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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A deep learning approach to real-time HIV outbreak detection using genetic data

Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylogenetic tree inference, they are vulnerable to errors from recombination and impose a high computational cost, making it difficult to obtain real-time results when the number of sequences is in or above the thousands. Here, we propose an alternative strategy to outbreak detection using genomic data based on deep learning methods developed for image classification. The key idea is to use a pairwise genetic distance matrix calculated from viral sequences as an image, and develop convolutional neutral network (CNN) models to classify areas of the images that show signatures of active outbreak, leading to identification of subsets of sequences taken from an active outbreak. We showed that our method is efficient in finding HIV-1 outbreaks with R0 ≥ 2.5, and overall a specificity exceeding 98% and sensitivity better than 92%. We validated our approach using data from HIV-1 CRF01 in Europe, containing both endemic sequences and a well-known dual outbreak in intravenous drug users. Our model accurately identified known outbreak sequences in the background of slower spreading HIV. Importantly, we detected both outbreaks early on, before they were over, implying that had this method been applied in real-time as data became available, one would have been able to intervene and possibly prevent the extent of these outbreaks. This approach is scalable to processing hundreds of thousands of sequences, making it useful for current and future real-time epidemiological investigations, including public health monitoring using large databases and especially for rapid outbreak identification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Capturing the Physics of MaNGA Galaxies with Self-supervised Machine Learning

As available data sets grow in size and complexity, advanced visualization tools enabling their exploration and analysis become more important. In modern astronomy, integral field spectroscopic galaxy surveys are a clear example of increasing high dimensionality and complex data sets, which challenges the traditional methods used to extract the physical information they contain. Here, we present the use of a novel self-supervised machine-learning method to visualize the multidimensional information on stellar population and kinematics in the MaNGA survey in a 2D plane. Our framework is insensitive to nonphysical properties such as the size of the integral field unit and is therefore able to order galaxies according to their resolved physical properties. Using the extracted representations, we study how galaxies distribute based on their resolved and global physical properties. We show that even when exclusively using information about the internal structure, galaxies naturally cluster into two well-known categories, rotating main-sequence disks and massive slow rotators, from a purely data-driven perspective, hence confirming distinct assembly channels. Low-mass rotation-dominated quenched galaxies appear as a third cluster only if information about the integrated physical properties is preserved, suggesting a mixture of assembly processes for these galaxies without any particular signature in their internal kinematics that distinguishes them from the two main groups. The framework for data exploration is publicly released with this publication, ready to be used with the MaNGA or other integral field data sets.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1988

Flow Analysis of Space Shuttle Feed Line 17-inch Disconnect Valve

A steady incompressible three-dimensional viscous flow analysis has been conducted for the Space Shuttle External Tank/Orbiter propellant feed line disconnect flapper valves with upstream elbows. The full Navier-Stokes code, INS3D, is modified to handle interior obstacles. Grids are generated by SVTGD3D code. Two dimensional initial grids in the flow cross section with and without the flappers are improved by elliptic smoothing to provide better orthogonality, clustering and smoothness to the three dimensional grid. The flow solver is tested for stability and convergence in the presence of interior flappers. An under-relaxation scheme has been incorporated to improve the solution stability. Important flow characteristics such as secondary flows, recirculation, vortex and wake regions, and separated flows are observed. Computed values for forces, moments, and pressure drop are in satisfactory agreement with water flow test data covering a maximum tube Reynolds number of 3.5 x 10(exp 6). The results will serve as a guide to improved design and enhanced testing of the disconnect.

Kandula, Max↗
DOE OSTI2024

DESI Emission-line Galaxies: Unveiling the Diversity of [O II ] Profiles and Its Links to Star Formation and Morphology

We study the [O II ] profiles of emission-line galaxies (ELGs) from the Early Data Release of the Dark Energy Spectroscopic Instrument (DESI). To this end, we decompose and classify the shape of [O II ] profiles with the first two eigenspectra derived from principal component analysis. Our results show that DESI ELGs have diverse line profiles, which can be categorized into three main types: (1) narrow lines with a median width of ∼50 km s −1 , (2) broad lines with a median width of ∼80 km s −1 , and (3) two redshift systems with a median velocity separation of ∼150 km s −1 , i.e., double-peak galaxies. To investigate the connections between the line profiles and galaxy properties, we utilize the information from the COSMOS data set and compare the properties of ELGs, including star formation rate (SFR) and galaxy morphology, with the average properties of reference star-forming galaxies with similar stellar mass, sizes, and redshifts. Our findings show that, on average, DESI ELGs have a higher SFR and more asymmetrical/disturbed morphology than the reference galaxies. Moreover, we uncover a relationship between the line profiles, the excess SFR, and the excess asymmetry parameter, showing that DESI ELGs with broader [O II ] line profiles have more disturbed morphology and higher SFR than the reference star-forming galaxies. Finally, we discuss possible physical mechanisms giving rise to the observed relationship and the implications of our findings on the galaxy clustering measurements, including the halo occupation distribution modeling of DESI ELGs and the observed excess velocity dispersion of the satellite ELGs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Full Modeling and parameter compression methods in configuration space for DESI 2024 and beyond

In the contemporary era of high-precision spectroscopic surveys, led by projects like DESI, there is an increasing demand for optimizing the extraction of cosmological information from clustering data. This work conducts a thorough comparison of various methodologies for modeling the full shape of the two-point statistics in configuration space. We investigate the performance of both direct fits (Full Modeling) and the parameter compression approaches (ShapeFit and Standard). We utilize the ABACUS-SUMMIT simulations, tailored to exceed DESI's precision requirements. Particularly, we fit the two-point statistics of three distinct tracers (LRG, ELG, and QSO), by employing a Gaussian Streaming Model in tandem with Convolution Lagrangian Perturbation Theory and Effective Field Theory. We explore methodological setup variations, including the range of scales, the set of galaxy bias parameters, the inclusion of the hexadecapole, as well as model extensions encompassing varying ns and allowing for w 0 w a CDM dark energy model. Throughout these varied explorations, while precision levels fluctuate and certain configurations exhibit tighter parameter constraints, our pipeline consistently recovers the parameter values of the mocks within 1σ in all cases for a 1-year DESI volume. Additionally, we compare the performance of configuration space analysis with its Fourier space counterpart using three models: PyBird, FOLPS and velocileptors, presented in companion papers. We find good agreement with the results from all these models.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Cosmological implications of the full shape of anisotropic clustering measurements in BOSS and eBOSS

We present the analysis of the full shape of anisotropic clustering measurement from the extended Baryon Oscillation Spectroscopic Survey (eBOSS) quasar sample together with the combined galaxy sample from the Baryon Oscillation Spectroscopic Survey (BOSS), re-analysed using an updated recipe for the non-linear matter power spectrum and the non-local bias parameters. We obtain constraints for flat Lambda cold dark matter cosmologies, focusing on the cosmological parameters that are independent of the Hubble parameter h. Our recovered value for the Root Mean Square (RMS) linear perturbation theory variance as measured on the scale of 12 Mpc is σ 12 = 0.805 ± 0.049, while using the traditional reference scale of 8 h –1 Mpc gives σ 8 = 0.815 ± 0.044. We quantify the agreement between our measurements and the latest cosmic microwave background data from Planck using the suspiciousness metric, and find them to be consistent within 0.64 ± 0.03σ. Combining our clustering constraints with the 3 × 2pt data sample from the Dark Energy Survey Year 1 release slightly degrades this agreement to the level of 1.54 ± 0.08σ, while still showing an overall consistency with Planck. We furthermore study the effect of imposing a Planck – like prior on the parameters that define the shape of the linear matter power spectrum, and find significantly tighter constraints on the parameters that control the evolution of density fluctuations. In particular, the combination of low-redshift data sets prefers a value of the physical dark energy density ω DE = 0.335 ± 0.011, which is 1.7σ higher than the one preferred by Planck.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models

This study evaluates the effect of complete nationwide lockdown in 2020 on residential electricity demand across 13 Indian cities and the role of digitalisation using a public smart meter dataset. We undertake a data-driven approach to explore the energy impacts of work-from-home norms across five dwelling typologies. Our methodology includes climate correction, dimensionality reduction and machine learning-based clustering using Gaussian Mixture Models of daily load curves. Results show that during the lockdown, maximum daily peak demand increased by 150-200% as compared to 2018 and 2019 levels for one room-units (RM1), one bedroom-units (BR1) and two bedroom-units (BR2) which are typical for low- and middle-income families. While the upper-middle- and higher-income dwelling units (i.e., three (3BR) and more-than-three bedroom-units (M3BR)) saw night-time demand rise by almost 44% in 2020, as compared to 2018 and 2019 levels. Our results also showed that new peak demand emerged for the lockdown period for RM1, BR1 and BR2 dwelling typologies. We found that the lack of supporting socioeconomic and climatic data can restrict a comprehensive analysis of demand shocks using similar public datasets, which informed policy implications for India's digitalisation. We further emphasised improving the data quality and reliability for effective data-centric policymaking.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Impact of Controlled Storage Conditions on the Hydrolysis and Surface Morphology of Amorphous-UO 3

The hydration and morphological effects of amorphous (A)-UO 3 following storage under varying temperature and relative humidity have been investigated. This study provides valuable insight into U-oxide speciation following aging, the U-oxide quantitative morphological data set, and, overall, the characterization of nuclear material provenance. A-UO 3 was synthesized via the washed uranyl peroxide synthetic route and aged based on a 3-factor circumscribed central composite design of experiment. Target aging times include 2.57, 7.00, 14.0, 21.0, and 25.4 days, temperatures of 5.51, 15.0, 30.0, 45.0, and 54.5 °C, and relative humidities of 14.2, 30.0, 55.0, 80.0, and 95.8% were examined. Following aging, crystallographic changes were quantified via powder X-ray diffraction and an internal standard Rietveld refinement method was used to confirm the hydration of A-UO 3 to crystalline schoepite phases. The particle morphology from scanning electron microscopy images was quantified using both the Morphological Analysis of MAterials software and machine learning. Results from the machine learning were processed via agglomerative hierarchical clustering analysis to distinguish trends in morphological attributes from the aging study. Significantly hydrated samples were found to have a much larger, plate-like morphology in comparison to the unaged controls. Predictive modeling via a response surface methodology determined that while aging time, temperature, and relative humidity all have a quantifiable effect on A-UO 3 crystallographic and morphological changes, relative humidity has the most significant impact.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗
NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗
DOE OSTI2023

Data-Driven Smoothers for Extreme-Scale Computing

Patch-based relaxation refers to a family of methods for solving linear systems which partitions the matrix into smaller pieces often corresponding to groups of adjacent degrees of freedom residing within patches of the computational domain. The two most common families of patch-based methods are block-Jacobi and Schwarz methods, where the former typically corresponds to non-overlapping domains and the later implies some overlap. We focus on cases where each patch consists of the degrees of freedom on a finite element method mesh cell. Patch methods often capture complex local physics much more effectively than simpler point-smoothers such as Jacobi; however, forming, inverting, and applying each patch can be prohibitively expensive in terms of both storage and computation time. To this end, we propose several approaches for performing analysis on these patches and constructing a reduced representation. The compression techniques rely on either matrix norm comparisons or unsupervised learning via a clustering approach. We illustrate how it is frequently possible to retain/factor less than 5% of all patches and still develop a method that converges only a little slower than when all patches are stored/factored.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

4.32 Billion Year Old Impact Melts at Apollo 14: Dating the Procellarum Basin?

Introduction: Recent work has improved our understanding of lunar crustal structure and basin evolution on the Moon, but its early impact history and implications for solar system dynamics remain poorly established [1,2]. In particular, there is a derth of absolute ages, especially for the period >4.0 Ga, that could be related to basin-forming events. Here we present U-Pb isotopic data for Zr-rich minerals found in impact-melt fragments from Apollo 14 soil sample 14163 that, when combined with previously published data, dates an impact event at 4324±15 Ma. The compositions and ages of these impact-melt fragments provide unique information about the timing of early impact events and the composition of the lunar crust. Results: Fourteen rocklets ranging in size from 1 to 3 mm were extracted from soil 14163. Most are impactmelt rocks with 10-30% clasts of mostly pyroxene and plagioclase (100 to 500 μm) in a crystalline matrix. Textures of the matrix vary from subophitic, formed by intergrowths of 10-20 μm plagioclase and pyroxene crystals, to poikilitic, with plagioclase and pyroxene reaching 20-50 μm size. Some fragments that contain no visible clasts have similar textures are also interpreted as impact-melt rocks. All fragments contain notably large proportions of euhedral to subhedral ilmenite, zircon, apatite/merrillite and less abundant zirconolite and baddeleyite in the melt matrices. These grains often form intricate intergrowths with each other and rock-forming minerals, indicating their crystallization from the melt (Fig. 1). However, some slightly larger (~50 μm) zircon and phosphate grains can be interpreted as relict clasts based on their granular textures and relationships with the surrounding phases (Fig. 1). This implies that zircon and phosphate minerals were present in the target rocks. Some of these grains were profoundly remelted during the impact, which resulted in an oversaturation of the melt in Zr and P and crystallization of new grains of Zrrich minerals and phosphates during melt solidification. The presence of zircon and phosphates indicates that the melts were significantly enriched in KREEP components and ties their provenance to the Procellarum- KREEP Terrane (PKT) [3]. Combined U-Pb zircon data obtained for all fragments form two main clusters on a concordia diagram (Fig. 2), between about 4.3 and 3.9 Ga. Phosphate data mostly concentrate on the younger end of this age range but some analyses are almost as old as the older zircon grains (Fig. 2). Figure 1: Zircon (Zr) and phosphate (Apt; Mer) grains in impact melt fragments from Apollo 14 soil 14161. A-zircon grains crystallized from impact melt; B-phosphate grain inherited from the target; C- granular zircon grains Our interpretation of these data is that all U-rich minerals experienced variable resetting of the U-Pb system, with phosphate, where closure temperature is significantly lower than that in zircon, affected more profoundly by Pb loss than zircon. Our best estimate of the time of formation of zircon and phosphate is based on statistically valid analysis of grains interpreted texturally as grown from the impact melt (Fig. 2). Ten of these analyses define an age of 4324±15 Ma (MSWD=3.0, probability of fit P=0.002). Our best estimate for the time of resetting is obtained by combining data from phosphates that are statistically indistinguishable from 3.9 Ga within the analytical uncertainties. This group is represented by 18 analyses of 15 phosphate grains from different fragments (Fig. 2) and defines an age of 3922±6 Ma (MSWD=1.2, P=0.23). Conversely, a minimum age of the target lithologies, remelted in the impact that produced the rocklets studied here (Fig. 2), can be determined from the five oldest analyses of zircon clasts at 4338±13 Ma (MSWD=1.5, P=0.2), which is indistinguishable from the age of the impact melt within the uncertainties. The obtained ages, combined with textural evidence, imply that the impact melt was formed at 4324±15 Ma, and that it occurred in a zircon-rich target with a minimum age of 4338±13 Ma. Further reworking occurred during a second impact event at 3922±6 Ma. Discussion: The younger age of 3922±6 Ma can be interpreted as the time of the Imbrium impact. The older age of 4324±15 Ma would then be the time of formation of the impact melt, which was then caught in the Imbrium ejecta either at the Apollo 14 landing site or within the target rocks of the Imbrium impact. The presence of abundant accessory phases such as zircons and phosphates is consistent with a substantial KREEP component in the analyzed particles. The current distribution of KREEP on the lunar surface appears to be strongly influenced by Imbrum ejecta [4] so interpretation of the 4.32 Ga age depends in part on assumed structure of the pre-impact crust and distribution of KREEP within the crust. If KREEP was present only in the deep crust at 4.32 Ga, then a basin-scale impact possibly analogous to Imbrium or larger may be necessary to excavate a KREEPy impact melt at this time. However, if KREEPy materials were present closer to the surface perhaps due to redistribution related to Mg-suite magmatism, then smaller impacts might be able to rework KREEPy compositions at shallower depths. The coherence of the data on the particles analysed here suggests a large volume of melt that has been preserved since 4.32 Ga, consistent with a large impact event. Relict zircons and mineral clasts suggests that the igneous crust in the vicinity of this impact was well developed by at least 4.34 Ga, similar to the model age of KREEP and older than the isochron ages of many Mg-suite cumulates [5]. A problem that confronts all lunar sample studies using the current collection is that the pre-Imbrium geology of the PKT (the source of these 4.32 Ga impact melts) is not well constrained. The South Pole-Aitken basin contains regions that are moderately enriched in Th, but its ejecta is Th-poor [6]; therefore these A14 fragments are probably not SPA ejecta. Alternatively, these fragments may represent formation of a hypothesized Procellarum basin [7] although the lack of a clearly defined basin ejecta signature is a potential problem with that interpretation. In any case, they provide a younger limit on the age of lunar differentiation and formation of KREEP within the lunar crust. Figure 2: U-Pb data for zircon and phosphate grains from impact melt fragments. A-all data; B-data used for age calculations. References: [1] Orgel C., Michael G., Fassett C. I., van der Bogert C. H., Riedel C., Kneissl T., and Hiesinger H. (2018) J. Geophys. Res. Planets 123, 748- 762. [2] Evans A. J., Andrews-Hanna J. C., Head J. W., Soderblom J. M., Solomon S. C., and Zuber M. T. (2018) J. Geophys. Planets. 123, 1596-1617. [3] Jolliff, B.L., Gillis, J.J., Haskin, L.A., Korotev, R.L. and Wieczorek, M.A. (2000) J. Geophys. Res: Planets 105, 4197-4216. [4] Haskin L. A. (1998) J. Geophys. Res. Planets 103, 1679-1689. [5] Borg L.E., Gaffney A.M., and Shearer C.K. (2015) MAPS 50, 715-732. [6] Moriarty, D.P., Watkins, R.N., Valencia, S.N., Kendall, J.D., Evans, A.J., Dygert, N. and Petro, N.E. (2021) J. Geophys. Res. Planets 126. [7] Zhu, M.H., Wünnemann, K., Potter, R.W., Kleine, T. and Morbidelli, A. (2019) J. Geophys. Res: Planets 124, 2117-2140

M D Norman↗
DOE OSTI2020

Hypernetwork science via high-order hypergraph walks

We propose high-order hypergraph walks as a framework to generalize graph-based network science techniques to hypergraphs. Edge incidence in hypergraphs is quantitative, yielding hypergraph walks with both length and width. Graph methods which then generalize to hypergraphs include connected component analyses, graph distance-based metrics such as closeness centrality, and motif-based measures such as clustering coefficients. We apply high-order analogs of these methods to real world hypernetworks, and show they reveal nuanced and interpretable structure that cannot be detected by graph-based methods. Lastly, we apply three generative models to the data and find that basic hypergraph properties, such as density and degree distributions, do not necessarily control these new structural measurements. Our work demonstrates how analyses of hypergraph-structured data are richer when utilizing tools tailored to capture hypergraph-native phenomena, and suggests one possible avenue towards that end.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

The Sagittarius stream in Gaia Early Data Release 3 and the origin of the bifurcations

The Sagittarius dwarf spheroidal (Sgr) is a dissolving galaxy being tidally disrupted by the Milky Way (MW). Its stellar stream still poses serious modelling challenges, which hinders our ability to use it effectively as a prospective probe of the MW gravitational potential at large radii. Our goal is to construct the largest and most stringent sample of stars in the stream with which we can advance our understanding of the Sgr-MW interaction, focusing on the characterization of the bifurcations. We improved on previous methods based on the use of the wavelet transform to systematically search for the kinematic signature of the Sgr stream throughout the whole sky in the Gaia data. We then refined our selection via the use of a clustering algorithm on the statistical properties of the colour-magnitude diagrams. Our final sample contains more than 700 000 candidate stars and is three times larger than previous Gaia samples. With it, we have been able to detect the bifurcation of the stream in both the northern and southern hemispheres, which requires four branches (two bright and two faint) to fully describe the system. We present the detailed proper motion distribution of the trailing arm as a function of the angular coordinate along the stream, showing, for the first time, the presence of a sharp edge (on the side of the small proper motions) beyond which there are no Sgr stars. We also characterize the correlation between kinematics and distance. Finally, the chemical analysis of our sample shows that the faint branch of the bifurcation is more metal poor than the bright. We provide analytical descriptions for the proper motion trends as well as for the sky distribution of the four branches of the stream. Based on our analysis, we interpret the bifurcations as a misaligned overlap of the material stripped at the antepenultimate pericentre (faint branches) with the stars ejected at the penultimate pericentre (bright branch), given that Sgr just went through its perigalacticon. The source of this misalignment is still unknown, but we argue that models with some internal rotation in the progenitor – at least during the time of stripping of the stars that are now in the faint branches – are worth exploring.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Reaction–drift–diffusion models from master equations: application to material defects

We present a general method to produce well-conditioned continuum reaction–drift–diffusion equations directly from master equations on a discrete, periodic state space. We assume the underlying data to be kinetic Monte Carlo models (i.e. continuous-time Markov chains) produced from atomic sampling of point defects in locally periodic environments, such as perfect lattices, ordered surface structures or dislocation cores, possibly under the influence of a slowly varying external field. Our approach also applies to any discrete, periodic Markov chain. Here, the analysis identifies a previously omitted non-equilibrium drift term, present even in the absence of external forces, which can compete in magnitude with the reaction rates, thus being essential to correctly capture the kinetics. To remove fast modes which hinder time integration, we use a generalized Bloch relation to efficiently calculate the eigenspectrum of the master equation. A well conditioned continuum equation then emerges by searching for spectral gaps in the long wavelength limit, using an established kinetic clustering algorithm to define a proper reduced, Markovian state space.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Probing Intra-Halo Light with Galaxy Stacking in CIBER Images

We study the stellar halos of 0.2 ≲ $z$ ≲ 0.5 galaxies with stellar masses spanning $M$* ~ 10 10.5 to 10 12 $M$ ⊙ (approximately $L$* galaxies at this redshift) using imaging data from the Cosmic Infrared Background Experiment (CIBER). A previous CIBER fluctuation analysis suggested that intra-halo light (IHL) contributes a significant portion of the near-infrared extragalactic background light (EBL), the integrated emission from all sources throughout cosmic history. In this work, we carry out a stacking analysis with a sample of ~30,000 Sloan Digital Sky Survey (SDSS) photometric galaxies from CIBER images in two near-infrared bands (1.1 and 1.8 $μ$m) to directly probe the IHL associated with these galaxies. We stack galaxies in five sub-samples split by brightness and detect an extended galaxy profile beyond the instrument point-spread function (PSF) derived by stacking stars. We jointly fit a model for the inherent galaxy light profile plus large-scale one- and two-halo clustering to measure the extended galaxy IHL. We detect nonlinear one-halo clustering in the 1.8 $μ$m band at a level consistent with numerical simulations. Furthermore, by extrapolating the fraction of extended galaxy light, we measure to all galaxy mass scales, we find ~30%/15% of the total galaxy light budget from galaxies is at radius $r$ > 10/20 kpc, respectively. These results are new at near-infrared wavelengths at the $L$* mass scale and suggest that the IHL emission and one-halo clustering could have appreciable contributions to the amplitude of large-scale EBL background fluctuations.

79 ASTRONOMY AND ASTROPHYSICS↗