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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 OSTI2025

Multi-channel, multi-template event reconstruction for SuperCDMS data using machine learning

SuperCDMS SNOLAB uses kilogram-scale germanium and silicon detectors to search for dark matter. Each detector has Transition Edge Sensors (TESs) patterned on the top and bottom faces of a large crystal substrate, with the TESs electrically grouped into six phonon readout channels per face. Noise correlations are expected among a detector's readout channels, in part because the channels and their readout electronics are located in close proximity to one another. Moreover, owing to the large size of the detectors, energy deposits can produce vastly different phonon propagation patterns depending on their location in the substrate, resulting in a strong position dependence in the readout-channel pulse shapes. Both of these effects can degrade the energy resolution and consequently diminish the dark matter search sensitivity of the experiment if not accounted for properly. We present a new algorithm for pulse reconstruction, mathematically formulated to take into account correlated noise and pulse shape variations. This new algorithm fits N readout channels with a superposition of M pulse templates simultaneously - hence termed the N$\times$M filter. We describe a method to derive the pulse templates using principal component analysis (PCA) and to extract energy and position information using a gradient boosted decision tree (GBDT). We show that these new N$\times$M and GBDT analysis tools can reduce the impact from correlated noise sources while improving the reconstructed energy resolution for simulated mono-energetic events by more than a factor of three and for the 71Ge K-shell electron-capture peak recoils measured in a previous version of SuperCDMS called CDMSlite to $<$ 50 eV from the previously published value of $\sim$100 eV. These results lay the groundwork for position reconstruction in SuperCDMS with the N$\times$M outputs.

Albakry, M. F. [British Columbia U.; TRIUMF]↗
DOE OSTI2024

Multi-track event reconstruction to constrain the p ̄ background in Mu2e

Mu2e experiment will search for the neutrinoless, coherent conversion of μ − → e − in the field of an Al nucleus. One of the expected backgrounds is p ̄ ’s annihilating in the stopping target to produce signal-like e − ’s. Although not a dominant background, it has a large uncertainty and cannot be suppressed by the timing cuts used to reduce the prompt background. However, at Mu2e energies, p p ̄ annihilation is the only source of events with multiple, simultaneous particles coming from the stopping target. We utilized this unique feature and developed a novel way to reconstruct multi-track events and estimate the p ̄ background.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2016

SIRENA Software for Athena X-IFU Event Reconstruction

The X-ray Observatory Athena was proposed in April 2014 as the mission to implement the science theme "The Hot and Energetic Universe" selected by ESA for L2 (the second Large-class mission in ESAs Cosmic Vision science programme). One of the two X-ray detectors designed to be onboard Athena is X-IFU, a cryogenic microcalorimeter based on Transition Edge Sensor (TES) technology that will provide spatially resolved high-resolution spectroscopy. X-IFU will be developed by an international consortium led by IRAP (PI), SRON (co-PI) and IAPS/INAF (co-PI) and involving ESA Member States, Japan and the United States. In Spain, IFCA (CSIC-UC) has an anticipated contribution to X-IFU through the Digital Readout Electronics (DRE) unit, in particular in the Event Processor Subsytem. For this purpose and in collaboration with the Athena end-to-end simulations team, we are currently developing the SIRENA package as part of the publicly available SIXTE end-to-end simulator. SIRENA comprises a set of processing algorithms aimed at recognizing, from a noisy signal, the intensity pulses generated by the absorption of the X-ray photons, to lately reconstruct their energy, position and arrival time. This poster describes the structure of the package and the different algorithms currently implemented as well as their comparative performance in the energy resolution achieved in the reconstruction of the instrument events.

M T Ceballos↗
DOE OSTI2020

Lowering the Energy Threshold of the CUORE Experiment: Benefits in the Surface Alpha Events Reconstruction

CUORE is a tonne-scale cryogenic experiment located at the Laboratori Nazionali del Gran Sasso that exploits bolometric technique to search for neutrinoless double beta decay of 130 Te. Thanks to its very low background and large mass, CUORE is also a powerful tool to study a broad class of phenomena, such as solar axions and WIMP scattering. The ability to conduct such sensitive searches crucially depends on the energy threshold, which has to be kept as low as possible. Here, we show how the trigger algorithm affects the sensitivity to low-energy phenomena and the interpretation of the energy spectrum. In particular, we focus on the impact that the trigger algorithm has on the identification of the coincidence events among different crystals and, consequently, on the reconstruction of the background.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Graph neural network for neutrino physics event reconstruction

Liquid argon time projection chamber (LArTPC) detector technology offers a wealth of high-resolution information on particle interactions, and leveraging that information to its full potential requires sophisticated automated reconstruction techniques. Here, this article describes NUGRAPH 2, a graph neural network for low-level reconstruction of simulated neutrino interactions in a LArTPC detector. Simulated neutrino interactions in the MicroBooNE detector geometry are described as heterogeneous graphs, with energy depositions on each detector plane forming nodes on planar subgraphs. The network utilizes a multihead attention message-passing mechanism to perform background filtering and semantic labeling on these graph nodes, identifying those associated with the primary physics interaction with 98.0% efficiency and labeling them according to particle type with 94.9% efficiency. The network operates directly on detector observables across multiple two-dimensional representations but utilizes a three-dimensional-context-aware mechanism to encourage consistency between these representations. Model inference takes 0.12 s / event on a CPU and 0.005 s / event batched on a GPU. This architecture is designed to be a general-purpose solution for particle reconstruction in neutrino physics, with the potential for deployment across a broad range of detector technologies, and offers a core convolution engine that can be leveraged for a variety of tasks beyond the two described in this paper.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Search for an eV-Scale Sterile Neutrino Using Improved High-Energy νμ Event Reconstruction in IceCube

This Letter presents the result of a 3+1 sterile neutrino search using 10.7 yr of IceCube data. We analyze atmospheric muon neutrinos that traverse the Earth with energies ranging from 0.5 to 100 TeV, incorporating significant improvements in modeling neutrino flux and detector response compared to earlier studies. Notably, for the first time, we categorize data into starting and throughgoing events, distinguishing neutrino interactions with vertices inside or outside the instrumented volume, to improve energy resolution. The best-fit point for a 3+1 model is found to be at sin^{2}(2θ_{24})=0.16 and Δm_{41}^{2}=3.5 eV^{2}, which agrees with previous iterations of this Letter. The result is consistent with the null hypothesis of no sterile neutrinos with a p value of 3.1%.

Neutrino detection↗
DOE OSTI2021

Event reconstruction in a liquid xenon Time Projection Chamber with an optically-open field cage

nEXO is a proposed tonne-scale neutrinoless double beta decay (0νββ) experiment using liquid 136 Xe (LXe) in a Time Projection Chamber (TPC) to read out ionization and scintillation signals. Between the field cage and the LXe vessel, a layer of LXe (“skin” LXe) is present, where no ionization signal is collected. Only scintillation photons are detected, owing to the lack of optical barrier around the field cage. Here, in this work, we show that the light originating in the skin LXe region can be used to improve background discrimination by 5% over previous published estimates. This improvement comes from two elements. First, a fraction of the γ-ray background is removed by identifying light from interactions with an energy deposition in the skin LXe. Then, background from 222 Rn dissolved in the skin LXe can be efficiently rejected by tagging the α decay in the 214 Bi- 214 Po chain in the skin LXe.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Machine learning for single-ended event reconstruction in PROSPECT experiment

The Precision Reactor Oscillation and Spectrum Experiment, PROSPECT, was a segmented antineutrino detector that successfully operated at the High Flux Isotope Reactor in Oak Ridge, TN, during its 2018 run. Despite challenges with photomultiplier tube base failures affecting some segments, innovative machine learning approaches were employed to perform position and energy reconstruction, and particle classification. This work highlights the effectiveness of convolutional neural networks and graph convolutional networks in enhancing data analysis. By leveraging these techniques, a 3.3% increase in effective statistics was achieved compared to traditional methods, showcasing their potential to improve analysis performance. Furthermore, these machine learning methodologies offer promising applications for other segmented particle detectors, underscoring their versatility and impact.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Semantic segmentation with a sparse convolutional neural network for event reconstruction in MicroBooNE

We present the performance of a semantic segmentation network, SparseSSNet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time projection chamber for the study of neutrino properties and interactions. SparseSSNet is a submanifold sparse convolutional neural network, which provides the initial machine learning based algorithm utilized in one of MicroBooNE's ν e -appearance oscillation analyses. The network is trained to categorize pixels into five classes, which are re-classified into two classes more relevant to the current analysis. The output of SparseSSNet is a key input in further analysis steps. This technique, used for the first time in liquid argon time projection chambers data and is an improvement compared to a previously used convolutional neural network, both in accuracy and computing resource utilization. Here, the accuracy achieved on the test sample is ≥ 99%. For full neutrino interaction simulations, the time for processing one image is ≈ 0.5 sec, the memory usage is at 1 GB level, which allows utilization of most typical CPU worker machine.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Methods and stability tests associated with the sterile neutrino search using improved high-energy ν μ event reconstruction in IceCube

We provide supporting details for the search for a 3 + 1 sterile neutrino using data collected over 10.7 years at the IceCube Neutrino Observatory. The analysis uses atmospheric muon-flavored neutrinos from 0.5 to 100 TeV that traverse Earth to reach the IceCube detector and finds a best-fit point at sin 2 ( 2 θ 24 ) = 0.16 and Δ m 41 2 = 3.5 eV 2 with a goodness-of-fit p value of 12% and consistency with the null hypothesis of no oscillations to sterile neutrinos with a p value of 3.1%. Several improvements were made over past analyses, which are reviewed in this article, including upgrades to the reconstruction and the study of sources of systematic uncertainty. We provide details of the fit quality and discuss stability tests that split the data for separate samples, comparing results. We find that the fits are consistent between split datasets. Published by the American Physical Society 2024

Abbasi, R.↗
DOE OSTI2024

NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore needed to unlock the full potential of LArTPC experiments. NuGraph2 is a state of the art Graph Neural Network for reconstruction of data in LArTPC experiments. NuGraph2 utilizes a heterogeneous graph structure, with separate subgraphs of 2D nodes (hits in each plane) connected across planes via 3D nodes (space points). The model provides a consistent description of the neutrino interaction across all planes. NuGraph2 is a multi-purpose network, with a common message-passing attention engine connected to multiple decoders with different classification or regression tasks. These include the classification of detector hits according to the particle type that produced them (semantic segmentation) and the separation of hits from the neutrino interaction from hits due to noise or cosmic-ray background. Additional decoders are being developed, performing tasks such as the regression of the neutrino interaction vertex position. Performance results will be presented based on publicly available samples from MicroBooNE. These include both physics performance metrics, achieving 95% accuracy for semantic segmentation and 98% classification of neutrino hits, as well as computational metrics for training and for inference on CPU or GPU. The status of the NuGraph integration in the LArSoft software framework will be presented, as well as initial studies about model interpretability and injection of domain knowledge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1998

Automated Track Recognition and Event Reconstruction in Nuclear Emulsion

The major advantages of nuclear emulsion for detecting charged particles are its submicron position resolution and sensitivity to minimum ionizing particles. These must be balanced, however, against the difficult manual microscope measurement by skilled observers required for the analysis. We have developed an automated system to acquire and analyze the microscope images from emulsion chambers. Each emulsion plate is analyzed independently, allowing coincidence techniques to be used in order to reject back- ground and estimate error rates. The system has been used to analyze a sample of high-multiplicity Pb-Pb interactions (charged particle multiplicities approx. 1100) produced by the 158 GeV/c per nucleon Pb-208 beam at CERN. Automatically reconstructed track lists agree with our best manual measurements to 3%. We describe the image analysis and track reconstruction techniques, and discuss the measurement and reconstruction uncertainties.

Deines-Jones, P.↗
NASA NTRS2012

Multi-Mission Simulation and Visualization for Real-Time Telemetry Display, Playback and EDL Event Reconstruction

he Jet Propulsion Laboratory's Entry, Descent and Landing (EDL) Reconstruction Task has developed a software system that provides mission operations personnel and analysts with a real time telemetry-based live display, playback and post-EDL reconstruction capability that leverages the existing high-fidelity, physics-based simulation framework and modern game engine-derived 3D visualization system developed in the JPL Dynamics and Real Time Simulation (DARTS) Lab. Developed as a multi-mission solution, the EDL Telemetry Visualization (ETV) system has been used for a variety of projects including NASA's Mars Science Laboratory (MSL), NASA'S Low Density Supersonic Decelerator (LDSD) and JPL's MoonRise Lunar sample return proposal.

Entry, Descent and Landing (EDL) Reconstruction↗