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At least 19 records

Reducing systematic bias in machine learning applications to J/ψ signal extraction in high-energy nuclear physics

Machine learning techniques are increasingly used in high-energy nuclear physics because they can exploit multivariate correlations more efficiently than conventional cut-based analyses. A central challenge is the construction of training samples that faithfully reproduce the detector response observed in data. Signal samples are usually derived from detector simulations; therefore, mismatches between simulation and data can degrade classifier performance and introduce systematic biases. This work presents two practical correction procedures, namely cumulative distribution function (CDF) mapping and a shift-and-scale transformation, to align simulated signal features with those measured in data. Their performance is demonstrated with $J$/$\psi$ yield measurements in $\sqrt{s_{nn}}$ = 200 GeV Ru+Ru and Zr+Zr collisions recorded by STAR. A set of self-consistency tests shows that these procedures substantially suppress the systematic bias associated with data-simulation discrepancies in machine-learning-based signal extraction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Data-flow parallelism for high-energy and nuclear physics computing frameworks

The processing tasks of a scientific workflow in high-energy and nuclear physics (HENP) can typically be represented as a directed acyclic graph formed according to the data flow—i.e. the data dependencies among algorithms executed as part of the workflow. With this representation, an HENP computing framework can optimally execute a workflow, exploiting the parallelism inherent among independent tasks. Despite such a natural description of a workflow, most HENP frameworks do not make use of technologies that provide concurrent execution of graph-based tasking structures. In this session, we describe Fermilab efforts to adopt a graph-based technology (specifically Intel’s oneTBB flow graph) for meeting the framework needs of its experiments, notably DUNE. After introducing the physics DUNE intends to explore, we will show that all common processing idioms supported by current HENP frameworks can naturally be supported by oneTBB’s data-flow technology, optimally leveraging the concurrent capabilities of the machine. In addition, we discuss collaborative efforts between Fermilab and the Intel oneTBB development team, who is considering improvements to the flow-graph technology to better support HENP use cases.

43 PARTICLE ACCELERATORS↗

Data-flow parallelism for high-energy and nuclear physics frameworks

The processing tasks of an event-processing workflow in high-energy and nuclear physics (HENP) can typically be represented as a directed acyclic graph formed according to the data flow—i.e. the data dependencies among algorithms executed as part of the workflow. With this representation, an HENP framework can optimally execute a workflow, exploiting the parallelism inherent among independent tasks. Despite such a natural description of a workflow, most HENP frameworks do not make use of technologies that provide concurrent execution of graph-based tasking structures. In this talk, we describe Fermilab efforts to adopt a graph-based technology (specifically Intel’s oneTBB flow graph) for meeting the framework needs of its experiments, notably DUNE. Building on the Meld project as presented at CHEP2023, we demonstrate that all common processing idioms supported by current frameworks can naturally be supported by oneTBB’s data-flow technology, optimally leveraging the concurrent capabilities of the machine. In addition, we discuss collaborative efforts between Fermilab and the Intel oneTBB development team, who is considering improvements to the flow-graph technology to better support HENP use cases.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Gain characterization of LGAD sensors with beta particles and 28-MeV protons

Low Gain Avalanche Diodes, also known as LGADs, are widely considered for fast-timing applications in high energy physics, nuclear physics, space science, medical imaging, and precision measurements of rare processes. Such devices are silicon-based and feature an intrinsic gain due to a p + -doped layer that allows the production of a controlled avalanche of carriers, with multiplication on the order of 10–100. This technology can provide time resolution on the order of 20–30 ps, and variants of this technology can provide precision tracking too. The characterization of LGAD performance has so far primarily been focused on the interaction of minimum ionizing particles for high energy and nuclear physics applications. This article expands the study of LGAD performance to highly-ionizing particles, such as 28-MeV protons, which are relevant for several future scientific applications, e.g. in biology and medical physics, among others. These studies were performed with a beam of 28-MeV protons from a tandem Van de Graaff accelerator at Brookhaven National Laboratory and beta particles from a ^90Sr source; these were used to characterize the response and the gain of an LGAD as a function of bias voltage and collected charge. Here, the experimental results are also compared to TCAD simulations.

47 OTHER INSTRUMENTATION↗

Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder

Real-time data collection and analysis in large experimental facilities present a great challenge across multiple domains, including high energy physics, nuclear physics, and cosmology. To address this, machine learning (ML)-based methods for real-time data compression have drawn significant attention. However, unlike natural image data, such as CIFAR and ImageNet that are relatively small-sized and continuous, scientific data often come in as three-dimensional 3D data volumes at high rates with high sparsity (many zeros) and non-Gaussian value distribution. This makes direct application of popular ML compression methods, as well as conventional data compression methods, suboptimal. To address these obstacles, this work introduces a dual-head autoencoder to resolve sparsity and regression simultaneously, called Bicephalous Convolutional AutoEncoder (BCAE). This method shows advantages both in compression fidelity and ratio compared to traditional data compression methods, such as MGARD, SZ, and ZFP. To achieve similar fidelity, the best performer among the traditional methods can reach only half the compression ratio of BCAE. Moreover, a thorough ablation study of the BCAE method shows that a dedicated segmentation decoder improves the reconstruction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Big PanDa Workflow Management on Titan for High Energy and Nuclear Physics and for Future Extreme Scale Scientific Application

Over a three year period, from 2016-2019, this project demonstrated the scientific benefits of integrating the Titan supercomputer at Oak Ridge Leadership Computing Facility into traditional high throughput grid based distributed computing systems managed by PanDA, the workflow management system used for the execution of all distributed computing applications by the ATLAS experiment at the Large Hadron Collider. PanDA manages millions of batch jobs daily at hundreds of clusters worldwide on request by thousands of physicist users, and processes more than an exabyte of data annually using grid middleware. High levels of operational use of Titan was sustained by PanDA in order to meet the physics goals of ATLAS. The success of this project led to the use of other supercomputers worldwide by ATLAS, and to the adoption of PanDA by other experiments and other scientists. Multiple innovative operational and computer science research goals were achieved supporting the use of supercomputers for scientific domains with large scale distributed data and distributed processing needs.

97 MATHEMATICS AND COMPUTING↗

A 500 KV INVERTED GEOEMTRY FEEDTHROUGH FOR A HIGH VOLT-AGE DC ELECTRON GUN

The Continuous Electron Beam Accelerator Facility injector at Jefferson Lab (JLab) utilizes an inverted-geometry ceramic insulator photogun operating at 130 kV direct current to generate spin-polarized electron beams for high-energy nuclear physics experiments. A second photogun delivers 180 keV beam for commissioning a SRF booster in a testbed accelerator, and a larger version delivers 300 keV magnetized beam in a test stand beam line. This contribution reports on the development of an unprecedented inverted-insulator with cable connector for reliably applying 500 kV DC to a future polarized beam photogun, to be designed for operating at 350 kV without field emission. Such a photogun design could then be used for generating a polarized electron beam to drive a spin-polarized positron source as a demonstrator for high energy nuclear physics at JLab. There are no commercial cable connectors that fit the large inverted insulators required for that voltage range. Our proposed concept is based on a modified epoxy receptacle with intervening SF6 layer and a test electrode in a vacuum vessel.

Hernandez-Garcia, C.↗

HIGH VOLTAGE DESIGN AND EVALUATION OF WIEN FILTERS FOR THE CEBAF 200 keV INJECTOR UPGRADE

High-energy nuclear physics experiments at the Jefferson Lab Continuous Electron Beam Accelerator Facility (CEBAF) require highly spin-polarization electron beams, produced from strained super-lattice GaAs photocathodes, activated to negative electron affinity in a photogun operating at 130 kV dc. A pair of Wien filter spin rotators in the injector defines the orientation of the electron beam polarization at the end station target. An upgrade of the CEBAF injector to better support the upcoming MOLLER experiment requires increasing the electron beam energy to 200 keV, to reduce unwanted helicity correlated intensity and position systematics and provide precise control of the polarization orientation. Our contribution describes design, fabrication and testing of the high voltage system to upgrade the Wien spin rotator to be compatible with the 200 keV beam. This required Solidworks modeling, CST and Opera electro- and magnetostatic simulations, upgrading HV vacuum feedthroughs, and assembly techniques for improving electrode alignment. The electric and magnetic fields required by the Wien condition and the successful HV characterization under vacuum conditions are also presented.

Palacios-Serrano, G.↗

Electrostatic Design and Characterization of a 200 keV Photogun and Wien Spin Rotator

High-energy nuclear physics experiments at the Jefferson Lab Continuous Electron Beam Accelerator Facility (CEBAF) require high spin-polarization electron beams produced from strained super-lattice GaAs photocathodes activated to negative electron affinity in a high voltage photogun operating at 130 kV dc. A pair of Wien filter spin rotators in the injector provides precise control of the electron beam polarization at the end station target. An upgrade of the CEBAF injector to better support the upcoming Moller experiment requires increasing the electron beam energy to 200 keV, resulting in better transmission through injector apertures and improved photocathode lifetime. In addition, the energy increase is expected to reduce unwanted helicity correlated intensity and position systematics. These requirements led to the design of a shielding electrode described in this work, which minimizes the electric field at the triple-point junction and linearizes the potential along the insulator, thus reducing the risk of field emission induced insulator arcing. The Wien spin rotator design was modified for increasing the electric field from 1.6 to 2.7 MV/m and the magnetic field from 9.1 to 13 mT. The upgrades required detailed modeling in Solidworks, electrostatic simulations using CST, beam dynamics using GPT, device implementation, and in situ high voltage characterization of the world?s first 200 keV polarized photoelectron gun and compatible Wien filter spin rotator.

Palacios-Serrano, Gabriel↗

Measurements of an AC-LGAD strip sensor with a 120 GeV proton beam

The development of detectors that provide high resolution in four dimensions has attracted wide-spread interest in the scientific community for several applications in high-energy physics, nuclear physics, medical imaging, mass spectroscopy as well as quantum information. In addition to high time resolution and thanks to the AC-coupling of the electrodes, LGAD silicon sensors can provide high resolution in the measurement of spatial coordinates of an incident minimum ionizing particle. Such AC-coupled LGADs, also known as AC-LGADs, are therefore considered as candidates for future detectors to provide 4-dimensional measurements in a single sensing device with 100% fill factor. This article presents the first characterization of an AC-LGAD sensor with a proton beam of 120 GeV momentum at Fermilab. The sensor consists of strips with 80 μm width, fabricated at Brookhaven National Laboratory. The signal properties, efficiency, spatial, and time resolution are presented. Overall, the experimental results show that the time resolution of such an AC-LGAD is compatible to standard LGADs with similar gain, and that AC-LGADs can be segmented with fine pitches as standard strip or pixel detectors.

47 OTHER INSTRUMENTATION↗

Measurements of an AC-LGAD strip sensor with a 120 GeV proton beam

The development of detectors that provide high resolution in four dimensions has attracted wide-spread interest in the scientific community for several applications in high-energy physics, nuclear physics, medical imaging, mass spectroscopy as well as quantum information. In addition to high time resolution and thanks to the AC-coupling of the electrodes, LGAD silicon sensors can provide high resolution in the measurement of spatial coordinates of an incident minimum ionizing particle. Such AC-coupled LGADs, also known as AC-LGADs, are therefore considered as candidates for future detectors to provide 4-dimensional measurements in a single sensing device with 100$\%$ fill factor. This article presents the first characterization of an AC-LGAD sensor with a proton beam of 120 GeV momentum at Fermilab. The sensor consists of strips with 80 $\mu$m width, fabricated at Brookhaven National Laboratory. The signal properties, efficiency, spatial, and time resolution are presented. The experimental results show that the time resolution of such an AC-LGAD is compatible to standard LGADs with similar gain, and that AC-LGADs can be segmented with fine pitches as standard strip or pixel detectors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Spin Physics at PHENIX

Situated at the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory, the PHENIX experiment has for almost two decades been at the forefront of investigations into spin structure and dynamics in high-energy nuclear physics. Although decommissioned in 2016, the PHENIX collaboration has released a number of new results over the past several years that continue to inform the field. Recent longitudinal spin measurements uncover the role of gluon and sea quark polarization in the proton. Transverse spin measurements probe the transverse momentum-dependent (TMD) distributions and higher-twist multiparton correlators that are needed to fully explain partonic dynamics in the initial and final state. Additionally, the effects of heavy ions on spin have been studied by comparing transverse spin measurements between p+p and p+A collisions. These recent results and their wider implications are presented.

PHENIX↗

Physics Flash Fall 2020

The Fall 2020 edition of Physics Flash includes article briefs on: Patrick Younk, Fusing concepts, technology to past performance of nuclear explosives; LANL inertial confinement fusion programs aims to develop burning plasma for weapons science; Lab's high energy nuclear physics explored at international conference; and, Novel study reveals new details on the impact of thermal interfaces in dynamic compression experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Future Physics Perspectives on the Equation of State from Heavy Ion Collisions to Neutron Stars

With the computational power and algorithmic improvements available today, the ongoing STAR/RHIC and HADES/GSI experiments, the future FAIR and NICA facilities becoming operational, and the new precise measurements from NICER and LIGO/VIRGO, the high-energy nuclear physics and astrophysics communities are in the unique position to set very stringent constraints on the equation of state of strongly interacting matter. Here, we review the state-of-the-art of different approaches used in the description of hot and ultradense baryonic matter in and out of equilibrium, and discuss the regions in the phase diagram where heavy-ion collisions and neutron star mergers can overlap. Future perspectives are discussed to help define a comprehensive, multi-disciplinary strategy to map out the phase diagram of strongly interacting matter from heavy ion collisions to neutron stars.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Diffusion model approach to simulating electron-proton scattering events

Generative artificial intelligence is a fast-growing area of research offering various avenues for exploration in high-energy nuclear physics. In this work, we explore the use of generative models for simulating electron-proton collisions relevant to experiments like the Continuous Electron Beam Accelerator Facility and the future Electron-Ion Collider (EIC). These experiments play a critical role in advancing our understanding of nucleons and nuclei in terms of quark and gluon degrees of freedom. The use of generative models for simulating collider events faces several challenges such as the sparsity of the data, the presence of global or eventwide constraints, and steeply falling particle distributions. In this work, we focus on the implementation of diffusion models for the simulation of electron-proton scattering events at EIC energies. Our results demonstrate that diffusion models can reproduce relevant observables such as momentum distributions and correlations of particles, momentum sum rules, and the leading electron kinematics, all of which are of particular interest in electron-proton collisions. Although the sampling process is relatively slow compared to other machine-learning architectures, we find diffusion models can generate high-quality samples. We foresee various applications of our work including inference for nuclear structure, interpretable generative machine learning, and searches of physics beyond the Standard Model. Published by the American Physical Society 2024

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Long Range Plan: Dense matter theory for heavy-ion collisions and neutron stars

Since the release of the 2015 Long Range Plan in Nuclear Physics, major events have occurred that reshaped our understanding of quantum chromodynamics (QCD) and nuclear matter at large densities, in and out of equilibrium. The US nuclear community has an opportunity to capitalize on advances in astrophysical observations and nuclear experiments and engage in an interdisciplinary effort in the theory of dense baryonic matter that connects low- and high-energy nuclear physics, astrophysics, gravitational waves physics, and data science. This is a white paper prepared by a group of nuclear physicists during the 2023 LRP process.

Lovato, Alessandro↗