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All sourcesNASA NTRSDOE OSTI
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 OSTI2022

Bandwidth and Aliasing in the Microwave SQUID Multiplexer

The microwave SQUID multiplexer (μmux) has enabled higher bandwidth or higher channel counts across a wide range of experiments in particle physics, astronomy, and spectroscopy. The large multiplexing factor coupled with recent commercial availability of microwave components and warm electronics readout systems make it an attractive candidate for systems requiring large cryogenic detector counts. Since the multiplexer is considered for both bolometric and calorimetric applications across several orders of magnitude of signal frequencies, understanding the bandwidth of the device and its interaction with readout electronics is key to appropriately designing and engineering systems. Here we discuss several important factors contributing to the bandwidth properties of μmux systems, including the intrinsic device bandwidth, interactions with warm electronics readout systems, and aliasing. Further, we present simulations and measurements of μmux devices coupled with SLAC Microresonator RF (SMuRF) tone-tracking electronics and discuss several implications for future experimental design.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Reflectivity of VUV-sensitive silicon photomultipliers in liquid Xenon

Silicon photomultipliers are regarded as a very promising technology for next-generation, cutting-edge detectors for low-background experiments in particle physics. This work presents systematic reflectivity studies of Silicon Photomultipliers (SiPM) and other samples in liquid xenon at vacuum ultraviolet (VUV) wavelengths. A dedicated setup at the University of Münster has been used that allows to acquire angle-resolved reflection measurements of various samples immersed in liquid xenon with 0.45° angular resolution. Four samples are investigated in this work: one Hamamatsu VUV4 SiPM, one FBK VUV-HD SiPM, one FBK wafer sample and one Large-Area Avalanche Photodiode (LA-APD) from EXO-200. Here, the reflectivity is determined to be 25–36 % at an angle of incidence of 20° for the four samples and increases to up to 65 % at 70° for the LA-APD and the FBK samples. The Hamamatsu VUV4 SiPM shows a decline with increasing angle of incidence. The reflectivity results will be incorporated in upcoming light response simulations of the nEXO detector.

(pin diodes, APDs, SiPMs, G-APDs, CCDs, EBCCDs, EM↗
DOE OSTI2026

The Future Polarized Target Program at Jefferson Lab

Polarized targets have played a crucial role in Jefferson Lab's exploration of nuclear structure over the past four decades. The three original experimental halls have seen 19 separate installations of polarized solid or gas targets for use in the particle physics scattering experiments, and this trend will continue in the next decade. Five polarized target systems are in preparation for use at JLab in the coming years. Hall B will see the use of two solid polarized targets, one longitudinally polarized to the beam, the other transversely, as well as a novel 3He gas polarized target. In Hall C, new experiments will augment the tensor polarization in dynamically polarized solids. Plans are under development to bring a polarized solid target to Jefferson Lab's photon beam hall, Hall D, for the first time. To support these efforts, the JLab polarized target group is building a test laboratory to develop dynamic nuclear polarization techniques, as well as an apparatus to irradiate target material using electrons from JLab's injector test facility. In this talk, we will explore the development progress and plans for each of these efforts.

Maxwell, James [Thomas Jefferson National Accelera↗
DOE OSTI2024

HGQ: High Granularity Quantization for Real-time Neural Networks on FPGAs

Neural networks with sub-microsecond inference latency are required by many critical applications. Targeting such applications deployed on FPGAs, we present High Granularity Quantization (HGQ), a quantization-aware training framework that optimizes parameter bit-widths through gradient descent. Unlike conventional methods, HGQ determines the optimal bit-width for each parameter independently, making it suitable for hardware platforms supporting heterogeneous arbitrary precision arithmetic. In our experiments, HGQ shows superior performance compared to existing network compression methods, achieving orders of magnitude reduction in resource consumption and latency while maintaining the accuracy on several benchmark tasks. These improvements enable the deployment of complex models previously infeasible due to resource or latency constraints. HGQ is open-source and is used for developing next-generation trigger systems at the CERN ATLAS and CMS experiments for particle physics, enabling the use of advanced machine learning models for real-time data selection with sub-microsecond latency.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Agentic artificial intelligence for multistage physics experiments at a large-scale user facility particle accelerator

We present a language-model-driven agentic artificial intelligence (AI) system to autonomously execute multistage physics experiments on a production synchrotron light source. Implemented at the Advanced Light Source particle accelerator, the system translates natural language user prompts into structured execution plans that combine archive data retrieval, control-system channel resolution, automated script generation, controlled machine interaction, and analysis. In a representative machine physics task, we show that preparation time was reduced by 2 orders of magnitude relative to manual scripting even for a system expert, while operator-standard safety constraints were strictly upheld. Core architectural features, plan-first orchestration, bounded tool access, and dynamic capability selection, enable transparent, auditable execution with fully reproducible artifacts. These results establish a blueprint for the safe integration of agentic AI into accelerator experiments and demanding machine physics studies, as well as routine operations, with direct portability across accelerators worldwide and, more broadly, to other large-scale scientific infrastructures.

Accelerator/storage ring control systems↗
DOE OSTI2021

Criteria for projected discovery and exclusion sensitivities of counting experiments

Abstract The projected discovery and exclusion capabilities of particle physics and astrophysics/cosmology experiments are often quantified using the median expected p-value or its corresponding significance. We argue that this criterion leads to flawed results, which for example can counterintuitively project lessened sensitivities if the experiment takes more data or reduces its background. We discuss the merits of several alternatives to the median expected significance, both when the background is known and when it is subject to some uncertainty. We advocate for standard use of the “exact Asimov significance” $$Z^\mathrm{A}$$ Z A detailed in this paper.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

The Muon g-2 Experiment at Fermilab

The muon magnetic anomaly, $a_{\mu}$, is a powerful test of the Standard Model of particle physics. A new experiment at Fermilab has recently measured $a_{\mu}$ with unprecedented precision, confirming the results of the earlier Brookhaven experiment and strengthening the tension with the prediction of the Standard Model as determined by dispersive methods. We here describe the experimental technique, recapitulate the recent result, and discuss some of the improvements made for subsequent analyses.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Celeritas: GPU-accelerated particle transport for detector simulation in High Energy Physics experiments

Within the next decade, experimental High Energy Physics (HEP) will enter a new era of scientific discovery through a set of targeted programs recommended by the Particle Physics Project Prioritization Panel (P5), including the upcoming High Luminosity Large Hadron Collider (LHC) HL-LHC upgrade and the Deep Underground Neutrino Experiment (DUNE). These efforts in the Energy and Intensity Frontiers will require an unprecedented amount of computational capacity on many fronts including Monte Carlo (MC) detector simulation. In order to alleviate this impending computational bottleneck, the Celeritas MC particle transport code is designed to leverage the new generation of heterogeneous computer architectures, including the exascale computing power of U.S. Department of Energy (DOE) Leadership Computing Facilities (LCFs), to model targeted HEP detector problems at the full fidelity of Geant4. This paper presents the planned roadmap for Celeritas, including its proposed code architecture, physics capabilities, and strategies for integrating it with existing and future experimental HEP computing workflows.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Toward the Frontiers of Particle Physics With the Muon $g\textrm{-}2$ Experiment

The Muon $g\textrm{-}2$ Experiment (E989) at Fermilab has a goal of measuring the muon anomaly ($a_\mu$) with unprecedented precision using positive muons. This measurement is motivated by the difference between the previous Brookhaven $a_\mu$ measurement and Standard Model prediction exceeding three standard deviations, which hints at the possibility of physics beyond the Standard Model. Muons are circulated in a storage ring, and the measurement requires a precise determination of the muon anomalous precession frequency (spin precession relative to momentum) from the resulting decay positron time and energy measurements collected with calorimeters. The average magnetic field seen by the muons needs to be known with high precision, and so the storage ring magnetic field is shimmed to be very uniform and is continually monitored with nuclear magnetic resonance (NMR) probes. Detailed Muon Campus beamline and muon storage ring simulations are also required for quantifying beam dynamics and spin-related systematic effects in the determination of the muon anomalous precession frequency, e.g. muon losses during the measurement window. At the time of the conference, the experiment has recently commenced Run-3, and the release of Run-1 physics results is planned for 2020.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Design and optimization of higher order mode couplers for the superconducting cavities of the PERLE energy recovery linac

The Powerful Energy Recovery Linac for Experiments (PERLE) is an energy recovery linac (ERL) facility based on superconducting radio-frequency (SRF) technology to be hosted at the Laboratoire de Physique des 2 Infinis Irène Joliot-Curie (IJCLab) in France. With a target beam power of 10 MW, PERLE aims to demonstrate the high-current, continuous wave, multi-pass operation to validate options for future high-energy machines, such as the 50 GeV ERL proposed for the Large Hadron electron Collider (LHeC) and the Future Circular electron-hadron Collider (FCC-eh), and host dedicated particle physics and nuclear experiments. In high-current ERLs, the regenerative Beam Breakup (BBU), emerging from the beam and cavity Higher Order Modes (HOMs) interaction, is a major concern for their stable operation. Beam-induced HOMs can increase the cavity heat load at cryogenic temperature and cause beam instabilities. HOM couplers are installed in the cavity beam pipes to absorb HOM energy and mitigate these effects. This thesis presents the design and optimization of several coaxial HOM couplers for the 5-cell 801.58 MHz elliptical Nb cavities of the 500 MeV PERLE ERL configuration. The RF transmission of the HOM couplers was optimized to enhance the damping of the most dangerous HOMs. The optimized HOM couplers were integrated into endgroups to simulate their damping performance and thermal behavior. The optimized HOM couplers were 3D-printed in epoxy and copper-coated. Low-power RF measurements were conducted on the produced HOM couplers installed in copper PERLE-type cavities to validate their damping performance and propose several endgroups for the PERLE 5-cell cavity to mitigate HOMs below the BBU instability limits.

Barbagallo, Carmelo↗
DOE OSTI2021

Investigating the XENON1T low-energy electronic recoil excess using NEST

The search for dark matter, the missing mass of the Universe, is one of the most active fields of study within particle physics. The XENON1T experiment recently observed a 3.5⁢𝜎 excess potentially consistent with dark matter, or with solar axions. Here, we will use the Noble Element Simulation Technique (NEST) software to simulate the XENON1T detector, reproducing the excess. We utilize different detector efficiency and energy reconstruction models, but they primarily impact sub-keV energies and cannot explain the XENON1T excess. However, using NEST, we can reproduce their excess in multiple, unique ways, most easily via the addition of 31 ± 11 37 Ar decays. Furthermore, this results in new, modified background models, reducing the significance of the excess to ≤2.2⁢𝜎 at least using non-Profile Likelihood Ratio (PLR) methods. This is independent confirmation that the excess is a real effect, but potentially explicable by known physics. Many cross-checks of our 37 Ar hypothesis are presented.

particle dark matter↗
DOE OSTI2025

Reweighting simulated events using machine-learning techniques in the CMS experiment

Data analyses in particle physics rely on an accurate simulation of particle collisions and a detailed simulation of detector effects to extract physics knowledge from the recorded data. Event generators together with a GEANT -based simulation of the detectors are used to produce large samples of simulated events for analysis by the LHC experiments. These simulations come at a high computational cost, where the detector simulation and reconstruction algorithms have the largest CPU demands. This article describes how machine-learning (ML) techniques are used to reweight simulated samples obtained with a given set of parameters to samples with different parameters or samples obtained from entirely different simulation programs. The ML reweighting method avoids the need for simulating the detector response multiple times by incorporating the relevant information in a single sample through event weights. Results are presented for reweighting to model variations and higher-order calculations in simulated top quark pair production at the LHC. This ML-based reweighting is an important element of the future computing model of the CMS experiment and will facilitate precision measurements at the High-Luminosity LHC.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Fitting a deep generative hadronization model

Hadronization is a critical step in the simulation of high-energy particle and nuclear physics experiments. As there is no first principles understanding of this process, physically-inspired hadronization models have a large number of parameters that are fit to data. Deep generative models are a natural replacement for classical techniques, since they are more flexible and may be able to improve the overall precision. Proof of principle studies have shown how to use neural networks to emulate specific hadronization when trained using the inputs and outputs of classical methods. However, these approaches will not work with data, where we do not have a matching between observed hadrons and partons. In this paper, we develop a protocol for fitting a deep generative hadronization model in a realistic setting, where we only have access to a set of hadrons in data. Our approach uses a variation of a Generative Adversarial Network with a permutation invariant discriminator. We find that this setup is able to match the hadronization model in Herwig with multiple sets of parameters. This work represents a significant step forward in a longer term program to develop, train, and integrate machine learning-based hadronization models into parton shower Monte Carlo programs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

A Common Tracking Software Project

Abstract The reconstruction of the trajectories of charged particles, or track reconstruction, is a key computational challenge for particle and nuclear physics experiments. While the tuning of track reconstruction algorithms can depend strongly on details of the detector geometry, the algorithms currently in use by experiments share many common features. At the same time, the intense environment of the High-Luminosity LHC accelerator and other future experiments is expected to put even greater computational stress on track reconstruction software, motivating the development of more performant algorithms. We present here A Common Tracking Software (ACTS) toolkit, which draws on the experience with track reconstruction algorithms in the ATLAS experiment and presents them in an experiment-independent and framework-independent toolkit. It provides a set of high-level track reconstruction tools which are agnostic to the details of the detection technologies and magnetic field configuration and tested for strict thread-safety to support multi-threaded event processing. We discuss the conceptual design and technical implementation of ACTS, selected applications and performance of ACTS, and the lessons learned.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Ion Manipulation from Liquid Xe to Vacuum: Ba-Tagging for a nEXO Upgrade and Future 0 νββ Experiments

Neutrinoless double beta decay (0𝜈𝛽𝛽) provides a way to probe physics beyond the Standard Model of particle physics. The upcoming nEXO experiment will search for 0𝜈𝛽𝛽 decay in 136 Xe with a projected half-life sensitivity exceeding 10 28 years at the 90% confidence level using a liquid xenon (LXe) Time Projection Chamber (TPC) filled with 5 tonnes of Xe enriched to ∼90% in the 𝛽𝛽-decaying isotope 136 Xe. In parallel, a potential future upgrade to nEXO is being investigated with the aim to further suppress radioactive backgrounds and to confirm 𝛽𝛽-decay events. This technique, known as Ba-tagging, comprises extracting and identifying the 𝛽𝛽-decay daughter 136 Ba ion. One tagging approach being pursued involves extracting a small volume of LXe in the vicinity of a potential 𝛽𝛽-decay using a capillary tube and facilitating a liquid-to-gas phase transition by heating the capillary exit. The Ba ion is then separated from the accompanying Xe gas using a radio-frequency (RF) carpet and RF funnel, conclusively identifying the ion as 136 Ba via laser-fluorescence spectroscopy and mass spectrometry. Simultaneously, an accelerator-driven Ba ion source is being developed to validate and optimize this technique. The motivation for the project, the development of the different aspects, along with the current status and results, are discussed here.

a-tagging↗
DOE OSTI2024

Distilling particle knowledge for fast reconstruction at high-energy physics experiments

Knowledge distillation is a form of model compression that allows artificial neural networks of different sizes to learn from one another. Its main application is the compactification of large deep neural networks to free up computational resources, in particular on edge devices. In this article, we consider proton-proton collisions at the High-Luminosity Large Hadron Collider (HL-LHC) and demonstrate a successful knowledge transfer from an event-level graph neural network (GNN) to a particle-level small deep neural network (DNN). Our algorithm, DistillNet, is a DNN that is trained to learn about the provenance of particles, as provided by the soft labels that are the GNN outputs, to predict whether or not a particle originates from the primary interaction vertex. The results indicate that for this problem, which is one of the main challenges at the HL-LHC, there is minimal loss during the transfer of knowledge to the small student network, while improving significantly the computational resource needs compared to the teacher. This is demonstrated for the distilled student network on a CPU, as well as for a quantized and pruned student network deployed on a field programmable gate array. Our study proves that knowledge transfer between networks of different complexity can be used for fast artificial intelligence (AI) in high-energy physics that improves the expressiveness of observables over non-AI-based reconstruction algorithms. Such an approach can become essential at the HL-LHC experiments, e.g. to comply with the resource budget of their trigger stages.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗