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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Measurement of the muon neutrino charged-current mesonless cross section in the NOvA near detector

NOvA is a long-baseline accelerator neutrino experiment at Fermilab. Its physics goals include precision neutrino oscillation measurements, neutrino interaction cross-section measurements and beyond Standard Model explorations. We present a measurement of muon neutrino charged-current cross section with zero mesons in the final state at the NOvA near detector. This measurement is performed as a function of the kinematics of the final state muon. Our chosen interaction channel is especially sensitive to quasielastic and meson exchange current interactions and it provides handles for constraining the cross section systematic uncertainties in oscillation analyses in present and future experiments. For particle identification, we use a convolutional neural network (CNN) trained on individual particles simulated in the NOvA Near detector. This allows us to select the desired signal while reducing the potential bias from neutrino interaction modeling. We study strategies for constraining the remaining charged-pion background via Michel electron information in a template fitting approach. The main experimental result is a two-dimensional differential cross section as a function of final-state muon kinetic energy and polar angle. The parameters of this measurement, including binning and unfolding, were optimized to reduce the expected systematic uncertainty in the total cross section. The final result shows good agreement with the main GENIE-based simulation framework that was independently fine-tuned in NOvA. We finally propose improvements and subsequent steps that build on this analysis and further dissect the final states of neutrino interactions. This work has been supported by US DOE grant DE-SC0015684.

Sánchez Falero, Sebastián Jesús [Iowa State U.]↗
NASA NTRS1973

Fast-neutron spectrometer developments

Li6 sandwich-type neutron spectrometer is equipped with proportional counter for particle identification. System uses current-sensitive preamplifiers to minimize pile-up of gamma-ray and particle pulses.

Moler, R. B.↗
DOE OSTI2026

The General Antiparticle Spectrometer (GAPS) Antarctic Balloon Payload

The General Antiparticle Spectrometer (GAPS) is an Antarctic stratospheric balloon mission designed to provide unmatched sensitivity to low-energy (<0.25 GeV/n) cosmic-ray antiprotons, antideuterons, and antihelium nuclei as signatures of dark matter. The distinctive GAPS particle identification technique relies on measuring the energy loss along the track of an incoming antinucleus as it slows down and is captured into an exotic atom, and then detecting the de-excitation X-rays and the nuclear annihilation products. This measurement is realized using a Tracker composed of more than 1000 custom silicon strip detectors and a plastic scintillator time-of-flight (TOF) system instrumenting more than 40m$^2$. Together, these subsystems provide the velocity and energy resolution, stopping power, particle tracking, and X-ray identification necessary to distinguish rare antinucleus signals from the abundant positive-nucleus backgrounds, all within the constraints of a high-altitude mission. A multi-loop capillary heat pipe system has been developed to maintain the tracker operating temperature with significant mass and power savings over a conventional pump-based system. The first GAPS science payload flew for 25 days during the 2025/26 NASA Antarctic balloon campaign. We detail the design, integration, and commissioning of the payload prior to flight.

Aoyama, Kazutaka [JAXA, Sagamihara]↗
DOE OSTI2025

Performance of Muon Neutrino Reconstruction at ICARUS using SPINE Machine Learning.

ICARUS is a liquid argon time projection chamber (LArTPC) neutrino experiment at Fermilab. Located ~600 m from the Booster Neutrino Beam (BNB) target, it serves as the far detector in the Short-Baseline Neutrino (SBN) Program. The primary objective of the SBN Program is to probe neutrino oscillation physics at short baselines using multiple detectors, including measurements of both muon neutrino disappearance and electron neutrino appearance. A key requirement for the neutrino oscillation measurements is robust event reconstruction, including accurate and precise particle identification (PID) and kinematic reconstruction across a wide range of topologies. To achieve the required performance, we employ the SPINE (“Scalable Particle Imaging with Neural Embeddings”) package, a state-of-the-art machine learning reconstruction framework. This talk presents the status of muon neutrino reconstruction using SPINE at ICARUS, including the evaluation of reconstruction performance that will enable the full physics program at ICARUS and at the SBN Program.

Totani, Dante [Colorado State U.] (ORCID:000000019↗
NASA NTRS1971

Nuclear tracks in lunar samples

An attempt is made to relate the appearance of an etched tract to the atomic number and velocity of the ion that left it using 10 MeV/nucleon Kr beams and 6 MeV/nucleon Zn beams. It was found that the etching rate along a tract in minerals and glass is a monototonic function of ionization rate thus, making particle identification possible. Results show the following were present in lunar samples: superheavy elements, cosmic rays with z greater than 26, and solar flare particles in Surveyor glass.

Price, P. B.↗
NASA NTRS2007

Genesis Concentrator Target Particle Contamination Mapping and Material Identification

The majority of surface particles were found to be < 5 microns in diameter with increasing numbers close to the optical resolution limit of 0.3 microns. Acceleration grid EDS results show that the majority of materials appear to be from the SRC shell and SLA materials which include carbon-carbon fibers and Si-rich microspheres in a possible silicone binder. Other major debris material from the SRC included white paint, kapton, collector array fragments, and Al. Image analysis also revealed that SRC materials were also found mixed with the Utah mud and salt deposits. The EDS analysis of the acceleration grid showed that particles < 1 m where generally carbon based particles. Chemical cleaning techniques with Xylene and HF in an ultrasonic bath are currently being investigated for removal of small particles by the Genesis science team as well as ultra-pure water megasonic cleaning by the JSC team [4]. Removal of organic contamination from target materials is also being investigated by the science team with the use of UV-ozone cleaning devices at JSC and Open University [5]. In preparation for solar wind oxygen analyses at UCLA and Open University [1, 2], surface particle contamination on three Genesis concentrator targets was closely examined to evaluate cleaning strategies. Two silicon carbide (Genesis sample # 60001 and 60003) and one chemical vapor deposited (CVD) 13C concentrator target (60002) were imaged and mosaic mapped with optical microscopes. The resulting full target mosaic images and particle feature maps were subsequently compared with non-flight, but flight-like, concentrator targets and sample return capsule (SRC) materials. Contamination found on the flown concentrator acceleration grid was further examined using a scanning electron microscope (SEM). Energy dispersive X-ray spectroscopy (EDS) for particle identification was subsequently compared with the optical images from the flown targets. Figure 1 show that all three targets imaged in this report are fully intact and do not show any signs of material fractures. However, previous ellipsometry results and overview imaging of both flown SiC targets show a solar wind irradiation gradient from the center focal point to the outer edge [3]. In addition, due to the hard landing, each target has experienced varying degrees of impacts, scratches, and particle debris from the spacecraft and Utah impact site.

Calaway, Michael J.↗
NASA NTRS1989

Minimum length Pb/SCIN detector for efficient cosmic ray identification

A study was made of the performance of a minimal length cosmic ray shower detector that would be light enough for space flight and would provide efficient identification of positions and protons. Cosmic ray positions are mainly produced in the decay chain of: Pion yields Muon yields Positron and they provide a measure of the matter density traversed by primary protons. Present positron flux measurements are consistent with the Leaky Box and Halo models for sources of cosmic rays. Abundant protons in the space environment are a significant source of background that would wash out the positron signal. Protons and positrons produced very distictive showers of particles when they enter matter; many studies have been published on their behavior on large calorimeter detectors. The challenge is to determine the minimal material necessary (minimal calorimeter depth) for positive particles identification. The primary instrument for the investigation is the Monte Carlo code GEANT, a library of programs from CERN that can be used to model experimental geometry, detector responses and particle interaction processes. The use of the Monte Carlo approach is crucial since statistical fluctuations in shower shape are significant. Studies conducted during the 1988 summer program showed that straightforward approaches to the problem achieved 85 to 90 percent correct identification, but left a residue of 10 to 15 percent misidentified particles. This percentage improved to a few percent when multiple shower-cut criteria were applied to the data. This summer, the same study was extended to employ several physical and statistical methods of identifying response of the calorimeter and the efficiency of the optimal shower cuts to off-normal incidence particle was determined.

Snyder, H. David↗
DOE OSTI2024

3D-Reconstruction of Tau Neutrinos in LArTPC Detectors

The Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment currently under construction. DUNE will consist of two high-resolution neutrino interaction imaging detectors exposed to the world’s most intense neutrino beam, with the Near Detector at Fermilab and the Far Detector 1,300 km away in the Sanford Underground Research Facility in South Dakota, US. The high statistics and excellent resolution capabilities of DUNE's $^{40}$Ar detector will allow us to make precision studies of oscillation parameters capable of searching for CP violation in the lepton sector, testing interaction models, and studying phenomena that have until now, seemed too complex to measure, like $\nu_\tau$ detection and therefore, providing the completion of the 3-flavor neutrino paradigm. Knowledge of the $\nu_\tau$ detection can impact a broad spectrum of open questions. These include searching for non-standard neutrino interactions, constraining the unitarity of the PMNS matrix, searching for sterile neutrinos, and studying neutrino interactions. In the case of LArTPC data, the detector hits can be considered nodes in a graph, and the edges represent the spatial and temporal relationships between them. By using graph neural networks, it is possible to exploit these relationships and improve the accuracy of particle identification and reconstruction. During my presentation and specifically for tau neutrino reconstruction, I will show the effectiveness and reliability of our in-house developed graph neural network (GNN), NuGraph. This GNN classifies detector hits based on the particle type responsible for their production, assuring that the system accurately identifies and categorizes information based on its unique characteristics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Moving toward automated µFTIR spectra matching for microplastic identification: addressing false identifications and improving accuracy

Abstract Infrared spectroscopy is a widely used tool for studying microplastics and identifying microparticles. Researchers rely on spectral libraries to differentiate between synthetic and natural materials. Unfortunately, spectral library matching is not perfect, and best practices require researchers to use time consuming, manual peak matching to assess spectral matches. Moving toward automated matching requires increased confidence in the matching process. Using spectra matching software may increase the efficiency of particle identification, however some matching strategies may confuse natural materials such as cotton, silk, and plant matter with common classes of synthetics such as polyesters and polyamides. In this experiment, we prepared 22 pristine sample materials from natural and synthetic sources and measured micro-Fourier transform infrared (µFTIR) spectra in transmission mode for each sample using a Thermo Nicolet iN10 MX instrument. The collected spectra were then input into two spectral library matching systems (Omnic Picta and Open Specy), using a total of five identification routines. Next, we placed a subset of four pristine microplastic materials in a biologically active river system for two weeks to simulate environmental samples. These simulated environmental samples were processed using 10% hydrogen peroxide for 24 h to remove organic contamination and then identified using the strongest performing library. We found that libraries with fewer sample spectra produced lower correlation matches and that using derivative correction greatly reduced the number of inaccuracies in identifying materials as either natural or synthetic. We also found that environmental fouling reduced the correlation value of library matches when compared to pristine particles, however the effect was not consistent across the four materials tested. Overall, we found that the accuracy of automated library matching in the tested systems and processing routines varied from 64.1 to 98.0% for distinguishing between natural and synthetic materials, and that a high Hit Quality Index (HQI) did not always correlate with accuracy. These results are important for the microplastic field, demonstrating a need to rigorously test spectral libraries and processing routines with known materials to ensure identification accuracy.

Kozloski, Rachel↗
DOE OSTI2026

Advancing Artificial Intelligence with Liquid Argon Neutrino Experiments (Technical Report)

The grant allowed two main contributions: 1) The development of a first successful demonstration of the employment of Optimal Transport in liquid argon time projection chamber neutrino detectors. Optimal Transport, used in other contexts and specifically with LHC calorimetric data, was adapted to address a key particle identification challenge in LArTPCs: the separation of pi0 backgrounds from single-electrons produced in charged-current electron neutrino interactions. The work, leveraging ML methods such as k-nearest-neighbor (kNN) and support-vector-machine (SVM), showed an increase in background rejection of a factor of two or more. Work is now ongoing to incorporate this development in physics analyses for LArTPC experiments and more broadly expand the use of OT in LArTPC detectors including DUNE. This work was done in collaboration with the phenomenology group led by Nathaniel Craig at UCSB. 2) The deployment of NuGraph2, a graph neural network developed for LArTPC reconstruction, in the MicroBooNE experiment. NuGraph2 uses novel graph-neural-network methods on the rather simple LArTPC inputs of reconstructed hits, greatly simplifying the workflow compared to the use of waveform or signal-deconvolved wire ROIs. The network performed particle classification and was shown to address many challenging problems in LArTPC imaging including track-shower separation and the identification of protons and charged pions from primary muons. Our group collaborated with Giuseppe Cerati (FNAL scientist) who is one of the core developers of NuGraph2 to integrate this tool in MicroBooNE’s analysis framework. This consisted in tow key contributions: a) Studying performance on real data, which came with several months of iterations because the MC-trained version of the network was found to show significant bias that our group investigated and addressed. b) Integrating the output hit labeling of NuGraph2 into the existing particle tracking and shower reconstruction code. As a result of this work led by our team NuGraph2 is now enabling a suite of new analyses which benefit from enhanced capabilities and thus broader physics reach. The grant supported primarily the salary of UCSB graduate student Chuyue “Michaelia” Fang as well as partial summer salary support for PI Caratelli. Some funds were used for travel by Michaelia to ML related schools and conferences.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

FCC feasibility studies: Impact of tracker- and calorimeter-detector performance on jet flavor identification and Higgs physics analyses

The ambitious physics program planned for the Future Circular Collider electron-positron phase imposes stringent constraints on detector performance. This study systematically investigates how different detector configurations impact jet flavor identification and their effects on high-profile physics analyses. Using Higgs boson coupling measurements and searches for invisible Higgs decays as benchmarks, we evaluate the sensitivity of these analyses to variations in tracker and calorimeter detector properties. We examine modifications to single-point resolution, material budget, silicon layer placement, and particle identification capabilities, quantifying their effects on flavor-tagging performance. Additionally, we present the first comprehensive study of Higgs-to-invisible decay detection using full detector simulation, providing insights for optimizing detector designs at lepton colliders.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Identifying Neutrino Final States and Energies in MicroBooNE with New Deep-Learning Based LArTPC Reconstruction Frameworks

MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the MiniBooNE low energy excess. The LArTPC technology employed by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision and other machine learning techniques are promising tools for image processing that could boost efficiencies for selecting $\nu_{e}$-CC and other rare signals, reduce cosmic and beam-induced backgrounds, and improve the reconstruction of neutrino energies. The MicroBooNE experiment has been at the forefront of developing and testing such techniques for use in physics analyses. In this poster we overview deep-learning based reconstruction methods. We will showcase the use of a recurrent neural network to estimate neutrino energies and present a new reconstruction framework that uses convolutional neural networks to locate neutrino interaction vertices, tag pixels with track and shower labels, and perform particle identification on reconstructed clusters. We will present studies characterizing the performance of these new tools and demonstrate their effectiveness through their use in an inclusive $\nu_{e}$-CC event selection.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Identifying Neutrino Final States and Energies in MicroBooNE with New Deep-Learning Based LArTPC Reconstruction Frameworks

MicroBooNE, a Liquid Argon Time Projection Chamber (LArTPC) located in the $\nu_{\mu}$-dominated Booster Neutrino Beam at Fermilab, has been studying $\nu_{e}$ charged-current (CC) interaction rates to shed light on the MiniBooNE low energy excess. The LArTPC technology employed by MicroBooNE provides the capability to image neutrino interactions with mm-scale precision. Computer vision and other machine learning techniques are promising tools for image processing that could boost efficiencies for selecting $\nu_{e}$-CC and other rare signals, reduce cosmic and beam-induced backgrounds, and improve the reconstruction of neutrino energies. The MicroBooNE experiment has been at the forefront of developing and testing such techniques for use in physics analyses. In this poster we overview deep-learning based reconstruction methods. We will showcase the use of a recurrent neural network to estimate neutrino energies and present a new reconstruction framework that uses convolutional neural networks to locate neutrino interaction vertices, tag pixels with track and shower labels, and perform particle identification on reconstructed clusters. We will present studies characterizing the performance of these new tools and demonstrate their effectiveness through their use in an inclusive $\nu_{e}$-CC event selection.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Measurements of hadron production in 90 GeV/𝑐 proton-carbon interactions

This paper presents the multiplicity of neutral and charged hadrons produced in 90 GeV/𝑐 proton-carbon interactions from a dataset taken by the NA61/SHINE experiment in 2017. Particle identification via d⁢𝐸/d⁢𝑥 was performed for the charged hadrons 𝜋 ± , 𝐾 ± , and $p/\bar{p}$; the neutral hadrons $K^0_S$, $Λ$, and $\bar{Λ}$ were identified via an invariant mass analysis of their decays to charged hadrons. Double-differential multiplicity results as a function of laboratory momentum and polar angle are presented for each particle species; these results provide vital constraints on the predicted neutrino beam flux for current and future long-baseline neutrino oscillation experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Low Energy Neutron-induced Charged-particle (Z) (LENZ) instrument development with a focus on pulse shape discrimination for low-energy charged particles

To study neutron-induced charged-particle reactions with high precision, the Low Energy Neutron-induced Charged-particle (Z) instrument (LENZ) was developed at the Los Alamos Neutron Science Center. For the interest of measuring (n,p) and (n, α) reactions simultaneously, Pulse Shape Discrimination methods were investigated to identify different charged particles in the energy range of 3 - 20 MeV and improve signal-to-background ratios using Double-sided Silicon Strip Detectors and waveform digitizers. The risetime and pulse shape properties of detected charged particles were characterized for various silicon detector’s thickness with different orientations. During the post-processing of waveforms, we implemented different digital filters for effective particle identifications and improved energy- and timing- resolutions. We validated the optimized digital filters and pulse shape analyses, via measurements with 228 Th and 229 Th calibration sources, proton-induced reactions on a 7 LiF target, and neutron-induced reactions on CH 2 , Ta 2 O 5 , 58 Ni, and 6 LiF/ 59 Ni targets at the time-of-flight facility, LANSCE. In conclusion, the summary of effective thresholds and Figure of Merits on separating different charged particles is reported.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Modelling detector-specific reconstruction uncertainties in LAr-TPC

The Short-Baseline Neutrino (SBN) program features three Liquid Argon Time Projection Chamber (LAr-TPC) detectors positioned along the Booster Neutrino Beam (BNB) axis: the Short Baseline Neutrino Near Detector, MicroBooNE, and the ICARUS T600. As the largest operational LAr-TPC, ICARUS T600 serves as the far detector, located 600 m from the BNB target. While its primary goal is to record neutrino events, it also detects other ionizing events, including cosmic rays. This work focuses on analyzing and modeling detector-specific reconstruction uncertainties in LAr-TPC. These inefficiencies, identified during the Pattern Recognition phase handled by the PANDORA algorithm, impact subsequent Particle Fits and Offline Analysis. Specifically, inaccuracies in track reconstruction can lead to significant physical consequences, such as erroneous particle energy estimates and poor Particle Identification (PID), reducing the efficiency of neutrino event characterization. A key issue addressed is split tracks, caused by missing hits or incomplete track stitching by PANDORA. The aim of this internship is to characterize, model, and quantify the impact of split tracks on track reconstruction.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Event Reconstruction for Water-based Liquid Scintillator Detectors

Water Cherenkov and liquid scintillator detectors represent two complementary approaches in neutrino physics. Water Cherenkov detectors provide particle track direction and particle identification (PID) through Cherenkov ring topology, while liquid scintillator detectors offer higher light yield and lower energy thresholds. Water-based liquid scintillator (WbLS) is designed to combine the advantages of both technologies. However, the simultaneous detection of Cherenkov and scintillation light by photosensors introduces significant challenges for event reconstruction. This poster presents event reconstruction studies for WbLS detectors. A likelihood-based reconstruction framework, fiTQun, has been successfully used in Super-Kamiokande to reconstruct water Cherenkov events in cylindrical detectors. We extend and improve this framework to demonstrate event reconstruction in WbLS detectors, enabling the concurrent reconstruction of particle energy, PID, vertex, and direction. The results demonstrate competitive performance in energy resolution, PID separation, and vertex and direction reconstruction, highlighting the strong potential of WbLS for next-generation neutrino experiments.

Xie, Zhenxiong [Minnesota U.] (ORCID:0009000301442↗
NASA NTRS1994

A modular solid state detector for measuring high energy heavy ion fragmentation near the beam axis

A multi-element solid state detector has been designed to measure fluences of fragments produced near the beam axis by high energy heavy ion beams in thick targets. The detector is compact and modular, so as to be readily reconfigured according to the range of fragment charges and energies to be measured. Preamplifier gain settings and detector calibrations are adjustable remotely under computer control. We describe the central detector, its associated detectors and electronics, triggering scheme, data acquisition and particle identification techniques, illustrated by data taken with 600 MeV/u 56Fe beams and thick polyethylene targets at the LBL Bevalac. The applications of this work to space radiation protection are discussed.

NASA Center HQS↗