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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Probing the cosmic distance duality relation using time delay lenses

The construction of the cosmic distance-duality relation (CDDR) has been widely studied. However, its consistency with various new observables remains a topic of interest. We present a new way to constrain the CDDR η( z ) using different dynamic and geometric properties of strong gravitational lenses (SGL) along with SNe Ia observations. We use a sample of 102 SGL with the measurement of corresponding velocity dispersion σ{sub 0} and Einstein radius θ {sub E} . In addition, we also use a dataset of 12 two image lensing systems containing the measure of time delay Δ t between source images. Jointly these two datasets give us the angular diameter distance D {sub A} {sub ol} of the lens. Further, for luminosity distance, we use the 740 observations from JLA compilation of SNe Ia. To study the combined behavior of these datasets we use a model independent method, Gaussian Process (GP). We also check the efficiency of GP by applying it on simulated datasets, which are generated in a phenomenological way by using realistic cosmological error bars. Finally, we conclude that the combined bounds from the SGL and SNe Ia observation do not favor any deviation of CDDR and are in concordance with the standard value (η=1) within 2σ confidence region, which further strengthens the theoretical acceptance of CDDR.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A FAIR and AI-ready Higgs boson decay dataset

Abstract To enable the reusability of massive scientific datasets by humans and machines, researchers aim to adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) models. This article provides a domain-agnostic, step-by-step assessment guide to evaluate whether or not a given dataset meets these principles. We demonstrate how to use this guide to evaluate the FAIRness of an open simulated dataset produced by the CMS Collaboration at the CERN Large Hadron Collider. This dataset consists of Higgs boson decays and quark and gluon background, and is available through the CERN Open Data Portal. We use additional available tools to assess the FAIRness of this dataset, and incorporate feedback from members of the FAIR community to validate our results. This article is accompanied by a Jupyter notebook to visualize and explore this dataset. This study marks the first in a planned series of articles that will guide scientists in the creation of FAIR AI models and datasets in high energy particle physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2012

Multidecadal Drought Cycles in the Great Basin Recorded by the Great Salt Lake: Modulation from a Transition-Phase Teleconnection

This study investigates the meteorological conditions associated with multidecadal drought cycles as revealed by lake level fluctuation of the Great Salt Lake (GSL). The analysis combined instrumental, proxy, and simulation datasets, including the Twentieth Century Reanalysis version 2, the North American Drought Atlas, and a 2000-yr control simulation of the GFDL Coupled Model, version 2.1 (CM2.1). Statistical evidence from the spectral coherence analysis points to a phase shift amounting to 6–9 yr between the wet–dry cycles in the Great Basin and the warm–cool phases of the interdecadal Pacific oscillation (IPO). Diagnoses of the sea surface temperature and atmospheric circulation anomalies attribute such a phase shift to a distinctive teleconnection wave train that develops during the transition points between the IPO’s warm and cool phases. This teleconnection wave train forms recurrent circulation anomalies centered over the southeastern Gulf of Alaska; this directs moisture flux across the Great Basin and subsequently drives wet–dry conditions over the Great Basin and the GSL watershed. The IPO life cycle therefore modulates local droughts–pluvials in a quarter-phase manner.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Using compressive sensing to recover images from PET scanners with partial detector rings

Purpose: Most positron emission tomography/computed tomography (PET/CT) scanners consist of tightly packed discrete detector rings to improve scanner efficiency. The authors’ aim was to use compressive sensing (CS) techniques in PET imaging to investigate the possibility of decreasing the number of detector elements per ring (introducing gaps) while maintaining image quality. Methods: A CS model based on a combination of gradient magnitude and wavelet domains (wavelet-TV) was developed to recover missing observations in PET data acquisition. The model was designed to minimize the total variation (TV) and L1-norm of wavelet coefficients while constrained by the partially observed data. The CS model also incorporated a Poisson noise term that modeled the observed noise while suppressing its contribution by penalizing the Poisson log likelihood function. Three experiments were performed to evaluate the proposed CS recovery algorithm: a simulation study, a phantom study, and six patient studies. The simulation dataset comprised six disks of various sizes in a uniform background with an activity concentration of 5:1. The simulated image was multiplied by the system matrix to obtain the corresponding sinogram and then Poisson noise was added. The resultant sinogram was masked to create the effect of partial detector removal and then the proposed CS algorithm was applied to recover the missing PET data. In addition, different levels of noise were simulated to assess the performance of the proposed algorithm. For the phantom study, an IEC phantom with six internal spheres each filled with F-18 at an activity-to-background ratio of 10:1 was used. The phantom was imaged twice on a RX PET/CT scanner: once with all detectors operational (baseline) and once with four detector blocks (11%) turned off at each of 0 °, 90 °, 180 °, and 270° (partially sampled). The partially acquired sinograms were then recovered using the proposed algorithm. For the third test, PET images from six patient studies were investigated using the same strategy of the phantom study. The recovered images using WTV and TV as well as the partially sampled images from all three experiments were then compared with the fully sampled images (the baseline). Comparisons were done by calculating the mean error (%bias), root mean square error (RMSE), contrast recovery (CR), and SNR of activity concentration in regions of interest drawn in the background as well as the disks, spheres, and lesions. Results: For the simulation study, the mean error, RMSE, and CR for the WTV (TV) recovered images were 0.26% (0.48%), 2.6% (2.9%), 97% (96%), respectively, when compared to baseline. For the partially sampled images, these results were 22.5%, 45.9%, and 64%, respectively. For the simulation study, the average SNR for the baseline was 41.7 while for WTV (TV), recovered image was 44.2 (44.0). The phantom study showed similar trends with 5.4% (18.2%), 15.6% (18.8%), and 78% (60%), respectively, for the WTV (TV) images and 33%, 34.3%, and 69% for the partially sampled images. For the phantom study, the average SNR for the baseline was 14.7 while for WTV (TV) recovered image was 13.7 (11.9). Finally, the average of these values for the six patient studies for the WTV-recovered, TV, and partially sampled images was 1%, 7.2%, 92% and 1.3%, 15.1%, 87%, and 27%, 25.8%, 45%, respectively. Conclusions: CS with WTV is capable of recovering PET images with good quantitative accuracy from partially sampled data. Such an approach can be used to potentially reduce the cost of scanners while maintaining good image quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Exploration of high-dimensional scalar function for nuclear reactor safety analysis and visualization

The next generation of methodologies for nuclear reactor Probabilistic Risk Assessment (PRA) explicitly accounts for the time element in modeling the probabilistic system evolution and uses numerical simulation tools to account for possible dependencies between failure events. The Monte-Carlo (MC) and the Dynamic Event Tree (DET) approaches belong to this new class of dynamic PRA methodologies. A challenge of dynamic PRA algorithms is the large amount of data they produce which may be difficult to visualize and analyze in order to extract useful information. We present a software tool that is designed to address these goals. We model a large-scale nuclear simulation dataset as a high-dimensional scalar function defined over a discrete sample of the domain. First, we provide structural analysis of such a function at multiple scales and provide insight into the relationship between the input parameters and the output. Second, we enable exploratory analysis for users, where we help the users to differentiate features from noise through multi-scale analysis on an interactive platform, based on domain knowledge and data characterization. Our analysis is performed by exploiting the topological and geometric properties of the domain, building statistical models based on its topological segmentations and providing interactive visual interfaces to facilitate such explorations. We provide a user's guide to our software tool by highlighting its analysis and visualization capabilities, along with a use case involving data from a nuclear reactor safety simulation. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Prospective study evaluating the use of IV contrast on IMRT treatment planning for lung cancer

Purpose: To investigate the impact of exclusively using intravenous (IV) contrast x-ray computed tomography (CT) scans on lung cancer intensity-modulated radiation therapy (IMRT) treatment planning. Methods: Eight patients with lung cancer (one small cell, seven nonsmall cell) scheduled to receive IMRT consented to acquisition of simulation CT scans with and without IV contrast. Clinical treatment plans optimized on the noncontrast scans were recomputed on contrast scans and dose coverage was compared, along with the γ passing rates. Results: IV contrast enhanced scans provided better target and critical structure conspicuity than the noncontrast scans. Using noncontrast scan as a reference, the median absolute/relative differences in mean, maximum, and minimum doses to the planning target volume (PTV) were −4.5 cGy/−0.09%, 41.1 cGy/0.62%, and −19.7 cGy/−0.50%, respectively. Regarding organs-at-risk (OARs), the median absolute/relative differences of maximum dose to heart was −13.3 cGy/−0.32%, to esophagus was −63.4 cGy/−0.89%, and to spinal cord was −16.3 cGy/−0.46%. The median heart region of interest CT Hounsfield Unit (HU) number difference between noncontrast and contrast scans was 136.4 HU (range, 94.2–161.8 HU). Subjectively, the regions with absolute dose differences greater than 3% of the prescription dose were small and typically located at the patient periphery and/or at the beam edges. The median γ passing rate was 0.9981 (range, 0.9654–0.9999) using 3% absolute dose difference/3 mm distance-to-agreement criteria. Overall, all evaluated cases were found to be clinically equivalent. Conclusions: PTV and OARs dose differences between noncontrast and contrast scans appear to be minimal for lung cancer patients undergoing IMRT. Using IV contrast scans as the primary simulation dataset could increase treatment planning efficiency and accuracy by avoiding unnecessary scans, manually region overriding, and planning errors caused by nonperfect image registrations.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Experimental design applications for modeling and assessing carbon dioxide sequestration in saline aquifers

This project was a computer modeling effort to couple reservoir simulation and ED/RSM using Sensitivity Analysis, Uncertainty Analysis, and Optimization Methods, to assess geologic, geochemical, geomechanical, and rock-fluid effects and factors on CO 2 injectivity, capacity, and plume migration. The project objective was to develop proxy models to simplify the highly complex coupled geochemical and geomechanical models in the utilization and storage of CO 2 in the subsurface. The goals were to investigate and prove the feasibility of the ED/RSM processes and engineering development, and bridge the gaps regarding the uncertainty and unknowns of the many geochemical and geomechanical interacting parameters in the development and operation of anthropogenic CO 2 sequestration and storage sites. The bottleneck in this workflow is the high computational effort of reactive transport simulation models and large number of input variables to optimize with ED/RSM techniques. The project was not to develop the reactive transport, geomechanical, or ED/RSM software, but was to use what was commercially and/or publically available as a proof of concept to generate proxy or surrogate models. A detailed geologic and petrographic mineral assemblage and geologic structure of the doubly plunging anticline was defined using the USDOE RMOTC formations of interest data (e.g., Lower Sundance, Crow Mountain, Alcova Limestone, and Red Peak). The assemblage of 23 minerals was primarily developed from literature data and petrophysical (well log) analysis. The assemblage and structure was input into a commercial reactive transport simulator to predict the effects of CO 2 injection and complex reactions with the reservoir rock. Significant impediments were encountered during the execution phase of the project. The only known commercial reactive transport simulator was incapable of simulating complex geochemistry modeled in this project. Significant effort and project funding was expended to determine the limitations of both the commercial simulator and the Lawrence Berkeley National Laboratory (LBNL) R&D simulator, TOUGHREACT available to the project. A simplified layer cake model approximating the volume of the RMOTC targeted reservoirs was defined with 1-3 minerals eventually modeled with limited success. Modeling reactive transport in porous media requires significant computational power. In this project, up to 24 processors were used to model a limited mineral set of 1-3 minerals. In addition, geomechanical aspects of injecting CO 2 into closed, semi-open, and open systems in various well completion methods was simulated. Enhanced Oil Recovery (EOR) as a storage method was not modeled. A robust and stable simulation dataset or base case was developed and used to create a master dataset with embedded instructions for input to the ED/RSM software. Little success was achieved toward the objective of the project using the commercial simulator or the LBNL simulator versions available during the time of this project. Several hundred realizations were run with the commercial simulator and ED/RSM software, most having convergence problems and terminating prematurely. A proxy model for full field CO 2 injection sequestration utilization and storage was not capable of being developed with software available for this project. Though the chemistry is reasonably known and understood, based on the amount of effort and huge computational time required, predicting CO 2 sequestration storage capacity in geologic formations to within the program goals of ±30% proved unsuccessful.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Utilizing novel diversity estimators to quantify multiple dimensions of microbial biodiversity across domains

Background: Microbial ecologists often employ methods from classical community ecology to analyze microbial community diversity. However, these methods have limitations because microbial communities differ from macro-organismal communities in key ways. This study sought to quantify microbial diversity using methods that are better suited for data spanning multiple domains of life and dimensions of diversity. Diversity profiles are one novel, promising way to analyze microbial datasets. Diversity profiles encompass many other indices, provide effective numbers of diversity (mathematical generalizations of previous indices that better convey the magnitude of differences in diversity), and can incorporate taxa similarity information. To explore whether these profiles change interpretations of microbial datasets, diversity profiles were calculated for four microbial datasets from different environments spanning all domains of life as well as viruses. Both similarity-based profiles that incorporated phylogenetic relatedness and naïve (not similarity-based) profiles were calculated. Simulated datasets were used to examine the robustness of diversity profiles to varying phylogenetic topology and community composition. Results: Diversity profiles provided insights into microbial datasets that were not detectable with classical univariate diversity metrics. For all datasets analyzed, there were key distinctions between calculations that incorporated phylogenetic diversity as a measure of taxa similarity and naïve calculations. The profiles also provided information about the effects of rare species on diversity calculations. Additionally, diversity profiles were used to examine thousands of simulated microbial communities, showing that similarity-based and naïve diversity profiles only agreed approximately 50% of the time in their classification of which sample was most diverse. This is a strong argument for incorporating similarity information and calculating diversity with a range of emphases on rare and abundant species when quantifying microbial community diversity. Conclusions: For many datasets, diversity profiles provided a different view of microbial community diversity compared to analyses that did not take into account taxa similarity information, effective diversity, or multiple diversity metrics. These findings are a valuable contribution to data analysis methodology in microbial ecology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Characterizing the acceleration time of laser-driven ion acceleration with data-informed neural networks

Peak ion energy is an important figure-of-merit in short-pulse, laser-driven ion acceleration and is dependent on an associated acceleration time. Standard metrics for these quantities depend on analytical results such as the self-similar fluid model or empirical models based on relatively small experimental and simulation datasets. In this work we attempt to use a data-informed neural network (NN) as a surrogate model for a large ensemble of PIC simulations to investigate an effective acceleration time. We explore the application of a stacked convolutional and recurrent NN architecture for improved regression by incorporating the time dependencies of the data into the training process. Of particular note is how pretraining a network on lower fidelity data, e.g. 1D analytical results, greatly improves the network's ability to learn more complex, higher fidelity data. Finally, the dependency of the acceleration time on various laser and plasma parameters is explored.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Assessment of metal artifact reduction methods in pelvic CT

Purpose: Metal artifact reduction (MAR) produces images with improved quality potentially leading to confident and reliable clinical diagnosis and therapy planning. In this work, the authors evaluate the performance of five MAR techniques for the assessment of computed tomography images of patients with hip prostheses. Methods: Five MAR algorithms were evaluated using simulation and clinical studies. The algorithms included one-dimensional linear interpolation (LI) of the corrupted projection bins in the sinogram, two-dimensional interpolation (2D), a normalized metal artifact reduction (NMAR) technique, a metal deletion technique, and a maximum a posteriori completion (MAPC) approach. The algorithms were applied to ten simulated datasets as well as 30 clinical studies of patients with metallic hip implants. Qualitative evaluations were performed by two blinded experienced radiologists who ranked overall artifact severity and pelvic organ recognition for each algorithm by assigning scores from zero to five (zero indicating totally obscured organs with no structures identifiable and five indicating recognition with high confidence). Results: Simulation studies revealed that 2D, NMAR, and MAPC techniques performed almost equally well in all regions. LI falls behind the other approaches in terms of reducing dark streaking artifacts as well as preserving unaffected regions (p < 0.05). Visual assessment of clinical datasets revealed the superiority of NMAR and MAPC in the evaluated pelvic organs and in terms of overall image quality. Conclusions: Overall, all methods, except LI, performed equally well in artifact-free regions. Considering both clinical and simulation studies, 2D, NMAR, and MAPC seem to outperform the other techniques.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-CD-201-07: Experimentally Investigating Proton Energy Deposition On the Microscopic Scale Using Fluorescence Nuclear Track Detectors

Purpose: In order to further understand the interplay between proton physics and radiobiology it is necessary to consider proton energy deposition on the microscopic scale. In this work we used Fluorescent Nuclear Track Detectors (FNTDs) to experimentally investigate proton energy deposition, track-by-track. Methods: We irradiated 8×4×0.5mm{sup 3} FNTD chips (Landauer Inc) at seven water depths along a pristine proton Bragg peak with range=12cm. After irradiation, the FNTDs were scanned using a confocal microscope (FV1200, Olympus) with a high-power red laser and an oil-immersion objective lens (UPLSAPO60XO, NA=1.35). 10 slice image stacks were acquired with a slice-thickness of 2µm at multiple positions across each FNTD. Image-based analyses of track radius and track “mass” (integrated signal intensity) were performed using trackpy. For comparison, Monte Carlo simulated data were obtained using TOPAS and TOPAS-nBio. Results: Excellent correlation was observed between median track mass and TOPAS dose-averaged linear energy transfer. The resolution of the imaging system was determined insufficient to detect a relationship between track radius and exposure depth. Histograms of track mass (i) displayed strong repeatability across positions within an FNTD and (ii) varied in peak position and shape as a function of depth. TOPAS-nBio simulations implemented on the nanometer scale using physics lists from GEANT4-DNA yielded energy deposition distributions for individual protons and electrons scored within a virtual FNTD. Good agreement was found between these simulated datasets and the FNTD track mass distributions. Conclusion: Robust experimental measurements of the integral energy deposited by individual proton tracks can be performed using FNTDs. Monte Carlo simulations offer an exceedingly powerful approach to the quantification of proton energy deposition on the microscopic scale, but whilst they have been well validated at the macroscopic level, their microscopic validation is far from complete. Our results demonstrate that FNTD-based study can play an important role in addressing this deficit. Tracy Underwood gratefully acknowledges the support of the European Commission under an FP7 Marie Curie International Outgoing Fellowship for Career Development (#630064).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

High Resolution PV Power Modeling for Distribution Circuit Analysis

NREL has contracted with Clean Power Research to provide 1-minute simulation datasets of PV systems located at three high penetration distribution feeders in the service territory of Southern California Edison (SCE): Porterville, Palmdale, and Fontana, California. The resulting PV simulations will be used to separately model the electrical circuits to determine the impacts of PV on circuit operations.

14 SOLAR ENERGY↗
DOE OSTI2023

Unlocking Autonomous Telescopes through Reinforcement Learning: An Offline Framework and Insights from a Case Study

Optimizing observational astronomy campaigns is becoming a complex and expensive task for next-generation telescopes, where manual planning of observations may tend to reach suboptimal results in terms of optimization.Reinforcement Learning (RL) has been well-demonstrated as a valuable approach for training autonomous systems, and it may provide the basis for self-driving telescopes capable of scanning the sky and optimizing the scheduling for astronomy campaigns.We have developed a framework for the optimization of telescope scheduling using RL techniques, based on a dataset containing data on a discrete set of sky locations that the telescope should visit, and a reward metric. We compared several RL algorithms applied to an offline simulation dataset based at the Stone Edge Observatory, considering a discrete set of sky locations to visit and using “t-effective” as a reward metric, a measure of the quality of the data.Deep Q-Networks (DQNs), belonging to the class of value-based methods, have shown remarkable success in the optimization of astronomical observations in our dataset. In the full environment, the average reward value in each state was found to be 92%±5% of the maximum possible reward, while on the test set it resulted in 87%±9% of the maximum possible reward.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Characterizing magnetized plasmas with dynamic mode decomposition

Accurate and efficient plasma models are essential to understand and control experimental devices. Existing magnetohydrodynamic or kinetic models are nonlinear and computationally intensive and can be difficult to interpret, while often only approximating the true dynamics. In this work, data-driven techniques recently developed in the field of fluid dynamics are leveraged to develop interpretable reduced-order models of plasmas that strike a balance between accuracy and efficiency. In particular, dynamic mode decomposition (DMD) is used to extract spatio-temporal magnetic coherent structures from the experimental and simulation datasets of the helicity injected torus with steady inductive (HIT-SI) experiment. Three-dimensional magnetic surface probes from the HIT-SI experiment are analyzed, along with companion simulations with synthetic internal magnetic probes. A number of leading variants of the DMD algorithm are compared, including the sparsity-promoting and optimized DMD. Optimized DMD results in the highest overall prediction accuracy, while sparsity-promoting DMD yields physically interpretable models that avoid overfitting. These DMD algorithms uncover several coherent magnetic modes that provide new physical insights into the inner plasma structure. These modes were subsequently used to discover a previously unobserved three-dimensional structure in the simulation, rotating at the second injector harmonic. Finally, using data from probes at experimentally accessible locations, DMD identifies a resistive kink mode, a ubiquitous instability seen in magnetized plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

GADRAS + Geant4 Simulated Gamma Spectra Dataset

This dataset contains GADRAS and Geant4 simulated gamma-ray energy spectra used in the papers "Sim-to-real supervised domain adaptation for radioisotope identification" (https://doi.org/10.1016/j.nima.2025.171159) and "Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy" (https://arxiv.org/abs/2603.05719). Each spectrum contains a mixture of several different radioisotopes, and the goal of the analysis is to leverage domain adaptation techniques to use a model trained on source-domain spectra (GADRAS) to classify target-domain spectra (Geant4).

Lalor, Peter William [Pacific Northwest National L↗
DOE OSTI2016

A Bloom Filter Based Scalable Data Integrity Check Tool for Large-Scale Dataset

Large scale HPC applications are becoming increasingly data intensive. At Oak Ridge Leadership Computing Facility (OLCF), we are observing the number of files curated under individual project are reaching as high as 200 millions and project data size is exceeding petabytes. These simulation datasets, once validated, often needs to be transferred to archival system for long term storage or shared with the rest of the research community. Ensuring the data integrity of the full dataset at this scale is paramount important but also a daunting task. This is especially true considering that most conventional tools are serial and file-based, unwieldy to use and/or can't scale to meet user's demand.To tackle this particular challenge, this paper presents the design, implementation and evaluation of a scalable parallel checksumming tool, fsum, which we developed at OLCF. It is built upon the principle of parallel tree walk and work-stealing pattern to maximize parallelism and is capable of generating a single, consistent signature for the entire dataset at extreme scale. We also applied a novel bloom-filter based technique in aggregating signatures to overcome the signature ordering requirement. Given the probabilistic nature of bloom filter, we provided a detailed error and trade-off analysis. Using multiple datasets from production environment, we demonstrated that our tool can efficiently handle both very large files as well as many small-file based datasets. Our preliminary test showed that on the same hardware, it outperforms conventional tool by as much as 4×. It also exhibited near-linear scaling properties when provisioned with more compute resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

MarmotViz

MarmotViz is an illustrative visualization plug-in for ParaView. It enables users to identify and track time-varying features in simulation datasets. It permits the application of illustrative visualization effects to these features including: selective visualization, feature coloring, boundary smoothing, haloing, silhouette outlining, speedlines, and strobe silhouettes. These techniques serve to assist in exploration and interpretation of simulation data and can be used to generate enhanced renderings for presentation. The MarmotViz plug-in is developed as a flexible framework, and can be easily extended to incorporate new region-of-interest (ROI) identification algorithms, feature matching and tracking algorithms, and illustrative visualization effects.

Rattner, Alexander↗
DOE OSTI2025

Contrastive learning for robust representations of neutrino data

In neutrino physics, analyses often depend on large simulated datasets, making it essential for models to generalize effectively to real-world detector data. Contrastive learning, a well-established technique in deep learning, offers a promising solution to this challenge. By applying controlled data augmentations to simulated data, contrastive learning enables the extraction of robust and transferable features. This improves the ability of models trained on simulations to adapt to real experimental data distributions. In this paper, we investigate the application of contrastive learning methods in the context of neutrino physics. Through a combination of empirical evaluations and theoretical insights, we demonstrate how contrastive learning enhances model performance and adaptability. Additionally, we compare it to other domain adaptation techniques, highlighting the unique advantages of contrastive learning for this field. Published by the American Physical Society 2025

Wilkinson, Alex (ORCID:0000000253404506)↗