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DOE OSTI2023

A flexible event reconstruction based on machine learning and likelihood principles

Event reconstruction is a central step in many particle physics experiments, turning detector observables into parameter estimates; for example estimating the energy of an interaction given the sensor readout of a detector. A corresponding likelihood function is often intractable, and approximations need to be constructed. Here, in our work, we first show how the full likelihood for a many-sensor detector can be broken apart into smaller terms, and secondly how we can train neural networks to approximate all terms solely based on forward simulation. Our technique results in a fast, flexible, and close-to-optimal surrogate model proportional to the likelihood and can be used in conjunction with standard inference techniques allowing for a consistent treatment of uncertainties. We illustrate our technique for parameter inference in neutrino telescopes based on maximum likelihood and Bayesian posterior sampling. Given its great flexibility, we also showcase our method for geometry optimization enabling to learn optimal detector designs. Lastly, we apply our method to realistic simulation of a ton-scale water-based liquid scintillator detector.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Dependence of polytetrafluoroethylene reflectance on thickness at visible and ultraviolet wavelengths in air

Polytetrafluoroethylene (PTFE) is an excellent diffuse reflector widely used in light collection systems for particle physics experiments. However, the reflectance of PTFE is a function of its thickness. Here, in this work, we investigate this dependence in air for light of wavelengths 260 nm and 450 nm using two complementary methods. We find that PTFE reflectance for thicknesses from 5 mm to 10 mm ranges from 92.5% to 94.5% at 450 nm, and from 90.0% to 92.0% at 260 nm. We also see that the reflectance of PTFE of a given thickness can vary by as much as 2.7% within the same piece of material. Finally, we show that placing a specular reflector behind the PTFE can recover the loss of reflectance in the visible without introducing a specular component in the reflectance.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

Particle trajectory representation learning with masked point modeling

Liquid argon time projection chambers (LArTPCs) offer millimeter-scale 3D images of particle trajectories, enabling precision studies of neutrino oscillation, detection of supernova and solar neutrinos, searches for exotic dark matter, and proton decay. Current approaches utilize supervised machine learning models, requiring extensive simulations of particle physics and detector response that can introduce bias. Self-supervised learning (SSL), a machine learning approach that learns useful representations of unlabeled data from the data itself, has significantly advanced how large datasets are utilized for representation learning; however, its potential for applications to sensory data in high precision particle physics experiments remains largely unexplored. We introduce the Point-based liquid argon masked autoencoder (PoLAr-MAE), a self-supervised framework that learns physically meaningful representations directly from unlabeled LArTPC images. PoLAr-MAE achieves remarkable data efficiency for a point-level segmentation task, outperforming fully supervised methods in low data regimes. Linear classifiers on model outputs demonstrate robust performance across multiple downstream tasks. Our results position sensor-level SSL as a practical foundation model strategy for LArTPCs.

Young, Samuel [Stanford Univ., CA (United States)]↗

DOE OSTI2026

Scintillation of liquid nitrogen

Liquid nitrogen is commonly used in cryogenic applications and is a promising medium for the direct immersion cooling of sensors used for nuclear and particle physics experiments. The scintillation properties of gaseous nitrogen are well-documented, but little is known about the scintillation of liquid nitrogen. If present, scintillation light from interactions of ambient radioactivity could produce backgrounds for rare event searches such as the direct detection of dark matter. Using a coincidence-tagged alpha decay, we demonstrate that liquid nitrogen exhibits measurable, albeit very faint, scintillation. Assuming the same scintillation wavelengths as gaseous nitrogen, we estimate a relative scintillation yield of $Y_{\liquidn}/Y^{\STP}_{\gasn} =$ \num[uncertainty-descriptors={stat,sys}]{0.0142(0.0005)(0.0030)} with respect to gaseous nitrogen at standard temperature and pressure. Considering the average scintillation yield from alpha decays in gaseous nitrogen, this implies a scintillation yield for alpha decays in liquid nitrogen of $Y_{\liquidn} = \qty[]{2.39(0.56)}{photons~per~MeV}$. To our knowledge this is the first measurement of scintillation in liquid nitrogen.

Pagani, Luca (ORCID:0000000234692581)↗

DOE OSTI2024

Fusion Neutron Generator

The proposed code, named FROG (Fusion neutron Generator) is built upon the open-source particle transport Monte Carlo toolkit Geant4. Geant4 provides C++ classes that can be leveraged to build application-specific codes dealing with the transport of particles through matter. Geant4-based codes are applied in high-energy particle physics experiments, medical applications, shielding, and space applications for example. The FROG code allows the user to define the geometry of a neutron converter device shaped as a hollow cylinder, where a neutron breeding material such as lithium deuteride (LiD) is cladded by two concentric cylinders. Such neutron converter is then placed inside a regular nuclear fission reactor, where thermal neutrons will react with the neutron breeder material (typically, Lithium 6), and through a series of reactions, will generate high-energy neutrons – neutrons whose kinetic energy are around 14 MeV. The hollowed central portion can hold a specimen that will be bombarded by high-energy neutrons created inside the neutron breeding material. Figuratively speaking, this type of device transforms neutrons from thermal (~0.625 eV) to fusion (~14 MeV) energies and is sometimes termed “fusion-to-thermal neutron converters” in the literature. The code consists of C++ source file compiled and linked to generate an executable. The user can select the dimensions of the converter (radius, length, and thickness of the breeder material), the breeder material type, the cladding material, and the specimen material that will be activated or irradiated. As input, the neutron flux for a specific location inside a reactor, for instance, positions in ATR, is required. As output, the code predicts the number of high-energy neutrons produced, the total neutron flux and fluence as well as its detailed spectrum. The physics involved in such device is very complex, as it requires modeling neutron transport, light-ion (tritons) transport, as well as fusion reactions. The Geant4 toolkit provides the required physical models.

Martin, NicholasP. [Idaho National Laboratory (INL↗

DOE OSTI2025

Full event particle-level unfolding with variable-length latent variational diffusion

The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, statistically adjusts the experimental data for detector effects. Recently, generative machine learning models have shown promise for performing unbinned unfolding in a high number of dimensions. However, all current generative approaches are limited to unfolding a fixed set of observables, making them unable to perform full-event unfolding in the variable dimensional environment of collider data. A novel modification to the variational latent diffusion model (VLD) approach to generative unfolding is presented, which allows for unfolding of high- and variable-dimensional feature spaces. The performance of this method is evaluated in the context of semi-leptonic t\bar{t} t t ‾ production at the Large Hadron Collider.

Shmakov, Alexander↗

DOE OSTI2022

Denoising Convolutional Networks to Accelerate Detector Simulation

The high accuracy of detector simulation is crucial for modern particle physics experiments. However, this accuracy comes with a high computational cost, which will be exacerbated by the large datasets and complex detector upgrades associated with next-generation facilities such as the High Luminosity LHC. We explore the viability of regression-based machine learning (ML) approaches using convolutional neural networks (CNNs) to "denoise" faster, lower-quality detector simulations, augmenting them to produce a higher-quality final result with a reduced computational burden. The denoising CNN works in concert with classical detector simulation software rather than replacing it entirely, increasing its reliability compared to other ML approaches to simulation. We obtain promising results from a prototype based on photon showers in the CMS electromagnetic calorimeter. Future directions are also discussed.

Banerjee, Sunanda↗

DOE OSTI2024

Hear It? – New Physics Calls For a Healthy Target!

Nowadays, when the attention of the physics community is drawn to muon collider prospects and neutrino projects, a target as the primary source of such exotic particles is a key component of a particle physics experiment. The extreme conditions placed on the target, though, decrease its functionality and threaten the future of high-power targetry. Subject to superb magnetic fields, extreme temperatures, and radiation damage, the target is constantly at risk of unexpected failures. The Target Health Monitor (THM) aims to enable the continuous analysis of the target state throughout the experiment. Based on the optical concept of Brewster s angle and the reflectivity variation with the compositional changes in the target material, the THM will record and evaluate the radiation-caused transmutations in the target to foresee its malfunction before it affects the experimental results. The first steps in validating our THM concept have been made this summer. Continuing reflectivity measurements of the prospective target materials, we anticipate proving the THM potential to monitor target health effectively.

Havryshchuk, Kateryna↗

DOE OSTI2025

Developing and Managing Data Acquisition Software Using Spack

The Data Acquisition systems of particle physics experiments regularly push the boundaries of high-throughput computing, demanding low-latency collection of data from thousands of devices, collating data into time-sliced events, processing these events and making trigger decisions, and writing the selected data streams to disk. To accomplish these tasks, the DAQ Engineering and Operations department at Fermilab leverages multiple software libraries and builds reusable DAQ frameworks on top. These libraries must be delivered in well-defined bundles and are thoroughly tested for compatibility and functionality before being deployed to live detectors. We have several techniques used to ensure that a consistent set of dependencies can be delivered and re-created at need. We must also support active development of DAQ software components, ideally in an environment as close as possible to that of the detectors. This development often occurs across multiple packages which have to be built in concert and features tested in a consistent and reproducible manner. I will present our scheme for accomplishing these goals using Spack environments, bundle packages, and Github Actions-based CI.

Flumerfelt, Eric [Fermilab]↗

DOE OSTI2026

Delivery Ring Spill Characterization and Impulse Study

High-intensity particle physics experiments require uniform beam extraction to prevent instantaneous rate spikes from overwhelming detector systems. By analyzing accelerator parameters and extracted beam dynamics, we directly inform spill regulation systems that make real-time adjustments to minimize non-uniformity. This Department of Energy Visiting Faculty Program project transitioned from characterizing Main Injector half-integer slow extraction for SpinQuest to Delivery Ring third-integer slow extraction for Mu2e. Working alongside the Fast Adaptive Neural Control (FANC) group, we developed an automated pipeline that aligns asynchronous instrument channels, embeds quality metrics, and isolates clean spill populations. Analyzing baseline spills alongside a dedicated quadrupole impulse study allowed us to quantify noise structures while mapping time-varying beam response and transit-delay dynamics. These empirical measurements directly ground digital twin models, supporting FANC’s deployment of real-time, FPGA-based neural network controllers in the Mu2e Spill Regulation System.

Dolen, James William [Purdue U., West Lafayette] (↗

DOE OSTI2025

Aβ [Eβ] in 37K decay: new physics with opposite β helicity

By extending our analysis and simulations of our K -decay data set to allow the asymmetry with respect to nuclear spin to vary with energy , we have gained sensitivity to new physics that depends on a helicity factor for the , . In particular, we constrain Lorentz scalar and tensor quark-lepton interaction strengths at a sensitivity complementary to the similar Fierz interference term in neutron decay. Our result for that new physics is = 0.033 0.084 (stat) 0.039 (syst), consistent with the standard model electroweak interaction value . We consider presently achieved complementarity to -decay and particle physics experiments, along with projectable technical improvements to our method.

Behr, J. A.↗

DOE OSTI2020

Dependence of polytetrafluoroethylene reflectance on thickness at visible and ultraviolet wavelengths in air

Polytetrafluoroethylene (PTFE) is an excellent diffuse reflector widely used in light collection systems for particle physics experiments. However, the reflectance of PTFE is a function of its thickness. In this work, we investigate this dependence in air for light of wavelengths 260 nm and 450 nm using two complementary methods. We find that PTFE reflectance for thicknesses from 5 mm to 10 mm ranges from 92.5% to 94.5% at 450 nm, and from 90.0% to 92.0% at 260 nm. We also see that the reflectance of PTFE of a given thickness can vary by as much as 2.7% within the same piece of material. Finally, we show that placing a specular reflector behind the PTFE can recover the loss of reflectance in the visible without introducing a specular component in the reflectance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

System and method for high dynamic range waveform digitization

Diverse applications from particle physics experiments to lidar are driving cost and current reduction in giga-hertz sampling rate high-resolution data conversion. Multiple imagers captures a single pixel of data and require processing at very high speed. High-bandwidth high-rate signal sampling, analog-to-digital conversion, and transfer of large amounts of data to a digital data acquisition block are required in such systems. Dynamic range, power consumption, and transfer of high-speed, high-bit width data are key implementation challenges. Data acquisition architectures optimized for specific requirements of such systems may facilitate system implementation and reduce overall system cost.

Mostafanezhad, Isar↗

DOE OSTI2026

Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)

The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning (AI/ML) and silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, novel codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing towards a successful transition to the new data frontier of fundamental science.

Gonski, Julia [SLAC]↗

DOE OSTI2020

A Generic High Bandwidth Data Acquisition Card for Physics Experiments

In high energy physics and nuclear physics experiments particularly the ones based on particle accelerator, the data rate from the detector is usually in the order of Terabytes per second. This high throughput data from detector front-end electronics need to be transmitted to the back-end computing farm for high level event selection and building. A Data Acquisition (DAQ) system with features of high-density, scalable, easily upgradeable is crucial to simplify the readout architecture of whole experiment. This paper will introduce the design of a generic high bandwidth PCIe card which can be used as the important input output card in a scalable DAQ system. It can factorize front-end electronics from data handling, and reduce amount of custom hardware in favor of scalable detectorindependent commercial hardware and software. Besides the 48 channels of bidirectional high speed fiber optical links with frontends, it also supports to synchronize with the experiment timing system, and to fanout the clock and trigger information with a fixed latency to the front-end electronics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Status and prospects of Muon g-2 experiment

This article reviews the muon g-2 experiment, a cornerstone in precision tests of the Standard Model of particle physics. The experiment measures the anomalous magnetic moment of the muon with unprecedented accuracy, seeking potential discrepancies between theoretical predictions and experimental results that might indicate physics beyond the Standard Model. We trace the evolution of this measurement from its beginnings at CERN in the 1960s to the current state-of-the-art experiment at Fermilab, highlighting the remarkable engineering achievements required to achieve parts-per-billion precision. Recent results from Runs 1-3 have achieved a systematic uncertainty of 70 ppb, exceeding design goals, while ongoing theoretical calculations continue to refine predictions. Despite these advances, the analysis remains statistics-limited, with continued data collection and novel experimental approaches at MUonE and J-PARC promising further insights into this fundamental physical quantity.

Karuza, Marin [INFN, Trieste; Rijeka U.] (ORCID:00↗

DOE OSTI2024

A low-latency graph computer to identify metastable particles at the Large Hadron Collider for real-time analysis of potential dark matter signatures

Abstract Image recognition is a pervasive task in many information-processing environments. We present a solution to a difficult pattern recognition problem that lies at the heart of experimental particle physics. Future experiments with very high-intensity beams will produce a spray of thousands of particles in each beam-target or beam-beam collision. Recognizing the trajectories of these particles as they traverse layers of electronic sensors is a massive image recognition task that has never been accomplished in real time. We present a real-time processing solution that is implemented in a commercial field-programmable gate array using high-level synthesis. It is an unsupervised learning algorithm that uses techniques of graph computing. A prime application is the low-latency analysis of dark-matter signatures involving metastable charged particles that manifest as disappearing tracks.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Novel Liquid Argon Time-Projection Chamber Readouts

Liquid argon time-projection chambers (LArTPCs) have become a prominent tool for experiments in particle physics. Recent years have yielded significant advances in the techniques used to capture the signals generated by these cryogenic detectors. This article summarizes these novel developments for detection of ionization electrons and scintillation photons in LArTPCs. New methods to capture ionization signals address the challenges of scaling traditional techniques to the large scales necessary for future experiments. Pixelated readouts improve signal fidelity and expand the applicability of LArTPCs to higher-rate environments. Methods that leverage amplification in argon enable measurements in the keV regime and below. Techniques to enhance collection of argon scintillation photons improve calorimetry and expand the physics program for very large detectors. Future efforts aim to demonstrate systems for the combined detection of both electrons and photons.

43 PARTICLE ACCELERATORS↗