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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 OSTI2024

Reduced-Order Modeling of Multigroup Neutron Cross Sections for High-Temperature Gas-cooled Reactors

Abstract – Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which consist of databases of tabulated values, used to calculate the neutron cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of microscopic cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. In order to address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multigroup cross section data across isotopes, reaction types, and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs have been trained for all isotopes in this work and systematic Griffin testing is ongoing to ensure the feasibility of this ROM technique for predicting cross section and reducing memory requirements without a significant sacrifice in computational performance.

42 - ENGINEERING↗
DOE OSTI2024

Advanced Cross Section Library Generation using Reduced Order Models

Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which consist of databases of tabulated values, used to calculate the neutron cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of microscopic cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. In order to address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multigroup cross section data across isotopes, reaction types, and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs have been trained for all isotopes in this work and systematic Griffin testing is ongoing to ensure the feasibility of this ROM technique for predicting cross section and reducing memory requirements without a significant sacrifice in computational performance.

42 - ENGINEERING↗
DOE OSTI2012

CONSTRAINING MASS RATIO AND EXTINCTION IN THE FU ORIONIS BINARY SYSTEM WITH INFRARED INTEGRAL FIELD SPECTROSCOPY

We report low-resolution near-infrared spectroscopic observations of the eruptive star FU Orionis using the Integral Field Spectrograph (IFS) Project 1640 installed at the Palomar Hale telescope. This work focuses on elucidating the nature of the faint source, located 0.''5 south of FU Ori, and identified in 2003 as FU Ori S. We first use our observations in conjunction with published data to demonstrate that the two stars are indeed physically associated and form a true binary pair. We then proceed to extract J- and H-band spectro-photometry using the damped LOCI algorithm, a reduction method tailored for high contrast science with IFS. This is the first communication reporting the high accuracy of this technique, pioneered by the Project 1640 team, on a faint astronomical source. We use our low-resolution near-infrared spectrum in conjunction with 10.2 {mu}m interferometric data to constrain the infrared excess of FU Ori S. We then focus on estimating the bulk physical properties of FU Ori S. Our models lead to estimates of an object heavily reddened, A{sub V} = 8-12, with an effective temperature of {approx}4000-6500 K. Finally, we put these results in the context of the FU Ori N-S system and argue that our analysis provides evidence that FU Ori S might be the more massive component of this binary system.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Dual energy CT: How well can pseudo-monochromatic imaging reduce metal artifacts?

Purpose: Dual Energy CT (DECT) provides so-called monoenergetic images based on a linear combination of the original polychromatic images. At certain patient-specific energy levels, corresponding to certain patient- and slice-dependent linear combination weights, e.g., E = 160 keV corresponds to α = 1.57, a significant reduction of metal artifacts may be observed. The authors aimed at analyzing the method for its artifact reduction capabilities to identify its limitations. The results are compared with raw data-based processing. Methods: Clinical DECT uses a simplified version of monochromatic imaging by linearly combining the low and the high kV images and by assigning an energy to that linear combination. Those pseudo-monochromatic images can be used by radiologists to obtain images with reduced metal artifacts. The authors analyzed the underlying physics and carried out a series expansion of the polychromatic attenuation equations. The resulting nonlinear terms are responsible for the artifacts, but they are not linearly related between the low and the high kV scan: A linear combination of both images cannot eliminate the nonlinearities, it can only reduce their impact. Scattered radiation yields additional noncanceling nonlinearities. This method is compared to raw data-based artifact correction methods. To quantify the artifact reduction potential of pseudo-monochromatic images, they simulated the FORBILD abdomen phantom with metal implants, and they assessed patient data sets of a clinical dual source CT system (100, 140 kV Sn) containing artifacts induced by a highly concentrated contrast agent bolus and by metal. In each case, they manually selected an optimal α and compared it to a raw data-based material decomposition in case of simulation, to raw data-based material decomposition of inconsistent rays in case of the patient data set containing contrast agent, and to the frequency split normalized metal artifact reduction in case of the metal implant. For each case, the contrast-to-noise ratio (CNR) was assessed. Results: In the simulation, the pseudo-monochromatic images yielded acceptable artifact reduction results. However, the CNR in the artifact-reduced images was more than 60% lower than in the original polychromatic images. In contrast, the raw data-based material decomposition did not significantly reduce the CNR in the virtual monochromatic images. Regarding the patient data with beam hardening artifacts and with metal artifacts from small implants the pseudo-monochromatic method was able to reduce the artifacts, again with the downside of a significant CNR reduction. More intense metal artifacts, e.g., as those caused by an artificial hip joint, could not be suppressed. Conclusions: Pseudo-monochromatic imaging is able to reduce beam hardening, scatter, and metal artifacts in some cases but it cannot remove them. In all cases, the CNR is significantly reduced, thereby rendering the method questionable, unless special post processing algorithms are implemented to restore the high CNR from the original images (e.g., by using a frequency split technique). Raw data-based dual energy decomposition methods should be preferred, in particular, because the CNR penalty is almost negligible.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-F-303-02: Achieving 4D MRI in Regular Breathing Cycle with Extended Acquisition Time of Dynamic MR Images

Purpose: The purpose of this study is to develop a technique to obtain four-dimensional (4D) magnetic resonance (MR) images that are more representative of a patient’s typical breathing cycle by utilizing an extended acquisition time while minimizing the image artifacts. Methods: The 4D MR data were acquired with the balanced steady state free precession in two-dimensional sagittal plane of view. Each slice was acquired repeatedly for about 15 s, thereby obtaining multiple images at each of the 10 phases in the respiratory cycle. This improves the probability that at least one of the images were acquired at the desired phase during a regular breathing cycle. To create optimal 4D MR images, an iterative approach was used to identify the set of images that yielded the highest slice-to-slice similarity. To assess the effectiveness of the approach, the data set was truncated into periods of 7 s (50 time points), 11 s (75 time points) and the full 15 s (100 time points). The 4D MR images were then sorted with data of the three different acquisition periods for comparison. Results: In general, the 4D MR images sorted using data from longer acquisition periods showed less mismatched artifacts. In addition, the normalized cross correlation (NCC) between slices of a 4D volume increases with increased acquisition period. The average NCC was 0.791 from the 7 s period, 0.794 from the 11 s period and 0.796 from the 15 s period. Conclusion: Our preliminary study showed that extending the acquisition time with the proposed sorting technique can improve image quality and reduce artifact presence in the 4D MR images. Data acquisition over two breathing cycles is a good trade-off between artifact reduction and scan time. This research was partially funded by the the Center for Radiation Oncology Research from UT MD Anderson Cancer Center.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Data-Driven Exploration of Climate Attractor Manifolds For Long-Term Predictability

Focal Area: This white paper responds to Focal Area 3. We seek to gain insight into decadal-scale climate predictability by applying novel manifold-finding probabilistic AI techniques to the complex data produced by Earth System models (ESMs) such as E3SM. The associated portfolio of research activities leverages DOE’s asset mix of HPC platforms, climate expertise, climate simulation codes, and AI expertise. Science Challenge: Climate and climate models are dynamical systems exhibiting properties that are interpretable through chaos theory. The theory contains an important concept that is relevant to multi-decade-scale climate prediction: a chaotic attractor. While the space containing all the possible states of the Earth’s atmosphere and ocean, the possible weather, is large, the realized states tend to stay near the smaller-dimensioned attractor. This behavior is responsible for the “order behind the irregularity” [1] of climate phenomena. Climate change can be thought of as a change in the properties of the attractor, and predicting the climate over years to decades is equivalent to predicting how those properties will change. To date, the attractor has been a useful conceptual tool, but has not been amenable to direct characterization. A new development is the advent of efficient high-dimensional manifold-finding probabilistic AI techniques, which permit a data-driven characterization of the ESM attractor and its probability distribution over weather states. Such a characterization would result in a natural dimensional reduction — a “non-linear Principal Components Analysis (PCA) adapted to climate simulation data” — leading to important advances in scenario-based long-term climate prediction, long-term prediction of water cycle extremes, ESM verification, inter-model comparison, and process model development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Understanding Oxygen Reduction on Tantalum Oxyphosphate and Tantalum Oxide Supported Platinum by X-ray Absorption Spectroscopy

The Pt activity for the oxygen reduction reaction (ORR) is improved when Pt nanoparticles are supported on nanoscale layers of tantalum oxyphosphate (nominally, TaOPO{sub 4}) on Vulcan carbon (VC) and heated at high temperature (660 C) in reducing conditions. We attempt to explain the increased activity of the Pt-TaOPO{sub 4}/VC 'HT' by comparison to other less-active electrocatalysts comprising Pt on tantalum oxide (Pt-Ta{sub 2}O{sub 5}/VC) and Pt-TaOPO{sub 4}/VC heated to 200 C in air. Our toolbox for this analysis contains the rotating disk electrode methodology for characterization of the ORR and high-angle annular dark-field scanning transmission electron microscopy with high-resolution energy-dispersive X-ray spectroscopy (EDS) capabilities. The adsorption of molecular species on the Pt and Ta is determined from the {Delta}{mu} XANES (X-ray absorption near-edge structure) adsorbate isolation technique of X-ray absorption spectroscopy (XAS) data for electrocatalsyts in situ, whereby H and OH adsorption products from water activation can be used to infer how an electrocatalyst would react for oxygen reduction. The {Delta}{mu} XANES analysis at the Pt L{sub 2} edge suggests that interfacial hydrogen exists between the Pt and the support for Pt-Ta{sub 2}O{sub 5}/VC and Pt-TaOPO{sub 4}/VC 'HT' at potentials > 0.3 V vs RHE even after surface H is removed. More importantly, in the most active sample, Pt-TaOPO{sub 4}/VC 'HT', the onset potential for O(H) adsorption is highest in the {Delta}{mu} XANES. The XAS and microscopy/EDS results show that the Pt-TaOPO{sub 4}/VC 'HT' is Pt-Ta{sub 2}O{sub 5}/VC with polyphosphate groups directly associated with the Pt. From the body of results, we surmise that the nanoscale layer of polyphosphate on the Pt-TaOPO{sub 4}/VC 'HT' facilitates proton conduction to the Pt particles and hence moves the equilibrium for OH adsorption on the Pt to higher potentials.

08 HYDROGEN↗
DOE OSTI2013

THE HERSCHEL AND JCMT GOULD BELT SURVEYS: CONSTRAINING DUST PROPERTIES IN THE PERSEUS B1 CLUMP WITH PACS, SPIRE, AND SCUBA-2

We present Herschel observations from the Herschel Gould Belt Survey and SCUBA-2 science verification observations from the JCMT Gould Belt Survey of the B1 clump in the Perseus molecular cloud. We determined the dust emissivity index using four different techniques to combine the Herschel PACS+SPIRE data at 160-500 {mu}m with the SCUBA-2 data at 450 {mu}m and 850 {mu}m. Of our four techniques, we found that the most robust method was filtering out the large-scale emission in the Herschel bands to match the spatial scales recovered by the SCUBA-2 reduction pipeline. Using this method, we find {beta} Almost-Equal-To 2 toward the filament region and moderately dense material and lower {beta} values ({beta} {approx}> 1.6) toward the dense protostellar cores, possibly due to dust grain growth. We find that {beta} and temperature are more robust with the inclusion of the SCUBA-2 data, improving estimates from Herschel data alone by factors of {approx}2 for {beta} and by {approx}40% for temperature. Furthermore, we find core mass differences of {approx}< 30% compared to Herschel-only estimates with an adopted {beta} = 2, highlighting the necessity of long-wavelength submillimeter data for deriving accurate masses of prestellar and protostellar cores.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

TU-G-BRA-01: Assessing Radiation-Induced Reductions in Regional Lung Perfusion Following Stereotactic Radiotherapy for Lung Cancer

Purpose: The dose-dependent nature of radiation therapy (RT)-induced lung injury following hypo-fractionated stereotactic RT is unclear. We herein report preliminary results of a prospective study assessing the magnitude of RT-induced reductions in regional lung perfusion following hypo-fractionated stereotactic RT. Methods: Four patients undergoing hypo-fractionated stereotactic lung RT (SBRT: 12 Gy x 4 fractions or 10 Gy x 5 fractions) had a pre-treatment SPECT (single-photon emission computed tomography) perfusion scan providing a 3D map of regional lung perfusion. Scans were repeated 3–6 months post-treatment. Pre- and post SPECT scans were registered to the planning CT scan (and hence the 3D dose data). Changes in regional perfusion (counts per cc on the pre-post scans) were computed in regions of the lung exposed to different doses of radiation (in 5 Gy intervals), thus defining a dose-response function. SPECT scans were internally normalized to the regions receiving <5 Gy. Results: At 3 months post-RT, the changes in perfusion are highly variable. At 6 months, there is a consistent dose-dependent reduction in regional perfusion. The average percent decline in regional perfusion was 10% at 15–20 Gy, 20% at 20–25 Gy, and 30% at 25–30 Gy representing a relatively linear dose response with an approximate 2% reduction per Gray for doses in excess of 10 Gy. There was a subtle increase in perfusion in the lung receiving <10 Gy. Conclusion: Hypo-fractionated stereotactic RT appears to cause a dose-dependent reduction in regional lung perfusion. There appears to be a threshold effect with no apparent perfusion loss at doses <10 Gy, though this might be in part due to the normalization technique used. Additional data is needed from a larger number of patients to better assess this issue. This sort of data can be used to assist optimizing RT treatment plans that minimize the risk of lung injury. Partly supported by the NIH (CA69579) and the Lance Armstrong Foundation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Machine learning for analysis of atomic spectral data

Physics based forward models are the basis on which many experimental diagnostics are interpreted. For some diagnostics, models can be computationally expensive which precludes their use in real time analysis. Reduced models have the potential to capture sufficient physics thereby enabling the desired real time analysis. Using statistical inference and machine learning techniques the application of reduced models for inversion of atomic spectral data used to diagnose magnetic fields in a plasma will be examined. Two approaches are considered, (a) a reduction of the forward model where traditional inversion can be performed on the proxy model, and (b) a reduction of the direct inverse where parameters are a function of measured signal. The resulting inversion is sufficiently fast to be utilized in an online context for digital twinning, and ultimately real-time prediction, design, and control of plasma systems, such as tokamaks. Furthermore, these methods will be demonstrated on both simulated and experimentally measured data.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

A machine-learning-driven data labeling pipeline for scientific analysis in MLExchange

This study introduces a novel labeling pipeline to accelerate the labeling process of scientific data sets by using artificial intelligence (AI)-guided tagging techniques. This pipeline includes a set of interconnected web-based graphical user interfaces (GUIs), where Data Clinic and MLCoach enable the preparation of machine learning (ML) models for data reduction and classification, respectively, while Label Maker is used for label assignment. Throughout this pipeline, data can be accessed through a direct connection to a file system or through Tiled for access through Hypertext Transfer Protocol (HTTP). Our experimental results present three use cases where this labeling pipeline has been instrumental for the study of large X-ray scattering data sets in the area of pattern recognition, the remote analysis of resonant soft X-ray scattering data and the fine-tuning process of foundation models. These use cases highlight the labeling capabilities of this pipeline, including the ability to label large data sets in a short period of time, to perform remote data analysis while minimizing data movement and to enhance the fine-tuning process of complex ML models with human involvement.

Chavez, Tanny (ORCID:0000000193172896)↗
DOE OSTI2025

Robust error calibration for serial crystallography

Serial crystallography is an important technique with unique abilities to resolve enzymatic transition states, minimize radiation damage to sensitive metalloenzymes and perform de novo structure determination from micrometre-sized crystals. This technique requires the merging of data from thousands of crystals, making manual identification of errant crystals unfeasible. cctbx.xfel.merge uses filtering to remove problematic data. However, this process is imperfect, and data reduction must be robust to outliers. We add robustness to cctbx.xfel.merge at the step of uncertainty determination for reflection intensities. This step is a critical point for robustness because it is the first step where the data sets are considered as a whole, as opposed to individual lattices. Robustness is conferred by reformulating the error-calibration procedure to have fewer and less stringent statistical assumptions and incorporating the ability to down-weight low-quality lattices. We then apply this method to five macromolecular XFEL data sets and observe the improvements to each. The appropriateness of the intensity uncertainties is demonstrated through internal consistency. This is performed through theoretical CC 1/2 and I /σ relationships and by weighted second moments, which use Wilson's prior to connect intensity uncertainties with their expected distribution. This work presents new mathematical tools to analyze intensity statistics and demonstrates their effectiveness through the often underappreciated process of uncertainty analysis.

Mittan-Moreau, David W.↗
DOE OSTI2016

Subsampling-based compression and flow visualization

As computational capabilities increasingly outpace disk speeds on leading supercomputers, scientists will, in turn, be increasingly unable to save their simulation data at its native resolution. One solution to this problem is to compress these data sets as they are generated and visualize the compressed results afterwards. We explore this approach, specifically subsampling velocity data and the resulting errors for particle advection-based flow visualization. We compare three techniques: random selection of subsamples, selection at regular locations corresponding to multi-resolution reduction, and introduce a novel technique for informed selection of subsamples. Furthermore, we explore an adaptive system which exchanges the subsampling budget over parallel tasks, to ensure that subsampling occurs at the highest rate in the areas that need it most. We perform supercomputing runs to measure the effectiveness of the selection and adaptation techniques. Overall, we find that adaptation is very effective, and, among selection techniques, our informed selection provides the most accurate results, followed by the multi-resolution selection, and with the worst accuracy coming from random subsamples.

Agranovsky, Alexy↗
DOE OSTI2016

AN IMPROVED DISTANCE AND MASS ESTIMATE FOR SGR A* FROM A MULTISTAR ORBIT ANALYSIS

We present new, more precise measurements of the mass and distance of our Galaxy’s central supermassive black hole, Sgr A*. These results stem from a new analysis that more than doubles the time baseline for astrometry of faint stars orbiting Sgr A*, combining 2 decades of speckle imaging and adaptive optics data. Specifically, we improve our analysis of the speckle images by using information about a star’s orbit from the deep adaptive optics data (2005–2013) to inform the search for the star in the speckle years (1995–2005). When this new analysis technique is combined with the first complete re-reduction of Keck Galactic Center speckle images using speckle holography, we are able to track the short-period star S0-38 ( K -band magnitude = 17, orbital period = 19 yr) through the speckle years. We use the kinematic measurements from speckle holography and adaptive optics to estimate the orbits of S0-38 and S0-2 and thereby improve our constraints of the mass ( M {sub bh}) and distance ( R {sub o} ) of Sgr A*: M {sub bh} = (4.02 ± 0.16 ± 0.04) × 10{sup 6} M {sub ⊙} and 7.86 ± 0.14 ± 0.04 kpc. The uncertainties in M {sub bh} and R {sub o} as determined by the combined orbital fit of S0-2 and S0-38 are improved by a factor of 2 and 2.5, respectively, compared to an orbital fit of S0-2 alone and a factor of ∼2.5 compared to previous results from stellar orbits. This analysis also limits the extended dark mass within 0.01 pc to less than 0.13 × 10{sup 6} M {sub ⊙} at 99.7% confidence, a factor of 3 lower compared to prior work.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Analysis techniques for the evaluation of the neutrinoless double- β decay lifetime in Te 130 with the CUORE-0 detector

We describe in detail the methods used to obtain the lower bound on the lifetime of neutrinoless double-beta ( 0 ν β β ) decay in 130 Te and the associated limit on the effective Majorana mass of the neutrino using the CUORE-0 detector. CUORE-0 is a bolometric detector array located at the Laboratori Nazionali del Gran Sasso that was designed to validate the background reduction techniques developed for CUORE, a next-generation experiment scheduled to come online in 2016. CUORE-0 is also a competitive 0 ν β β decay search in its own right and functions as a platform to further develop the analysis tools and procedures to be used in CUORE. These include data collection, event selection and processing, as well as an evaluation of signal efficiency. In particular, we describe the amplitude evaluation, thermal gain stabilization, energy calibration methods, and the analysis event selection used to create our final 0 ν β β search spectrum. We define our high level analysis procedures, with emphasis on the new insights gained and challenges encountered. We outline in detail our fitting methods near the hypothesized 0 ν β β decay peak and catalog the main sources of systematic uncertainty. Finally, we derive the 0 ν β β decay half-life limits previously reported for CUORE-0, T 0 ν 1 / 2 > 2.7 × 10 24 yr , and in combination with the Cuoricino limit, T 0 ν 1 / 2 > 4.0 × 10 24 yr .

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Moltensaltpropnet

MoltenSaltPropnet is a physics-informed machine learning framework that aims to predict the thermophysical properties of molten fluoride and chloride salt mixtures, which are crucial for the design and safety of Generation IV molten salt reactors. The code processes data from the Molten-Salt Thermal Properties Database (MSTDB-TP) and the Janz compendium, converting critically evaluated correlations into fast, differentiable surrogate models for density, viscosity, thermal conductivity, and heat capacity across 448 distinct salt systems. The implementation consists of several key components: 1. Data Curation: The code parses and cleans the raw data, normalizing elemental mole fractions and extracting relevant regression coefficients for various thermophysical properties. 2. Feature Engineering: It generates fixed-length numerical descriptors that encapsulate the composition and temperature, incorporating polynomial interaction terms and dimensionality-reduction techniques to optimize model performance. 3. Coefficient Learning: Four different machine learning architectures are employed: a deep residual network (ResNet), a Kolmogorov–Arnold network (KAN), a sparsity-inducing neural network (SNN), and classical regression models. Each model learns to predict coefficients that define the temperature-dependent correlations for the thermophysical properties. 4. Property Reconstruction: The predicted coefficients are used to compute temperature-dependent property values, ensuring positivity and monotonic trends through a composite loss function that enforces physical constraints. 5. User Interface: An open-source web application enables users to filter the database, train task-specific models, and visualize the results, allowing for rapid exploration of candidate salt mixtures. MoltenSaltPropnet bridges the gap between limited experimental data and high-fidelity reactor simulations, providing a powerful tool for researchers in the field of molten salt reactors and advanced nuclear energy systems.

Retamales, Mauricio Eduardo Tano [Idaho National L↗
DOE OSTI2025

Searching for Neutrino Tridents in the NOvA Near Detector

This dissertation presents a search for neutrino trident production in the NOvA near detector through the coherent ``dimuon" channel: $\nu_\mu +\hspace{1pt}\text{X} \rightarrow \nu_\mu + \mu^- + \mu^+ +\hspace{1pt}\text{X}$. Trident production is a rare, purely electroweak process with sensitivity to physics beyond the Standard Model. The theoretical background, motivation for studying the process, and previous experimental measurements are reviewed. The analysis uses data collected by the NOvA near detector (ND) from Fermilab's Neutrinos at the Main Injector (NuMI) beam between November 2014 and February 2024, corresponding to an exposure of $25.5\times 10^{20}$ protons on target. The ND is a segmented tracking calorimeter located 800~m from the beam target, receiving neutrinos with a mean energy of 2~GeV. A multi-pass background reduction strategy is implemented, including the development of a novel dimuon-specific tracking technique. Trident candidates are identified using a boost ed decision tree classifier trained on simulated signal and background events. Limited background Monte Carlo statistics necessitate the use of functional fits to sideband data, which are extrapolated to estimate backgrounds in the signal region. The unblinded data contain 9 trident-like events, with an estimated background of 5.66 $\pm$ 5.15 events. This yields a best fit estimate of 3.34 tridents compared to the Standard Model prediction of 4.66. A profiled Feldman-Cousins method is used to determine a 90\% confidence interval of [0,9.1] on the number of signal events, corresponding to an upper limit of 1.95$\times$ the Standard Model prediction. This result represents the lowest energy search for trident events to date, and the first experimental contribution to the process in 27 years.

Bowles, Reed Scott [Indiana U.]↗
DOE OSTI2017

Towards the Best Parallaxes and a Limit on the Giant Planet Occurrence Rate for Nearby T Brown Dwarfs

The coldest of stars, the hottest of planets; brown dwarf stars, despite considerable data, are an enigma. Unable to fuse atomic nuclei past Deuterium, brown dwarfs are cold in temperature, and are difficult to detect with all but the most sensitive of telescopes. Brown dwarf stars were first posited in the 1960s, but evidence for their existence continued to elude astronomers. In the late 1980s, the first physical evidence for these stars was discovered. Using the Shane telescope at UCO Lick, and the Gemini Observatory, data for nearby T Brown Dwarf star fields was taken over the course of many months. This project took that data and reduced the individual star fields using primarily at field techniques to remove artifacts and pixel to pixel variation. For each individual star in each reduced field, a 2-dimensional projection was taken, producing a light curve. From the light curve, a baseline for the quality of the reduction can be formed by examining the full-width half maximum (FWHM) of the curve. The Shane telescope produced light curves with large FWHM, a mark of poor Adaptive Optic (AO) correction. The data from the Gemini observatory yielded a low FWHM, a mark of good AO correction. These reduced fields will enable the determination of Astrophysical parallax, and with supplementary data, other stellar parameters such as Mass and Luminosity can be determined. The end goal of this project is to determine a limit on the rate of occurrence of Giant Planets around brown dwarf stars.

79 ASTRONOMY AND ASTROPHYSICS↗