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At least 163 records · Page 9

Characterization of Hamamatsu VUV4 SiPM Performance for Liquid Argon Scintillation

Detection of light signals is crucial to a wide range of particle detectors. In particular, efficient detection of vacuum ultraviolet (VUV) light will provide new opportunities for some novel detectors currently being developed, but is technically challenging. In this article, we characterized the performance of Hamamatsu VUV4 silicon photomultipliers (SiPMs) for detecting VUV argon scintillation light without wavelength shifting. Here, using a customized cryogenic amplifier design, we operated two models of VUV4 SiPMs inside liquid argon and thoroughly examined their direct sensitivities to liquid argon scintillation. In addition to describing their cryogenic performance, we measured a photon detection efficiency of 14.7+1.1–2.4% and 17.2+1.6–3.0% at 128 nm for these two VUV4 models for operation at 4 V of overvoltage, with the main uncertainty arising from the SiPM reflectivity for VUV light.

Pershing, Teal↗

Implementation of a Time-domain Cosmic-ray-muon Tagger for the NEXUS Low-background Cryogenic Facility

Quantifying the effects of radiation on the operation of qubits both as quantum information systems as well as particle detectors has emerged as a pressing issue in quantum science in recent years. We present an overview of the design, operation, and deployment of a 90-$\mathrm{in}^2$ three-panel muon detector for use in the NEXUS experimental facility at Fermilab to temporally isolate correlated errors in qubits and determine if they possess an astrophysical origin. Constructed with three scintillator-attached PMTs read out with NIM modules in a triply-coincident logic scheme, we measure a surface-level muon luminosity of 9.7425 muons per second---consistent with an average surface-level cosmic-ray flux of approximately one muon per square centimeter per minute. Integrating the NIM modules with an MCC 128 DAQ HAT and Raspberry Pi, a Python script records exactly when a muon struck the detector and writes a timestamp to a log file for follow-up cross referencing. This experiment will broadly contribute to further studies aimed at understanding the source and mitigation of information loss in qubits.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

A Graph Neural Network Surrogate Model for hls4ml

Recent advancements in use of machine learning (ML) techniques on field-programmable gate arrays (FPGAs) have allowed for the implementation of embedded neural networks with extremely low latency. This is invaluable for particle detectors at the Large Hadron Collider, where latency and used area are strictly bounded. The hls4ml framework is a procedure that converts trained ML model software to a synthesis result to can be used on an FPGA. However, running the pipeline is a time-consuming procedure, and there is a strong risk of failure. In particular, it may not be possible to successfully convert a model into a synthesis result, or the resource consumption of the model may exceed the resources of the target FPGA. To aid with this development, we introduce wa-hls4ml, a surrogate model using a graph neural network to emulate the structure of the source models. The goal is to estimate the chance of success and resource consumption of a given model when passed through the hls4ml pipeline, without needing to run the pipeline.

Plotnikov, Dennis↗

Measuring quasiparticle dynamics for particle impact reconstruction in a superconducting qubit chip

Quasiparticle poisoning following particle impacts poses a significant challenge to the development of fault-tolerant superconducting quantum computers, as a sudden excess of quasiparticles can simultaneously degrade the coherence of multiple qubits across large device arrays. In this work, we present a statistical analysis that models the time evolution of radiation-induced qubit energy relaxation through quasiparticle density dynamics. This study provides insight into quasiparticle loss processes by distinguishing between recombination and trapping decay channels and assessing their respective impact on qubit performance. We precisely measure quasiparticle recombination in multiple transmon qubits and uncover an unexpected dependence of qubit relaxation dynamics on deposited energy. By linking correlated relaxation events across qubits to ballistic phonon propagation, we introduce a statistical localization approach to extract the energy deposited in the substrate, which is in good agreement with Monte Carlo simulation. This work establishes the quantitative framework for using an arbitrary subset of superconducting transmon qubits in a QPU as energy-resolving witness particle detectors.

Celi, E. [Northwestern U.]↗

Collective excitations and low-energy ionization signatures of relativistic particles in silicon detectors

Abstract Solid-state detectors with a low energy threshold have several applications, including searches of non-relativistic halo dark-matter particles with sub-GeV masses. When searching for relativistic, beyond-the-Standard-Model particles with enhanced cross sections for small energy transfers, a small detector with a low energy threshold may have better sensitivity than a larger detector with a higher energy threshold. In this paper, we calculate the low-energy ionization spectrum from high-velocity particles scattering in a dielectric material. We consider the full material response including the excitation of bulk plasmons. We generalize the energy-loss function to relativistic kinematics, and benchmark existing tools used for halo dark-matter scattering against electron energy-loss spectroscopy data. Compared to calculations commonly used in the literature, such as the Photo-Absorption-Ionization model or the free-electron model, including collective effects shifts the recoil ionization spectrum towards higher energies, typically peaking around 4–6 electron-hole pairs. We apply our results to the three benchmark examples: millicharged particles produced in a beam, neutrinos with a magnetic dipole moment produced in a reactor, and upscattered dark-matter particles. Our results show that the proper inclusion of collective effects typically enhances a detector’s sensitivity to these particles, since detector backgrounds, such as dark counts, peak at lower energies.

Physics↗

Nb$_3$Sn Superconducting Radiofrequency Cavities: a Maturing Technology for Particle Accelerators and Detectors

Nb3Sn superconducting radiofrequency (SRF) cavities have substantial potential for enabling new performance capabilities for particle accelerators for high energy physics (HEP), as well as for RF cavity-based detectors for dark matter, gravitational waves, and other quantum sensing applications. Outside of HEP, Nb3Sn SRF cavities can also benefit accelerators for nuclear physics, basic energy sciences, and the industry. In this contribution to Snowmass 2021, we overview the potential, status, and outlook of Nb3Sn SRF cavities.

43 PARTICLE ACCELERATORS↗

Motivation for Two Detectors at a Particle Physics Collider

It is generally accepted that it is preferable to build two general purpose detectors at any given collider facility. We reinforce this point by discussing a number of aspects and particular instances in which this has been important. The examples are taken mainly, but not exclusively, from experience at the Tevatron collider.

43 PARTICLE ACCELERATORS↗

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↗

Search for Long-Lived Particles with Muon Detector Shower Signature in the CMS Run-3 data

Many beyond standard model theories predict the existence of long-lived particles (LLPs). These LLPs can have sizable lifetimes and decay several meters from their production vertex. In this poster/talk, we present the analysis strategy for searching for LLPs using the Compact Muon Solenoid (CMS) Experiment. The proton-proton collision data used in this analysis were collected from 2022 to 2024 at a center-of-mass energy of 13.6 TeV, corresponding to an integrated luminosity of 170 fb^-1. The LLP decays are reconstructed as a high-multiplicity cluster of detector hits in the cathode strip chambers (CSC) of the muon system endcap. This signature is referred to as the Muon Detector Showers (MDS). This search requires events to contain at least one MDS cluster. The analysis focuses on LLP hadronic decays and LLP masses up to a few tens of GeV. We present the signal properties in MonteCarlo simulation, event selection, background modeling, and evaluation of the expected sensitivity. The results are interpreted under the Twin Higgs model benchmark.

Agyemang-Duah, Andrews [Grambling State U.]↗

Full event interpretation with machine-learning-based particle-flow reconstruction in the CMS detector

The particle-flow (PF) algorithm constructs a global description of each particle collision by producing a comprehensive list of final-state particles, and is central to event reconstruction in the CMS experiment at the CERN LHC. The existing PF implementation relies on physics-motivated heuristics and assumptions that can be replaced by machine-learning (ML) models trained directly on simulated data and naturally suited to modern graphics processing units (GPUs). A state-of-the-art ML-based PF (MLPF) reconstruction algorithm, implemented within the CMS software framework, is presented. The MLPF algorithm performs a learnable full-event reconstruction on GPUs, generalizes across detector conditions and collision energies, and replaces multiple modular reconstruction steps with a single unified model. Physics performance comparable to standard PF reconstruction is achieved in both simulation and data, with improved jet energy resolution and inference time. In simulated top quark-antiquark events under LHC Run-3 (2023-2024) conditions, the jet energy resolution improves by 10-20% for jets with transverse momentum between 30-100 GeV. Inference time is evaluated using simulated multijet events, with a median of $20\,\hbox {ms}$ per event on an Nvidia L4 GPU, compared to approximately $110\,\hbox {ms}$ for the standard CMS PF reconstruction.

Hayrapetyan, Aram [Yerevan Phys. Inst.]↗

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↗

Searching for Short-Lived Particles Using the FASER Neutrino Detector

Short-lived particles such as $\tau$ leptons and charm hadrons produced from high-energy neutrino interactions can provide key insights into the Standard Model and beyond. In this study, initial results of the performance of dedicated search tools developed for FASERν are presented. For charged charm searches, all daughter tracks are identified for ~3/4 of the events. Advanced yet reliable machine learning tools are used for D0 detection. For $\tau$ searches, a signal / background ratio of 3.8 is achieved. These results are a major step towards discovering the first charm hadrons and $\tau$ leptons produced from collider neutrinos.

Thor, Simon [CERN] (ORCID:000000029183526X)↗

ElectroMon Geometry Considerations: Simulations of Electron Transport to a Diamond-Based Detector

Particle-in-Cell simulations were performed to investigate the effects of electrode bias and pitch on the expected number of electrons arriving at a diamond-based detector at a set of relevant incident electron energies for electron cloud monitoring. Results of these simulations indicate that the pitch of the electrodes directly impacts the number of incident electrons based on complete opacity of the electrodes to the electrons; image charge does not play a role at any relevant electron energy. Positive bias on the incident electrodes directly increases the energy of the incident electrons, while negative bias reduces the energy and completely repels incoming electrons of energies lower than the applied bias. These results are intended to be used as input parameters for a solid-state simulation which will determine the collection efficiency of the electron-hole pairs produced within the diamond as a function of position and energy.

43 PARTICLE ACCELERATORS↗

On-chip probabilistic inference for charged-particle tracking at the sensor edge

Modern scientific instruments operate under increasingly extreme constraints on bandwidth, latency, and power. Inference at the sensor edge determines experimental data collection efficiency by deciding which information to save for further analysis. Particle tracking detectors at the Large Hadron Collider exemplify this challenge: pixelated silicon sensors generate rich spatiotemporal ionization patterns, yet most of this information is discarded due to data-rate limitations. Concurrently, advancements in co-design tools provide rapid turn-around for incorporating machine learning into application-specific integrated circuits, motivating designs for particle detectors with new integrated technologies. We demonstrate that neural networks embedded in the front-end electronics can infer charged-particle kinematic parameters from a single silicon layer. We regress hit positions and incident angles with calibrated uncertainties, while satisfying stringent constraints on numerical precision, latency, and silicon area. Our results establish a path toward probabilistic inference directly at the edge, opening new opportunities for intelligent sensing in high-rate scientific instruments.

Das, Arghya Ranjan [Purdue U.] (ORCID:000000018451↗

3D Integrated Sensing Solutions

We will present the proposed research project to develop 3D-integrated sensors using advanced manufacturing capability for novel sensors, heterogeneously integrated with energy efficient readout circuits. The lack of precision timing in particle tracking detectors and the absence of low power, high throughput communications solutions to read them out limits progress in multiple fields of fundamental science. The aim of the project is to develop technology to enable large-scale particle detectors with 3D-integrated ASIC designs to simultaneously achieve 10 μm position resolution and 10 ps precision timing, with low-power consumption and high throughput rates. This research program leverages the unique combination of facilities and cross-disciplinary expertise of scientists and engineers at SLAC, FNAL, and LLNL and industrial partners.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Particle showers detected on ISS in Timepix pixel detectors

We detect regular particle showers in several compact pixel detectors, distributed over the International Space Station. These showers are caused by high energy galactic cosmic rays, with energies often in the 10 s of TeV or higher. We survey the frequency of these events, their dependence on location on ISS, and their independence of the location of ISS, on its orbit. The Timepix detectors used allow individual particle tracks to be resolved, providing a possibility to perform physical analysis of shower events, which we demonstrate. In terms of radiation dosimetry, these showers indicate certain possible limitations of traditional dosimetric measures, in that (a) the dose measured in small sensor may be less than that received in a larger distribution of matter, such as a human and (b) the spatial and temporal extent of these events represents a regime of poorly documented biological response.

79 ASTRONOMY AND ASTROPHYSICS↗