC), helping bolster various fronts ranging from fast and accurate detector simulations to physics analysis probing possible deviations from the Standard Model. The game-changing feature of these new algorithms is the ability to extract relevant information from high-dimensional input spaces, often regarded as “replacing the expert” in designing physics-intuitive variables. While this may seem true at first glance, it is far from reality. Existing research shows that physics-inspired feature extractors have many advantages beyond improving the qualitative understanding of the extracted features. In this review, we systematically explore automatic feature extraction from a phenomenological viewpoint and the motivation for physics-inspired architectures. We also discuss how prior knowledge from physics results in the naturalness of the point cloud representation and discuss graph-based applications to LHC phenomenology.

Bhardwaj, Akanksha↗
DOE OSTI2026

Searching for the Most Harmful Field Errors in the HSR IR Superconducting Magnets

In this project, we improve beam stability for the Electron-Ion Collider. Magnetic field errors can reduce beam stability, making it essential to identify the field errors that have the greatest impact on accelerator performance. However, this is particularly challenging because beam stability depends on the complex interactions of many magnetic field errors, resulting in a high-dimensional and nonlinear optimization problem. We determine which field errors are the most influential for the large physical aperture superconducting magnet B2PF, a critical magnet in the Interaction Region (IR) in the Hadron Storage Ring (HSR). We complete and analyze nearly 30,000 simulations on the Brookhaven National Laboratory Linux Cluster by varying 18 nonlinear magnetic field errors. We evaluate beam stability using the dynamic aperture and the tune diffusion. We identify the field errors that most strongly influence beam stability and establish quantitative field error tolerances that improve accelerator performance.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Hadron-argon Cross Section Measurements in ProtoDUNE

Modern accelerator-based neutrino experiments use complex nuclei, such as argon, as neutrinotargets that rely on nuclear models to unfold the reconstructed neutrino energy to the true neu-trino energy. The nuclear effects complicate the neutrino oscillation measurements and are notwell-understood, and there are very limited measurements of hadron cross sections on argon.ProtoDUNE-SP, a prototype liquid argon time projection chamber (LArTPC) for the Deep Un-derground Neutrino Experiment (DUNE) far detector, collected data from a hadronic test beamat CERN in 2018, including protons, pions, and kaons in the range 1 to 7 GeV/c. In this talk, wewill present the status and results of the many hadron-argon cross section analyses.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Quantum fluctuations of quarks and gluons in nuclei

Acceptance talk for the 2024 Kenneth G. Wilson Award for Excellence in Lattice Field Theory: For key contributions to lattice QCD studies of noise reduction in nuclear systems, the structure of nuclei, and transverse-momentum dependent hadronic structure functions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

US ATLAS and US CMS HPC and Cloud Blueprint

The Large Hadron Collider (LHC) at CERN houses two general purpose detectors - ATLAS and CMS - which conduct physics programs over multi-year runs to generate increasingly precise and extensive datasets. The efforts of the CMS and ATLAS collaborations lead to the discovery of the Higgs boson, a fundamental particle that gives mass to other particles, representing a monumental achievement in the field of particle physics that was recognized with the awarding of the Nobel Prize in Physics in 2013 to François Englert and Peter Higgs. These collaborations continue to analyze data from the LHC and are preparing for the high luminosity data taking phase at the end of the decade. The computing models of these detectors rely on a distributed processing grid hosted by more than 150 associated universities and laboratories worldwide. However, such new data will require a significant expansion of the existing computing infrastructure. To address this, both collaborations have been working for years on integrating High Performance Computers (HPC) and commercial cloud resources into their infrastructure and continue to assess the potential role of such resources in order to cope with the demands of the new high luminosity era. US ATLAS and US CMS computing management have charged the authors to provide a blueprint document looking at current and possibly future use of HPC and Cloud resources, outlining integration models, possibilities, challenges and costs. The document will address key questions such as the optimal use of resources for the experiments and funding agencies, the main obstacles that need to be overcome for resource adoption, and areas that require more attention.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗