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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 OSTI2022

Overview of the Instrumentation for the Dark Energy Spectroscopic Instrument

Abstract The Dark Energy Spectroscopic Instrument (DESI) embarked on an ambitious 5 yr survey in 2021 May to explore the nature of dark energy with spectroscopic measurements of 40 million galaxies and quasars. DESI will determine precise redshifts and employ the baryon acoustic oscillation method to measure distances from the nearby universe to beyond redshift z > 3.5, and employ redshift space distortions to measure the growth of structure and probe potential modifications to general relativity. We describe the significant instrumentation we developed to conduct the DESI survey. This includes: a wide-field, 3.°2 diameter prime-focus corrector; a focal plane system with 5020 fiber positioners on the 0.812 m diameter, aspheric focal surface; 10 continuous, high-efficiency fiber cable bundles that connect the focal plane to the spectrographs; and 10 identical spectrographs. Each spectrograph employs a pair of dichroics to split the light into three channels that together record the light from 360–980 nm with a spectral resolution that ranges from 2000–5000. We describe the science requirements, their connection to the technical requirements, the management of the project, and interfaces between subsystems. DESI was installed at the 4 m Mayall Telescope at Kitt Peak National Observatory and has achieved all of its performance goals. Some performance highlights include an rms positioner accuracy of better than 0.″1 and a median signal-to-noise ratio of 7 of the [O ii ] doublet at 8 × 10 −17 erg s −1 cm −2 in 1000 s for galaxies at z = 1.4–1.6. We conclude with additional highlights from the on-sky validation and commissioning, key successes, and lessons learned.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Thermodynamics-guided machine learning model for predicting convective boundary layer height and its multi-site applicability

Accurate estimation of convective boundary layer height (CBLH) is vital for weather, climate, and air quality modeling. Machine learning (ML) shows promise in CBLH prediction, but input parameter selection often lacks physical grounding, limiting generalizability. This study introduces a novel ML framework for CBLH prediction, integrating thermodynamic constraints and the diurnal CBLH cycle as an implicit physical guide. Boundary layer growth is modeled as driven by surface heat fluxes and atmospheric heat absorption represented with the low tropospheric stability, using the diurnal cycle as input and output. TPOT and AutoKeras are employed to select optimal models, validated against Doppler lidar-derived CBLH data, achieving an R 2 of 0.84 across untrained years. Comparisons of eddy covariance (ECOR) and energy balance Bowen ratio (EBBR) flux measurements show the same prediction capability. Models trained on the ARM SGP C1 site with ECOR data and tested at E37 and E39 yield R 2 values of 0.79 and 0.81, respectively, demonstrating their adaptability. The ML model trained with all sites' data slightly enhances the performance compared with ML models trained over single-site data. The interquartile range for predicted CBLH is consistently narrower than that for DL-derived CBLH, reflecting lower variability in predicted CBLH compared to DL-derived CBLH, which is influenced by additional factors, which are not well represented with the model inputs. The model's generalizability across multiple sites at the ARM SGP site demonstrates its potential for transfer to greater distances, offering a scalable approach for enhancing boundary layer parameterization in atmospheric models.

Chu, Yufei [Stony Brook Univ., NY (United States)]↗
DOE OSTI2023

EI_MS_ML

The unambiguous identification of compounds from their electron ionization mass (EI-MS) spectra remains a significant unsolved problem in the field of metabolomics and analytical chemistry as a whole. Typically EI-MS spectra are compared using various mathematical operations that convert the spectral similarity or differences into a distance-like metric that roughly approximates the similarity of any two spectra. A commonly used metric for this is the cosine similarity metric which has values close to one for very similar spectra and a value of zero for very dissimilar spectra; however, no metric is perfect. Due to the prevalence of structurally-similar compounds such as isomers and the prevalence of certain fragmentation patterns across structurally-dissimilar compounds, the unambiguous assignment of EI-MS spectra compounds remains difficult. Frequently, querying an observed EI-MS spectrum against a large database such as the NIST17 library yields multiple possible assignments requiring the end user to distinguish between multiple high scoring hits, or multiple low scoring hits while keeping in mind that the correct hit may not be in the database at all. Although techniques such as orthogonal information from techniques such as chromatography can greatly aid in unambiguous assignment, this also requires more complicated experimental designs and access to more complicated analytical instrumentation. Substructures can be trivially detected and represented as strings using a previously published technique called node coloring from a known chemical structure. However, for experimentally-derived EI-MS spectra this information must be derived from the spectra itself (i.e., because we do not know what compound it represents). To achieve this, the software uses techniques from the field of machine learning and a large training dataset of EI-MS spectra corresponding to known structures annotated with substructure strings, to build models that can predict the presence of a given chemical substructure from an EI-MS spectrum directly.If these predictions are of high-quality (i.e., are unlikely to be false positives), the presence of one or more predicted substructures can be used to constrain the number of possible hits for a query spectrum. Mathematically, this restriction could be expressed in many forms, but the most straight-forward implementation is to weight the cosine similarity of a query spectrum and a plausible database match with a Tanimoto-like coefficient based on the ratio of the number of substructures predicted to the number of substructures present in the potential database hit. Determining which combination of models best reduces assignment ambiguity will be achieved using a combination of manual curation and optimization techniques such as genetic algorithms. This software will perform all the steps necessary to construct said models from a training dataset and evaluate them using a holdout dataset. Various statistical analyses can be performed to determine if this approach does decrease assignment ambiguity. For example, if this approach works, on average, the rank-order of the correct assignment for the holdout set of EI-MS spectra should decrease and the weighted cosine similarities for most of the possible matches in the database should be better than the unweighted cosine similarities. Furthermore, this same pipeline can be used on real experimental data to generate less ambiguous assignments.

Mitchell, Joshua↗
DOE OSTI2019

Deep ugrizY imaging and DEEP2/3 spectroscopy: a photometric redshift testbed for LSST and public release of data from the DEEP3 Galaxy Redshift Survey

ABSTRACT We present catalogues of calibrated photometry and spectroscopic redshifts in the Extended Groth Strip, intended for studies of photometric redshifts (photo-z’s). The data includes ugriz photometry from Canada–France–Hawaii Telescope Legacy Survey (CFHTLS) and Y-band photometry from the Subaru Suprime camera, as well as spectroscopic redshifts from the DEEP2, DEEP3, and 3D-HST surveys. These catalogues incorporate corrections to produce effectively matched-aperture photometry across all bands, based upon object size information available in the catalogue and Moffat profile point spread function fits. We test this catalogue with a simple machine learning-based photometric redshift algorithm based upon Random Forest regression, and find that the corrected aperture photometry leads to significant improvement in photo-z accuracy compared to the original SExtractor catalogues from CFHTLS and Subaru. The deep ugrizY photometry and spectroscopic redshifts are well suited for empirical tests of photometric redshift algorithms for LSST. The resulting catalogues are publicly available at http://d-scholarship.pitt.edu/36064/. We include a basic summary of the strategy of the DEEP3 Galaxy Redshift Survey to accompany the recent public release of DEEP3 data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Hierarchical Modeling and Statistical Calibration for Photometric Redshifts

The cosmological exploitation of modern photometric galaxy surveys requires both accurate (unbiased) and precise (narrow) redshift probability distributions derived from broadband photometry. Existing methodologies do not meet these requirements. Standard template fitting delivers interpretable models and errors, but lacks the flexibility to learn inaccuracies in the observed photometry or spectral templates. Machine learning addresses those issues, but requires representative training data, and the resulting models and uncertainties cannot be interpreted in the context of a physical model or outside of the training data. We present a hierarchical modeling approach simultaneously addressing the issues of flexibility, interpretability, and generalization. It combines template fitting with flexible (machine-learning-like) models to correct the spectral templates, model their redshift distributions, and recalibrate the photometric observations. By optimizing the full posterior distribution of the model and solving for its (thousands of) parameters, one can perform a global statistical calibration of the data and the spectral energy distribution (SED) model. We apply this approach to the public Dark Energy Survey Science Verification data and show that it provides more accurate and compact redshift posterior distributions than existing methods, as well as insights into residual photometric and SED systematics. Here, the model is causal and makes predictions for future data (e.g., additional photometric bandpasses), and its internal parameters and components are interpretable. This approach does not formally require the training data to be complete or representative; in principle, it can even work in regimes in which few or no spectroscopic redshifts are available.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Photometric redshift estimation with convolutional neural networks and galaxy images: Case study of resolving biases in data-driven methods

Deep-learning models have been increasingly exploited in astrophysical studies, but these data-driven algorithms are prone to producing biased outputs that are detrimental for subsequent analyses. In this work, we investigate two main forms of biases: class-dependent residuals, and mode collapse. We do this in a case study, in which we estimate photometric redshift as a classification problem using convolutional neural networks (CNNs) trained with galaxy images and associated spectroscopic redshifts. We focus on point estimates and propose a set of consecutive steps for resolving the two biases based on CNN models, involving representation learning with multichannel outputs, balancing the training data, and leveraging soft labels. The residuals can be viewed as a function of spectroscopic redshift or photometric redshift, and the biases with respect to these two definitions are incompatible and should be treated individually. We suggest that a prerequisite for resolving biases in photometric space is resolving biases in spectroscopic space. Experiments show that our methods can better control biases than benchmark methods, and they are robust in various implementing and training conditions with high-quality data. Our methods hold promises for future cosmological surveys that require a good constraint of biases, and they may be applied to regression problems and other studies that make use of data-driven models. Nonetheless, the bias-variance tradeoff and the requirement of sufficient statistics suggest that we need better methods and optimized data usage strategies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Data Science Shows that Entropy Correlates with Accelerated Zeolite Crystallization in Monte Carlo Simulations

We have performed a data science study of Monte Carlo simulation trajectories to understand factors that can accelerate formation of zeolite nanoporous crystals, a process that can take days or even weeks. In previous work, Monte Carlo simulations predicted and experiments confirmed that using a secondary organic structure-directing agent (OSDA) accelerates crystallization of all-silica LTA zeolite, with experiments finding a three-fold speedup [PCCP 24, 142-148 (2022)]. However, it remains unclear what physical factors cause the speed-up. Here, we apply data science to analyze the simulation trajectories to discover what drives accelerated zeolite crystallization in Monte Carlo going from a one-OSDA synthesis (1OSDA) to a two-OSDA version (2OSDA). We encoded simulation snapshots using the Smooth Overlap of Atomic Positions approach, which represents all 2- and 3-body correlations within a given cutoff distance. Principal component analyses failed to discriminate datasets of structures from 1OSDA and 2OSDA simulations, while the Support Vector Machine (SVM) approach succeeded at classifying such structures with an area-under-curve (AUC) score of 0.99 (where AUC = 1 is a perfect classification) with all 3-body correlations, and as high as 0.94 with only 2-body correlations. SVM decision functions reveal relatively broad / narrow histograms for 1OSDA / 2OSDA datasets, suggesting that the two simulations differ strongly in information heterogeneity. Informed by these results, we performed pair (2-body) entropy calculations during crystallization, resulting in entropy differences that semi-quantitatively account for the speedup observed in the previous Monte Carlo simulations. We conclude that altering synthesis conditions in ways that substantially changes the entropy of labile silica networks may accelerate zeolite crystallization, and we discuss possible approaches for achieving such acceleration.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics

Graph neural networks have been shown to achieve excellent performance for several crucial tasks in particle physics, such as charged particle tracking, jet tagging, and clustering. An important domain for the application of these networks is the FGPA-based first layer of real-time data filtering at the CERN Large Hadron Collider, which has strict latency and resource constraints. We discuss how to design distance-weighted graph networks that can be executed with a latency of less than one μs on an FPGA. To do so, we consider a representative task associated to particle reconstruction and identification in a next-generation calorimeter operating at a particle collider. We use a graph network architecture developed for such purposes, and apply additional simplifications to match the computing constraints of Level-1 trigger systems, including weight quantization. Using the hls4ml library, we convert the compressed models into firmware to be implemented on an FPGA. Performance of the synthesized models is presented both in terms of inference accuracy and resource usage.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Combined Machine Learning and Molecular Dynamics Reveal Two States of Hydration of a Single Functional Group of Cationic Polymeric Brushes

The state of hydration of a macromolecular system regulates a plethora of different properties of such a system. In this article, we develop a novel machine learning (ML) approach, based on the unsupervised clustering algorithm, for probing the hydration behavior of the {N(CH 3 ) 3 } + functional group of the PMETAC [Poly(2-(methacryloyloxy)ethyl trimethylammonium chloride] polyelectrolyte (PE) brush system. The PE brushes and the brush-supported water molecules and counterions (chloride ions) are first described using all-atom molecular dynamics (MD) simulations. The simulation data is subsequently used in our ML framework to identify that (1) the {N(CH 3 ) 3 } + functional groups of the PMETAC brushes have two distinct hydration states with one state (state 1) being characterized by less structured water molecules and the other state (state 2) being characterized by more structured water molecules and (2) an enhancement in the brush grafting density leads to the progressive dissapparenace of state 2. An increase in the grafting density increases the number of chloride counterions in a given volume around the {N(CH 3 ) 3 } + functional group and increases the number of shared water molecules between the {N(CH 3 ) 3 } + and Cl - . The chloride counterions are associated with a hydration layer with much less structured water molecules. Therefore, with an increase in the grafting density, an increase in the percentage of shared water molecules leads to the prevalence of the hydration state [of the {N(CH 3 ) 3 } + moiety] with less structured water molecules. Finally, we explain how the present findings are commensurate with two key previous related results, namely a significantly large chloride ion mobility inside the PMETAC brush layer and the {N(CH 3 ) 3 } + -Cl - average distance remaining independent of the PMETAC brush grafting density. Furthermore, we anticipate that the combined ML-MD-simulation approach proposed in this study can be adapted to probe other soft matter systems to reveal new insights of the underlying mechanisms of emergent phenomenon.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Alternate Retrieval A-105 Application Assessment Report

This report summarizes the historical development of the Confined Sluicing End Effector (CSEE) technology as well scarification technology. We also summarize the systems integration and deployment of that technology in the Gunite and Associated Tanks (GAAT) at the Oak Ridge Reservation where the retrieval system successfully removed the remaining sludge and heel in the eight tanks for tank closure and disposition. We also summarize the lessons learned from the deployment at Oak Ridge. Simulant development and testing have been key activities for any waste retrieval effort as many of the physical properties of the waste are unknown; therefore, this report discusses a bounding series of simulants that helps to reduce uncertainty for operations. These simulants have been developed for potential Hanford waste retrieval deployment. The potential key attributes for retrieval, based upon information provided by Washington River Protection Services (WRPS), of single-shell tank A-105 are discussed along with the potential application of CSEE technology to the challenges presented by the unique nature of A-105. These unique features include a major tear in the bottom of the tank, which necessitates technologies other than past practice sluicing to be deployed to prevent further intrusion of waste into the vadose zone. In addition, significant in-tank hardware makes A-105 particular challenging. However, there appear to be a number of parameters that should be investigated through additional testing or review of prior testing that may demonstrate the applicability of the CSEE technology to A-105. Testing of the Waste Retrieval End Effector (WREE), which was based on the original CSEE design, to demonstrate the ability to remove sludge and hardpan from Tank 241-C-106 will be reviewed to determine relevance to the Tank A-105 retrieval. There may be new requirements for the deployment system that accesses the tank with the CSEE because the tank bottom may not be flat, requiring the ability to maneuver the CSEE to maintain the appropriate stand-off distance from the CSEE and the tank bottom. Finally, we provide a set of recommendations, a set of task descriptions, and a range of estimated costs for a demonstration of the technology by the end of FY17. These tasks are summarized as: Complete system-level functional requirements; Procure or access the capacity to perform; Generate simulant development and simulant testing; Develop CSEE functionality; Develop demonstration plan; Perform demonstration; Analyze and document testing results; Perform project management and engineering.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Investigation and Recovery Following the Drum Event at INL's ARP-V Facility - 19517

Contractors working under the Department of Energy's (DoE's) Idaho Field Office have been exhuming and processing buried legacy waste from early weapons development and productions operations since 1984. Since operations began, nearly 400,000 drums have passed through this process. On April 11, 2018, repackaged drums were staged in an airlock (radiological buffer area) awaiting transfer to another facility for characterization. Late in the evening after operations had ended, four of the drums over pressurized causing an energetic displacement of their lids. A significant fraction of drum contents was ejected and dispersed throughout the airlock. The event activated the fire detection system in the facility initiating an emergency response from the site fire department, radiological protection, and operations. Initial intervention by fire department personnel was unsuccessful at quenching the reaction, so the responders egressed to a safe distance and monitored the situation. After determining the event had terminated and it was safe to enter, the facility was assessed revealing significant contamination in the airlock from the ejected waste. The area surrounding the facility was surveyed and it was determined that no contamination was released outside the facility. The Fluor Idaho, LLC (Fluor Idaho) Program Manager commissioned an investigation and recovery effort, which focused on three aspects: (1) technical investigation, (2) event and causal analysis, and (3) facility recovery. A team of highly qualified individuals from around the DOE Complex was assembled to support the technical investigation. The team included representatives from several Fluor Idaho, Fluor Corporate, Battelle Energy Alliance, LLC (INL), LANL, SRNL, South West Research Institute, DOE Carlsbad Field Office, and Defense Nuclear Facility Safety Board, and DOE-Headquarters. This team addressed the reaction mechanism of the event by guiding the sampling activities and coordinating analysis and testing by the analytical laboratories. Independent and experienced investigators were brought in to conduct a rigorous event and cause analysis. The Causal Analysis Team pursued direct, root, and contributing causes. Interviews were conducted, and documents were reviewed to develop an event and causal analysis. The Accelerated Retrieval Project V Operations team mobilized to decontaminate and recover the facility. Significant progress has been made in this effort, area and airborne contamination levels have been significantly reduced allowing limited activities to resume. In addition, this group supported sample collection efforts as directed by the Technical Investigation Team. This paper reviews key findings and milestones from the technical investigation, the event and causal analysis, and facility recovery operations. From these elements of the investigation, lessons learned are identified that would benefit other organizations involved with legacy waste management and disposition. Waste management is common to all sites in the DOE Complex and the events and recovery efforts described in this paper may provide beneficial insights to these operations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Cybersecurity for Distance Relay Protection

This project is a DOE follow-up effort on the CREDC workshop held on September 13, 2018 in Cambridge, MA to discuss cybersecurity of distance relays, which considered the benefits, vulnerabilities and risk mitigations for the use of communication systems in power system protection. The objectives of this project are to define the taxonomy of relay protection and associated communications; define use cases describing approaches to reduce the cyber-attack surface on those protective relays; and evaluate the loss of operational functional capability from changes to communication coverage. Mitigating controls will also be evaluated to understand if there are other approaches to reduce attack surfaces while maintaining communications or partial communications. Distance relays are used to protect transmission lines of approximately 10 to 300 miles in length, by detecting short circuits (i.e., faults) on the lines and then tripping circuit breakers in the substation. Such protection systems are a subset of the power system and they incorporate sensing, logic and communication functions. Protection system exposure to cyberattack could be drastically limited by disconnecting relays from all vulnerable communication systems, but this may adversely impact overall power system performance in the absence of cyberattack. This project began with a use case analysis of protection systems with communications, as summarized in this report. It continued with modeling, testing and evaluation in a miniature power system (MPS), located in the Western Area Power Administration (WAPA) Electric Power Training Center (EPTC). The project also incorporated feedback from two industry meetings held in February and September 2019. The suggested next steps account for and complement the work already underway with DOE/CESER funding: 1. Study the performance of LCD and PC vs. PUTT, which is less reliant on communication system performance and GPS timing references. The PUTT scheme could prove to be more resilient to cyberattack or communications-related disruption. It could also be more tolerant of message re-routing with SDN/SDR communication systems. On the other hand, it will be more vulnerable to false tripping during dynamic events or to loss of the voltage signal. The optimum choice of scheme may depend on the specific power system and risk assessment. This study could provide a new template for evaluation based on business functions. 2. Research and develop new methods to detect and monitor distributed physical attacks, possibly using drones, video sensors, thermal sensors, machine learning and other advanced techniques. This will help mitigate the impact of cyberattack on the protection system, and will also help mitigate the impact of wild fires. 3. Implement a scalable PKI for use in electric utility protection systems. This will encourage widespread adoption of secure authentication methods that are already available, but not widely used at present. This will help secure engineering access to the relays. 4. Investigate the use of SDN in combination with SDR to achieve better cybersecurity and electromagnetic security of the network, incorporating path variability. This would help secure both engineering access and peer-to-peer GOOSE messaging. 5. Perform additional testing, with operator evaluation of “red button” scenarios, PUTT vs. LCD, relay mis-operations, and other cyberattacks in the EPTC. This is an important advantage of testing in the EPTC rather than by computer simulation or even hardware-in-the-loop simulation; the EPTC is already dedicated to managing the situational awareness, operator response times and other human impacts. One of the project objectives was to settle on a common nomenclature for this problem space. We have concluded that the OSI layer model, supplemented by ANSI device numbers and other IEEE standards, is already well-accepted by the industry. The IEEE PSRC knowledge base provides a great deal of public information

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

All-purpose, all-sky photometric redshifts for the Legacy Imaging Surveys Data Release 8

ABSTRACT In this paper, we present photometric redshift (photo-z) estimates for the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys, currently the most sensitive optical survey covering the majority of the extragalactic sky. Our photo-z methodology is based on a machine-learning approach, using sparse Gaussian processes augmented with Gaussian mixture models (GMMs) that allow regions of parameter space to be identified and trained separately in a purely data-driven way. The same GMMs are also used to calculate cost-sensitive learning weights that mitigate biases in the spectroscopic training sample. By design, this approach aims to produce reliable and unbiased predictions for all parts of the parameter space present in wide area surveys. Compared to previous literature estimates using the same underlying photometry, our photo-zs are significantly less biased and more accurate at z > 1, with negligible loss in precision or reliability for resolved galaxies at z < 1. Our photo-z estimates offer accurate predictions for rare high-value populations within the parent sample, including optically selected quasars at the highest redshifts (z > 6), as well as X-ray or radio continuum selected populations across a broad range of flux (densities) and redshift. Deriving photo-z estimates for the full Legacy Imaging Surveys Data Release 8, the catalogues provided in this work offer photo-z estimates predicted to be of high quality for ≳9 × 108 galaxies over ${\sim}19\, 400\, \text{deg}^{2}$ and spanning 0 < z ≲ 7, offering one of the most extensive samples of redshift estimates ever produced.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Beyond interpolation: Physics-inspired gating transformers for extrapolating irradiation conditions to novel nuclear fuels

The qualification of advanced nuclear fuels relies on irradiation experiments in test reactors that emulate commercial conditions. Designing these tests requires accurate prediction of key irradiation quantities, particularly heat generation rate and burnup, yet obtaining them typically involves computationally expensive multi-step simulation workflows. We propose a physics-inspired gating transformer (PIGT) that integrates an inverse-square, distance-based attenuation into the encoder representation to bias attention toward physically relevant spatial relationships while retaining data-driven flexibility. Using MiniFuel irradiation data from the High Flux Isotope Reactor at Oak Ridge National Laboratory, we benchmark against ensemble methods, feedforward and recurrent networks, convolutional models, and standard transformers. While baseline models perform well under interpolation, they exhibit a pronounced generalization gap when evaluated on fuels not included in the training set. The proposed model consistently improves extrapolative accuracy and stability, yielding the strongest performance on unseen fuel configurations. These results indicate that a lightweight physics structure embedded within attention mechanisms can substantially improve robustness, enabling more reliable surrogate predictions to accelerate the design of nuclear fuel irradiation experiments.

Fuel qualification↗
DOE OSTI2023

Using angular two-point correlations to self-calibrate the photometric redshift distributions of DECaLS DR9

Calibrating the redshift distributions of photometric galaxy samples is essential in weak lensing studies. The self-calibration method combines angular auto- and cross-correlations between galaxies in multiple photometric redshift (photo-z) bins to reconstruct the scattering rates matrix between redshift bins. In this paper, we test a recently proposed self-calibration algorithm using the DECaLS Data Release 9 and investigate to what extent the scattering rates are determined. We first mitigate the spurious angular correlations due to imaging systematics by a machine learning based method. We then improve the algorithm for χ 2 minimization and error estimation. Finally, we solve for the scattering matrices, carry out a series of consistency tests, and find reasonable agreements: (1) finer photo-z bins return a high-resolution scattering matrix, and it is broadly consistent with the low-resolution matrix from wider bins; (2) the scattering matrix from the Northern Galactic Cap is almost identical to that from Southern Galactic Cap; (3) the scattering matrices are in reasonable agreement with those constructed from the power spectrum and the weighted spectroscopic subsample. We also evaluate the impact of cosmic magnification. Although it changes little the diagonal elements of the scattering matrix, it affects the off-diagonals significantly. The scattering matrix also shows some dependence on scale cut of input correlations, which may be related to a known numerical degeneracy between certain scattering pairs. In conclusion, this work demonstrates the feasibility of the self-calibration method in real data and provides a practical alternative to calibrate the redshift distributions of photometric samples.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Measuring photometric redshifts for high-redshift radio source surveys

With the advent of deep, all-sky radio surveys, the need for ancillary data to make the most of the new, high-quality radio data from surveys like the Evolutionary Map of the Universe (EMU), GaLactic and Extragalactic All-sky Murchison Widefield Array survey eXtended, Very Large Array Sky Survey, and LOFAR Two-metre Sky Survey is growing rapidly. Radio surveys produce significant numbers of Active Galactic Nuclei (AGNs) and have a significantly higher average redshift when compared with optical and infrared all-sky surveys. Thus, traditional methods of estimating redshift are challenged, with spectroscopic surveys not reaching the redshift depth of radio surveys, and AGNs making it difficult for template fitting methods to accurately model the source. Machine Learning (ML) methods have been used, but efforts have typically been directed towards optically selected samples, or samples at significantly lower redshift than expected from upcoming radio surveys. This work compiles and homogenises a radio-selected dataset from both the northern hemisphere (making use of Sloan Digital Sky Survey optical photometry) and southern hemisphere (making use of Dark Energy Survey optical photometry). We then test commonly used ML algorithms such as k-Nearest Neighbours (kNN), Random Forest, ANNz, and GPz on this monolithic radio-selected sample. We show that kNN has the lowest percentage of catastrophic outliers, providing the best match for the majority of science cases in the EMU survey. We note that the wider redshift range of the combined dataset used allows for estimation of sources up to z = 3 before random scatter begins to dominate. When binning the data into redshift bins and treating the problem as a classification problem, we are able to correctly identify ≈ 76% of the highest redshift sources—sources at redshift z > 2.51 —as being in either the highest bin (z > 2.51) or second highest (z = 2.25).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Negative Ion Drift Time Projection Chamber Development for High-Resolution Tracking

Gas Time Projection Chambers (TPCs) are used extensively in High Energy Physics. Modern micro-pattern gaseous detectors (MPGDs) have made it feasible to construct large-volume TPCs with high readout granularity, so that ionization can be imaged with spatial resolution of order 100 μm or better. A challenge with large TPCs is preserving the track geometry over large drift distances. The reduced diffusion offered by negative ion drift (NID) has long been considered a possible means to scaling up TPCs at reduced cost, but NID has up to now required toxic and often flammable multi-component gas mixtures. A number of recent breakthroughs in the field have, however, led to a resurgence of interest in NID gas TPCs: First, it was found that multiple charge carriers in NID can be utilized to measure the absolute position of primary ionization in the drift direction, yielding a powerful new handle on backgrounds, at zero additional cost. Second, work by one of the PIs has revealed SF6 gas to be a non-toxic, non-flammable, NID gas with suitable minority charge carriers present. Third, preliminary work by the PIs suggests that multiple MPGD technologies work well with SF6. This opens the door to a new generation of large, high-resolution, low-background, low-cost gas TPCs. In this work we will investigate MPGD charge multiplication and charge readout in NID TPCs with SF6 gas. The objectives are to quantify the improvement over electron drift TPCs, and to identify the optimal MPGD types, operational parameters, electronics, and vacuum technologies for future low-background experiments. We will measure low-level detector performance parameters such as gain, gain resolution, and spatial resolution, as well as the resulting higher-level performance parameters such nuclear recoil identification efficiency, gamma-ray background rejection, track angle resolution and recoil energy resolution. The proposed work also includes investigation of acrylic vacuum vessels and collaboration on dedicated charge readout chips, both required for next-generation NID MPGD TPCs. MGPD NID TPCs may prove transformative for applications which require both high readout granularity and large detector volumes, such as the reconstruction of nuclear recoils in the context of large-scale directional dark matter searches, measurements of coherent neutrino-nucleus scattering, and directional neutron detection. While the proposed work is focused on nuclear recoils, the lessons learned will be widely applicable to gas TPCs in HEP, and hence have broad impact.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Hierarchical reconstruction of 3D well-connected porous media from 2D exemplars using statistics-informed neural network

The relationships between porous microstructures and transport properties are of fundamental importance in various scientific and engineering applications. Due to the intricacy, stochasticity and heterogeneity of porous media, reliable characterization and modeling of transport properties often require a complete dataset of internal microstructure samples. However, it is often an unbearable cost to acquire sufficient 3D digital microstructures by purely using microscopic imaging systems. Herein this paper presents a machine learning-based technique to hierarchically reconstruct 3D well-connected porous microstructures from one isotropic or several anisotropic low-cost 2D exemplar(s). To compactly characterize the large-scale microstructural features, a Gaussian image pyramid is built for each 2D exemplar. Local morphology patterns are collected from the Gaussian image pyramids, and then they serve as the training data to embed the 2D morphological statistics into feed-forward neural networks at multiple length levels. By using a specially-developed morphology integration scheme, the 3D morphological statistics at different levels can be inferred from the statistics-informed neural networks. Gibbs sampling is adopted to hierarchically reconstruct 3D microstructures by using multi-level 3D morphological statistics, where the large-scale, regional and local morphological patterns are statistically generated and successively added to the same 3D random field. The proposed method is tested on a series of porous media with distinct morphologies, and the statistical equivalence between the reconstructed and the real microstructures is systematically evaluated by comparing morphological descriptors and transport properties. The results demonstrate that the proposed 2D-to-3D microstructure reconstruction method is a universal and efficient approach to generating morphologically and physically realistic samples of porous media.

42 ENGINEERING↗