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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Searches for Light Dark Matter and Evidence of Coherent Elastic Neutrino-Nucleus Scattering of Solar Neutrinos with the LUX-ZEPLIN (LZ) Experiment

We present searches for light dark matter (DM) with masses 3–9 GeV/𝑐 2 in the presence of coherent elastic neutrino-nucleus scattering (CE⁢𝜈⁢NS) from 8 B solar neutrinos with the LUX-ZEPLIN experiment. This analysis uses a 5.7 tonne-yr exposure with data collected between March 2023 and April 2025. In an energy range spanning 1–6 keV, we report no significant excess of events attributable to dark matter nuclear recoils, but we observe a significant signal from 8 B CE ⁢𝜈 ⁢NS interactions that is consistent with expectation. We set world-leading limits on spin-independent and spin-dependent-neutron DM-nucleon interactions for masses down to 5 GeV/𝑐 2 . In the no-dark-matter scenario, we observe a signal consistent with 8 B CE⁢ 𝜈 ⁢NS events, corresponding to a 4.5⁢𝜎 statistical significance. This is the most significant evidence of 8 B CE 𝜈 ⁢NS interactions and is enabled by robust background modeling and mitigation techniques. This demonstrates LZ’s ability to detect rare signals at keV-scale energies.

Dark matter detectors↗
DOE OSTI2025

Expanding the physics reach of DUNE in the near and far detectors

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline neutrino oscillation experiment. Its primary goal is the determination of the neutrino mass hierarchy and the CP-violating phase. The DUNE physics programme also includes the detection of astrophysical neutrinos and the search for beyond the Standard Model (BSM) phenomena. DUNE will consist of a near detector (ND) complex placed at Fermilab, and a modular Liquid Argon Time Projection Chamber (LArTPC) far detector (FD) to be built in the Sanford Underground Research Facility (SURF), approximately 1300 km away from the neutrino production point. This thesis describes three different projects within DUNE. First, a novel strategy to improve the triggering capabilities of the DUNE FD is proposed. It uses matched filters to enhance the production of online hits across all charge collection planes. Next, the possibility of detecting neutrinos coming from dark matter (DM) annihilations in the Sun with the FD is explored. The complementarity of DUNE to this kind of DM searches is shown. Finally, the simulation and reconstruction framework of ND-GAr, the gas argon ND proposed for Phase II of DUNE, is presented. A number of additions to this are described, particularly focused on the development of the particle identification (PID) capabilities of the detector. These are then used to perform the first event selection studies with an end-to-end simulation in ND-GAr, in particular the selection of pion exclusive samples in $\nu_{\mu}$ CC interactions. All three of these projects share the common goal of enhancing the physics programme of DUNE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Extending the dark matter reach of water Cherenkov detectors using Jupiter

We propose the first method for water Cherenkov detectors to constrain GeV-scale dark matter (DM) below the solar evaporation mass. While previous efforts have highlighted the Sun and Earth as DM capture targets, we demonstrate that Jupiter is a viable target. Jupiter’s unique characteristics, such as its lower core temperature and significant gravitational potential, allow it to capture and retain light DM more effectively than the Sun, particularly in the mass range below 4 GeV where direct detection sensitivity diminishes. Our calculations provide the first sensitivity estimates to GeV-scale annihilating DM within Jupiter, predicting Hyper-K can reach spin dependent cross sections as low as $𝜎^{SD}_{𝑝⁢𝜒}$ = 2×10 −35 cm 2 for DM masses below 2 GeV. This surpasses current solar limits and direct detection results. We additionally provide estimates for Super-K ORCA, and the IceCube-Upgrade, showing that these experiments could provide complimentary bounds to direct detection experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Opportunities and challenges to study solar neutrinos with a Q-Pix pixel readout

The study of solar neutrinos presents significant opportunities in astrophysics, nuclear physics, and particle physics. However, the low-energy nature of these neutrinos introduces considerable challenges to isolate them from background events, requiring detectors with low-energy threshold, high spatial and energy resolutions, and low data rate. We present the study of solar neutrinos with a kiloton-scale liquid argon detector located underground, instrumented with a pixel readout using the Q-Pix technology. We explore the potential of using volume fiducialization, directional topological information, light signal coincidence, and pulse-shape discrimination to enhance solar neutrino sensitivity. We find that discriminating neutrino signals below 5 MeV is very difficult. However, we show that these methods are useful for the detection of solar neutrinos when external backgrounds are sufficiently understood and when the detector is built using low-background techniques. When building a workable background model for this study, we identify 𝛾 background from the cavern walls and from capture of 𝛼 particles in radon decay chains as both critical to solar neutrino sensitivity and significantly underconstrained by existing measurements. Finally, we highlight that the main advantage of the use of Q-Pix for solar neutrino studies lies in its ability to enable the continuous readout of all low-energy events with minimal data rates and manageable storage for further off-line analyses.

multi-purpose particle detectors↗
DOE OSTI2024

Combined Pre-supernova Alert System with KamLAND and Super-Kamiokande

Preceding a core-collapse supernova (CCSN), various processes produce an increasing amount of neutrinos of all flavors characterized by mounting energies from the interior of massive stars. Among them, the electron antineutrinos are potentially detectable by terrestrial neutrino experiments such as KamLAND and Super-Kamiokande (SK) via inverse beta decay interactions. Once these pre-supernova (pre-SN) neutrinos are observed, an early warning of the upcoming CCSN can be provided. In light of this, KamLAND and SK, both located in the Kamioka mine in Japan, have been monitoring pre-SN neutrinos since 2015 and 2021, respectively. Recently, we performed a joint study between KamLAND and SK on pre-SN neutrino detection. A pre-SN alert system combining the KamLAND detector and the SK detector was developed and put into operation, which can provide a supernova alert to the astrophysics community. Fully leveraging the complementary properties of these two detectors, the combined alert is expected to resolve a pre-SN neutrino signal from a 15 M ⊙ star within 510 pc of the Earth at a significance level corresponding to a false alarm rate of no more than 1 per century. For a Betelgeuse-like model with optimistic parameters, it can provide early warnings up to 12 hr in advance.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Machine Learning for DUNE Supernova Trigger

One of the major scientific goals of the Deep Underground Neutrino Experiment (DUNE) is to detect and measure the neutrino flux from galactic core-collapse supernovae. These neutrinos, which exist in the low energy range of up to a few tens of MeV and are responsible for carrying away over 99% of the gravitational binding energy of the supernova, provide an opportunity to study the end of life evolution of massive stars, as well as unique properties and interactions of neutrinos. Because galactic supernovae are expected to occur only on the timespan of every few decades, it is crucial that DUNE is able to detect supernova neutrino interactions when they occur. However, detecting these supernova interactions requires sifting through a large amount of data, and DUNE detectors require a trigger to signal when supernova neutrino events occur. Machine learning provides a potential approach to creating this trigger. This project generates ADC and ground truth images of neutrino interactions in a LArTPC detector as simulated by the Model of Argon Reaction Low Energy Yields (MARLEY) to be used for machine learning. The eventual goal of this work is to facilitate DUNE s detection of supernova neutrino interactions by building a machine learning pipeline that will train the trigger algorithm.

Damish, Stephanie↗
DOE OSTI2024

Machine Learning for DUNE Supernova Trigger

One of the major scientific goals of the Deep Underground Neutrino Experiment (DUNE) is to detect and measure the neutrino flux from galactic core-collapse supernovae. These neutrinos, which exist in the low energy range of up to a few tens of MeV and are responsible for carrying away over 99% of the gravitational binding energy of the supernova, provide an opportunity to study the end of life evolution of massive stars, as well as unique properties and interactions of neutrinos. Because galactic supernovae are expected to occur only on the timespan of every few decades, it is crucial that DUNE is able to detect supernova neutrino interactions when they occur. However, detecting these supernova interactions requires sifting through a large amount of data, and DUNE detectors require a trigger to signal when supernova neutrino events occur. Machine learning provides a potential approach to creating this trigger. This project generates ADC and ground truth images of neutrino interactions in a LArTPC detector as simulated by the Model of Argon Reaction Low Energy Yields (MARLEY) to be used for machine learning. The eventual goal of this work is to facilitate DUNE s detection of supernova neutrino interactions by building a machine learning pipeline to train the trigger algorithm.

Damish, Stephanie↗
DOE OSTI2023

Exclusion and Verification of Remote Nuclear Reactors with a 1-kiloton Gd -Doped Water Detector

To date, antineutrino experiments built for the purpose of demonstrating a nonproliferation capability have typically employed organic scintillators situated as close to the core as possible (typically at a distance of a few meters to tens of meters) and have not exceeded a few tons in size. One problem with this approach is that proximity to the reactor core requires accommodation by the host facility. Water Cherenkov detectors located offsite, at distances of a few kilometers or greater, may facilitate nonintrusive monitoring and verification of reactor activities over a large area. As the standoff distance increases, the detector target mass must scale accordingly. This paper quantifies the degree to which a kiloton-scale gadolinium-doped water Cherenkov detector can exclude the existence of undeclared reactors within a specified distance, and remotely detect the presence of a hidden reactor in the presence of declared reactors, by verifying the operational power and standoff distance using a Feldman-Cousins-based likelihood analysis. A 1-kton scale (fiducial) water Cherenkov detector can exclude gigawatt-scale nuclear reactors up to tens of kilometers within a year. In conclusion, when attempting to identify the specific range and power of a reactor, the detector energy resolution is not sufficient to delineate between the two.

42 ENGINEERING↗
DOE OSTI2022

Detection of astrophysical tau neutrino candidates in IceCube

High-energy tau neutrinos are rarely produced in atmospheric cosmic-ray showers or at cosmic particle accelerators, but are expected to emerge during neutrino propagation over cosmic distances due to flavor mixing. When high energy tau neutrinos interact inside the IceCube detector, two spatially separated energy depositions may be resolved, the first from the charged current interaction and the second from the tau lepton decay. We report a novel analysis of 7.5 years of IceCube data that identifies two candidate tau neutrinos among the 60 “High-Energy Starting Events” (HESE) collected during that period. The HESE sample offers high purity, all-sky sensitivity, and distinct observational signatures for each neutrino flavor, enabling a new measurement of the flavor composition. The measured astrophysical neutrino flavor composition is consistent with expectations, and an astrophysical tau neutrino flux is indicated at 2.8σ significance.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Directional detection of dark matter with diamond

Searches for weakly interacting massive particle (WIMP) dark matter will in the near future be sensitive to solar neutrinos. Directional detection offers a method to reject solar neutrinos and improve WIMP searches, but reaching that sensitivity with existing directional detectors poses challenges. We propose a combined atomic/particle physics approach using a large-volume diamond detector. WIMP candidate events trigger a particle detector, after which spectroscopy of nitrogen vacancy (NV) centers reads out the direction of the incoming particle. Here we discuss the current state of technologies required to realize directional detection in diamond and present a path towards a detector with sensitivity below the neutrino floor.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

New Physics from the Neutrino Portal: Early Universe Implications and Detection Prospects

Despite their ubiquity, the nature of Dark Matter (DM) and neutrinos remains mysterious. Both are examples of particles which are neutral, stable, and require physics beyond the Standard Model (SM) of particle physics. It is therefore natural to investigate theories which relate them. If DM has more than gravitational interactions, it must not couple very sizably to the Standard Mode (SM) in order to have escaped detection so far. The nature of this coupling (or “portal”) is of paramount importance in determining the optimal experimental search strategies. This proposal aims to focus on the implications of “Hidden Sector” models of new physics where the dominant interaction is from the neutrino-mixing portal. Such scenarios give rise to new neutrino self-interactions and DM-neutrino interactions with a variety of consequences. By undertaking the thorough study of neutrino portal (NP) theories of DM this proposal seeks to achieve the following specific objectives: (1) Expand and deepen the theoretical foundations of self-interacting neutrinos in the early universe with application to sterile neutrino DM, (2) establish theoretical and phenomenological bases for DM-modified neutrino oscillations, (3) establish sensitivity projections for neutrino self-interactions from future IceCube data. The proposed research will intellectually contribute to the physics literature by thoroughly characterizing the consequences of “hidden sector” models incorporating DM and neutrinos. Theoretical consistency and a complete description of their impact on the early universe will constitute one component of this work. As a result of the additional structure in the neutrino sector the detectability of various aspects of neutrino physics will be impacted. This includes modifications to oscillation experiments, ultra-high-energy neutrino telescopes (e.g. IceCube, KM3NET), and Cosmic Microwave Background data. The success of this research proposal will depend on the successful integration of these complementary probes of new physics in the proper context of theoretically motivated scenarios determined from their impact on the early universe.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

A pion-argon cross section measurement in the ProtoDUNE-SP experiment with cosmogenic muon

Neutrinos are tiny mysterious fundamental particles with small cross sections. Through neutrino physics, scientists across the world are trying to answer many intriguing questions about nature such as the dominance of matter over antimatter, CP violation in the lepton sector, number of supernovas in the early universe, etc. Detection of neutrinos requires massive particle detectors and intense neutrino beam owing to their small cross section. Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment that is planned to start taking data beginning in 2026. DUNE will consist of 4 massive detectors, the first of which will be using single-phase liquid argon time projection chamber (LArTPC) technology. The ProtoDUNE-SP experiment is a prototype of the DUNE built at the CERN neutrino platform and uses the same detector technology that will be used in DUNE first module. The ProtoDUNE-SP experiment collected months of test beam and cosmic ray data beginning in September 2018. It was built to provide a testbed for the installation of detector parts for DUNE, showing long-term stability of the detector, understanding detector response for different test beam particles (including protons, pions, electrons, kaons, muons), and measurement of hadron-argon cross sections. When a particle passes through LArTPC electron-ion pairs are produced. To reconstruct the position and energy of a particle passing through the medium knowledge of ionization electron drift velocity is essential. The electron drift velocity is distorted by an excess positive charge built up in the detector, known as space charge. This study discusses a novel technique for measuring the ionization electron drift velocity using cosmic-ray muons. The technique uses tracks that travel the entire drift distance of the TPC for drift velocity determination. Secondly, the study discusses a method for converting the charge deposited into energy. The method is carried out in two step s. In th e first step detector response for energetic cosmic ray muons crossing the entire the TPC is used to make the charge deposition uniform throughout the TPC, and in the second step stopping cosmic-ray muons are used for determining the energy scale. Finally, the study discusses a pion-argon cross section measurement based on reweighting of Monte Carlo simulations using J. Calcutt's Geant4Reweight framework. Neutrinos cannot be directly detected; they are identified based on the interaction products. Pions are a common interaction product in a neutrino interaction. For precise modeling of neutrino event generators, it is essential to understand the pion-argon interaction. Pion-argon cross section measurement serves as an important input for neutrino interaction models. The results of the pion-argon total reaction cross section using the Geant4 reweighting technique are found to be in good agreement with Geant4 predictions. The many studies carried out in the ProtoDUNE-SP experi ment wil l be useful for current and future neutrino experiments using LArTPC technology including ICARUS, MicroBooNE, DUNE

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

DUNE Photon Detection System

The Deep Underground Neutrino Experiment (DUNE) aims to provide a broad physics program primarily addressed to probing CP violation in the neutrino sector and identifying the neutrino mass hierarchy. The search for proton decay, the observation of supernova neutrino bursts, and the investigation of solar neutrinos represent other additional goals of DUNE experiment, which can be enhanced by the use and the high performances of the Photon Detection System (PDS). Using the technology based on liquid argon Time Projection Chamber (LArTPC), the experiment plans to observe neutrino interactions inside detectors located 1300 km away from the Long Baseline Neutrino Facility (LBNF) at Fermi National Accelerator Laboratory (FNAL), where the neutrinos are produced. The experiment consists of two main parts, respectively the far and near detectors. The far site will comprise four detector modules. The first module is constituted by a vertical drift single-phase LArTPC, while the second module provides a horizontal drift single-phase LArTPC. The configurations of the other modules are still under definition. Neutrino detection in LArTPCs is achieved by identifying the charge and light generated from its interactions with liquid argon. The wire planes of the instrumented anode and the PDS detect these signals, respectively. The PDS in the first two modules, detailed in the present document, uses a modified version of the so-called ARAPUCA technology, named X-ARAPUCA. This system consisting of a highly reflecting box with an entrance window made by dichroic filters and wavelength shifters, creates a trap to detect the VUV (128 nm) scintillation photons. The X-ARAPUCA of the first module is called Supercell, and it has dimensions of 488×100 mm 2 , while Megacell is the second module version, with an active area of 60×60 cm 2 . This latter configuration also represents a significant technological advancement. Since half of the modules are placed on the cathode at high voltage, they are powered and read out using innovative power-over-fiber (PoF) and signal-over-fiber (SoF) technologies. Meanwhile, the other half are installed in a membrane behind the field cage, with a total transparency of around 70%.

Neutrino detectors↗
DOE OSTI2025

Towards combination of charge and light readout at O(10) MeV energies in DUNE

The Deep Underground Neutrino Experiment (DUNE) is an experiment under construction that will employ 10-kt scale liquid-argon-TPC technology to do precision measurements of neutrino oscillations. Apart from the determination of CP violation in neutrino interactions, one of the main scientific goals of DUNE is the detection of neutrinos from a supernova burst. It is imperative to maximise the potential of the detector for extracting information for these rare events. The use of light signals to aid the calorimetry of neutrino interactions provides such potential. We will present current efforts to demonstrate the application of combined calorimetry in one of the DUNE prototype detectors at CERN. The ground-level placement of the detector allows collection of large samples of cosmic-ray muons stopping within its volume. Michel electrons from muon decay can be identified in both the ionization charge and scintillation light, serving as a proxy for supernova neutrino interactions at O(10) MeV energies. The method for demonstrating combined calorimetry and its associated challenges will be introduced. We will also summarise the current analysis status and future prospects.

Pěč, Viktor [Prague, Inst. Phys.] (ORCID:000000034↗
DOE OSTI2022

First Measurement of Differential Charged Current Quasi-Elastic-Like $\nu_{\mu}$ Argon Scattering Cross Sections with the MicroBooNE Detector

Current and future generation neutrino oscillation experiments aim towards a high-precisionmeasurement of the oscillation parameters, which requires an unprecedented understanding ofneutrino-nucleus scattering. Charged-current quasi-elastic (CCQE) scattering is the process inwhich the neutrino produces a charged lepton and removes a single intact nucleon from the nu-cleus without producing any additional particles. For existing and forthcoming accelerator–based neutrino experiments, CCQE interactions are either the dominant process or part of the signal.MicroBooNE is the first liquid argon time projection chamber (LArTPC) commissioned as part ofthe Short Baseline Neutrino (SBN) program at Fermilab and its excellent particle reconstructioncapabilities allow the detection of neutrino interactions using exclusive final states, which will playa crucial role in the success of future kiloton LArTPC detectors such as DUNE. This talk willpresent the first measurement of exclusive νμ-Ar CCQE–like flux integrated total and differentialcross sections using single proton knock–out interactions recorded by the MicroBooNE LArTPCdetector, which has comparable acceptance to deuterium Bubble Chambers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Machine Learning-Based Extreme Data Reduction for Prompt Supernova Pointing at DUNE

One of the goals of the Deep Underground Neutrino Experiment (DUNE) is to use the massive underground liquid argon time projection chamber (LArTPC) detectors at its far site for multimessenger astronomy (MMA), in the detection of neutrinos from core-collapse supernovae (SNe). Its current baseline trigger strategy detects activity in the detector that is consistent with supernova (SN) neutrinos and saves the raw data for further offline analysis but provides no prompt pointing information crucial for optical follow-ups by other observatories. This approach is based on the assumption that prompt pointing determination using raw data is computationally prohibitive. In this article, we demonstrate a proof-of-concept based on applying extreme data reduction on the buffered SN data in the DUNE data acquisition (DAQ) system’s front-end computers using a machine learning (ML) workflow. This reduces the data by ~5 orders of magnitude, allowing a full track reconstruction to be carried out quickly on a single server. The total time to perform the ML-based data reduction and the full track reconstruction is less than the time to transfer the SN data back to Fermilab or a high-performance computing (HPC) center. This shows that prompt processing of raw SN data is possible and, in fact, trivial once the data have been reduced to reject radiological backgrounds, paving the way to a high-quality SN pointing trigger that is based on fully reconstructed data instead of trigger primitives (TPs).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Machine Learning for DUNE Supernova Trigger

One of the major scientific goals of the Deep Underground Neutrino Experiment (DUNE) is to detect and measure the neutrino flux originating from galactic core-collapse supernovae. These neutrinos provide an opportunity to study the end of life evolution of massive stars, and reveal information about the structure of core-collapse that is not visible in observations of the electromagnetic spectrum. Because of the rarity of these events, it is crucial that DUNE is able to detect supernova neutrino interactions when they occur. However, this will require sifting through a large quantity of data, motivating the development of a trigger algorithm to identify significant events and discard irrelevant data. Machine learning provides a potential approach to building this trigger. This project generates ADC and ground truth images of simulated neutrino interactions in a LArTPC detector to be used for machine learning, and uses them to train a sparse Convolutional Neural Network (C NN). The performance of this model when applied to the task of pixel classification based on interaction type is examined. This project found that the sparse CNN approach has the potential to have high accuracy in pixel classification, meaning it may be highly relevant to the development of a supernova neutrino trigger for the DUNE far detector.

Damish, S.↗
DOE OSTI2024

Accessing new physics with an undoped, cryogenic CsI CEvNS detector for COHERENT at the SNS

We consider the potential for a 10 kg undoped cryogenic CsI detector operating at the Spallation Neutron Source to measure coherent elastic neutrino-nucleus scattering and its sensitivity to discover new physics beyond the standard model (BSM). Through a combination of increased event rate, lower threshold, and good timing resolution, such a detector would significantly improve on past measurements. We considered tests of several BSM scenarios such as neutrino nonstandard interactions and accelerator-produced dark matter. This detector’s performance was also studied for relevant questions in nuclear physics and neutrino astronomy, namely the weak charge distribution of Cs and I nuclei and detection of neutrinos from a core-collapse supernova. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗