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DOE OSTI2021

Hubble Frontier Field Clusters and Their Parallel Fields: Photometric and Photometric Redshift Catalogs

We present a multiband analysis of the six Hubble Frontier Field clusters and their parallel fields, producing catalogs with measurements of source photometry and photometric redshifts. We release these catalogs to the public along with maps of intracluster light and models for the brightest galaxies in each field. This rich data set covers a wavelength range from 0.2 to 8 μm, utilizing data from the Hubble Space Telescope, Keck Observatories, Very Large Telescope array, and Spitzer Space Telescope. We validate our products by injecting into our fields and recovering a population of synthetic objects with similar characteristics to those in real extragalactic surveys. The photometric catalogs contain a total of over 32,000 entries, with 50% completeness at a threshold of mag AB ~ 29.1 for unblended sources and magAB ~ 29 for blended ones, in the IR-weighted detection band. Photometric redshifts were obtained by means of template fitting and have an average outlier fraction of 10.3% and scatter σ = 0.067 when compared to spectroscopic estimates. The software we devised, after being tested in the present work, will be applied to new data sets from ongoing and future surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Photometric Properties of Jupiter Trojans Detected by the Dark Energy Survey

The Jupiter Trojans are a large group of asteroids that are coorbiting with Jupiter near its L4 and L5 Lagrange points. The study of Jupiter Trojans is crucial for testing different models of planet formation that are directly related to our understanding of solar system evolution. In this work, we select known Jupiter Trojans listed by the Minor Planet Center from the full six years data set (Y6) of the Dark Energy Survey (DES) to analyze their photometric properties. The DES data allow us to study Jupiter Trojans with a fainter magnitude limit than previous studies in a homogeneous survey with griz band measurements. We extract a final catalog of 573 unique Jupiter Trojans. Our sample include 547 asteroids belonging to L5. This is one of the largest analyzed samples for this group. By comparing with the data reported by other surveys we found that the color distribution of L5 Trojans is similar to that of L4 Trojans. We find that L5 Trojans’ g − i and g − r colors become less red with fainter absolute magnitudes, a trend also seen in L4 Trojans. Both the L4 and L5 clouds consistently show such a color–size correlation over an absolute magnitude range 11 < H < 18. We also use DES colors to perform taxonomic classifications. C- and P-type asteroids outnumber D-type asteroids in the L5 Trojans DES sample, which have diameters in the 5–20 km range. This is consistent with the color–size correlation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Early Results from GLASS-JWST. II. NIRCam Extragalactic Imaging and Photometric Catalog

We present the reduced images and multiwavelength catalog of the first JWST NIRCam extragalactic observations from the GLASS Early Release Science Program, obtained as coordinated parallels of the NIRISS observations of the Abell 2744 cluster. Images in seven bands (F090W, F115W, F150W, F200W, F277W, F356W, and F444W) have been reduced using an augmented version of the official JWST pipeline; we discuss the procedures adopted to remove or mitigate defects in the raw images. We obtain a multiband catalog by means of forced aperture photometry on point-spread function (PSF)-matched images at the position of F444W-detected sources. The catalog is intended to enable early scientific investigations, and it is optimized for faint galaxies; it contains 6368 sources, with limiting magnitude 29.7 at 5 σ in F444W. We release both images and catalog in order to allow the community to become familiar with the JWST NIRCam data and evaluate their merit and limitations given the current level of knowledge of the instrument.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Small Seismic Events in Oklahoma Detected and Located by Machine Learning–Based Models

A complete earthquake catalog is essential to understand earthquake nucleation and fault stress. Following the Gutenberg–Richter law, smaller, unseen seismic events dominate the earthquake catalog and are invaluable for revealing the fault state. The published earthquake catalogs, however, typically miss a significant number of small earthquakes. Part of the reason is due to a limitation of conventional algorithms, which can hardly extract small signals from background noise in a reliable and efficient way. To address this challenge, we utilized a machine learning method and developed new models to detect and locate seismic events. These models are efficient in processing a large amount of seismic data and extracting small seismic events. We applied our method to seismic data in Oklahoma, United States, and detected ~14 times more earthquakes compared with the standard Oklahoma Geological Survey catalog. The rich information contained in the new catalog helps better understand the induced earthquakes in Oklahoma.

58 GEOSCIENCES↗

DOE OSTI2020

The Sixth Data Release of the Radial Velocity Experiment (Rave). II. Stellar Atmospheric Parameters, Chemical Abundances, and Distances

We present part 2 of the sixth and final Data Release (DR6) of the Radial Velocity Experiment (Rave), a magnitude-limited (9<I<12) spectroscopic survey of Galactic stars randomly selected in Earth’s southern hemisphere. The Rave medium-resolution spectra (R ∼ 7500) cover the Ca triplet region (8410–8795 Å) and span the complete time frame from the start of Rave observations on 2003 April 12 to their completion on 2013 April 4. In the second of two publications, we present the data products derived from 518,387 observations of 451,783 unique stars using a suite of advanced reduction pipelines focusing on stellar atmospheric parameters, in particular purely spectroscopically derived stellar atmospheric parameters (T{sub eff}, logg, and the overall metallicity), enhanced stellar atmospheric parameters inferred via a Bayesian pipeline using Gaia DR2 astrometric priors, and asteroseismically calibrated stellar atmospheric parameters for giant stars based on asteroseismic observations for 699 K2 stars. In addition, we provide abundances of the elements Fe, Al, and Ni, as well as an overall [α/Fe] ratio obtained using a new pipeline based on the GAUGUIN optimization method that is able to deal with variable signal-to-noise ratios. The Rave DR6 catalogs are cross-matched with relevant astrometric and photometric catalogs, and are complemented by orbital parameters and effective temperatures based on the infrared flux method. The data can be accessed via the Rave website (http://rave-survey.org) or the Vizier database.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Combined tracer analysis for DESI 2024 BAO

This paper demonstrates how the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) and future baryon acoustic oscillations (BAO) analyses can optimally combine overlapping tracers (galaxies of distinct types) in the same redshift range. We make a unified catalog of Luminous Red Galaxies (LRGs) and Emission Line Galaxies (ELGs) in the redshift range 0.8 < z < 1.1 and investigate the impact on the BAO constraints. DESI DR1 contains ∼ 30% of the final DESI LRG sample and less than 25% of the final ELG sample, and the combination of LRGs and ELGs increases the number density and reduces the shot noise. We developed a pipeline to merge the overlapping tracers using galaxy bias as an approximately optimal weight and tested the pipeline on a suite of Abacus simulations, calibrated on the final version of the DESI Early Data Release. When applying our pipeline to the DESI DR1 catalog, we find an improvement in the BAO constraints of 11% for α iso and ∼ 7.0% for α AP consistent with our findings in mock catalogs. Our analysis was integrated into the DESI DR1 BAO analysis to produce theLRG+ELG result in the 0.8 < z < 1.1 redshift bin, which provided the most precise BAO measurement from DESI DR1 with a 0.86% constraint on the BAO distance scale and a 9.1σ detection of the isotropic BAO feature.

baryon acoustic oscillations↗

DOE OSTI2021

Active anomaly detection for time-domain discoveries

Aims. We present the first evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Methods. Our method follows an active learning strategy where the learning algorithm chooses objects which can potentially improve the learner if additional information about them is provided. This new information is subsequently used to update the machine learning model, allowing its accuracy to evolve with each new information. For the case of anomaly detection, the algorithm aims to maximize the number of scientifically interesting anomalies presented to the expert by slightly modifying the weights of a traditional Isolation Forest (IF) at each iteration. In order to demonstrate the potential of such techniques, we apply the Active Anomaly Discovery (AAD) algorithm to 2 data sets: simulated light curves from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) and real light curves from the Open Supernova Catalog. We compare the AAD results to those of a static IF. For both methods, we performed a detailed analysis for all objects with the ~2% highest anomaly scores. Results. We show that, in the real data scenario , AAD was able to identify ~80% more true anomalies than the IF. This result is the first evidence that AAD algorithms can play a central role in the search for new physics in the era of large scale sky surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Predictive Phenomics Initiative Project Dataset Catalog Collection

The Predictive Phenomics Science & Technology Initiative (PPI) at Pacific Northwest National Laboratory are tackling the grand challenge of understanding and predicting phenotype by identifying the molecular basis of function and enable function-driven design and control of biological systems. Research projects within this initiative are divided into three Thrust Areas (TAs): TA1) Enhancing Multi-Scale Phenomics Measurements, TA2) Identifying Molecular Patterns of Biological Function, and TA3) Computational Methods - Phenotypic Signatures. In efforts to enable discovery, reproducibility, and reuse of PPI-funded digital research data generated or used through the course of the proposed research-funded lifecycles, all corresponding digital data assets conducted under the Laboratory Directed Research and Development Program at PNNL are linked to this PPI dataset catalog collection.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Predictive Phenomics Initiative Project Dataset Catalog Collection

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Data Analysis of the 2020 Central Idaho Mainshock-Aftershock Sequence

In an effort to inform the Senior Seismic Hazard Analysis Committee for the Idaho National Laboratory, we provide an improved aftershock catalog related to the March 31, 2020, Mw6.5 Stanley, Idaho earthquake from picks related to a temporary network of two real-time and 15 non-telemetered seismometers within the epicentral area. From the permanent and temporary (XP) real-time network, the USGS cataloged 1,946 aftershocks between April 1, 2020 and October 31, 2020. To improve aftershock location and magnitudes, we manually picked arrival times of P and S waves from off-line stations in the XP temporary network, generated a new crustal velocity model, and independently relocated each event using the HypoDD double-difference earthquake algorithm. We created our new velocity model from existing broadband and active source seismic campaign data that were acquired near the epicentral region prior to the 2020 earthquake. We compare arrival time differences, epicentral locations and depths between aftershocks recorded with the two catalogs. We find the addition of local stations provides tighter aftershock clustering that suggests an improved aftershock locations. To detect lower magnitude events, we employed deep learning. Our method solves common problems associated with detecting many events that have a low signal-to-noise ratio. From the machine learning database, we detected more than 74,000 aftershocks. Based on the number of identified earthquakes and Gutenberg-Richter relationships derived from the USGS catalog, we estimate that we have reduced the completion magnitude for the Stanley earthquake sequence to below M1 using this machine learning approach. We located each aftershock with our new velocity model. Our new velocity model and picks suggests aftershocks occurred mostly at shallower depths than assessed in the USGS catalog. These aftershocks align along two linear trends that suggest the activation of two unnamed primary faults.

58 GEOSCIENCES↗

DOE OSTI2022

ADDGALS: Simulated Sky Catalogs for Wide Field Galaxy Surveys

Abstract We present a method for creating simulated galaxy catalogs with realistic galaxy luminosities, broadband colors, and projected clustering over large cosmic volumes. The technique, denoted Addgals (Adding Density Dependent GAlaxies to Lightcone Simulations), uses an empirical approach to place galaxies within lightcone outputs of cosmological simulations. It can be applied to significantly lower-resolution simulations than those required for commonly used methods such as halo occupation distributions, subhalo abundance matching, and semi-analytic models, while still accurately reproducing projected galaxy clustering statistics down to scales of r ∼ 100 h −1 kpc . We show that Addgals catalogs reproduce several statistical properties of the galaxy distribution as measured by the Sloan Digital Sky Survey (SDSS) main galaxy sample, including galaxy number densities, observed magnitude and color distributions, as well as luminosity- and color-dependent clustering. We also compare to cluster–galaxy cross correlations, where we find significant discrepancies with measurements from SDSS that are likely linked to artificial subhalo disruption in the simulations. Applications of this model to simulations of deep wide-area photometric surveys, including modeling weak-lensing statistics, photometric redshifts, and galaxy cluster finding, are presented in DeRose et al., and an application to a full cosmology analysis of Dark Energy Survey (DES) Year 3 like data is presented in DeRose et al. We plan to publicly release a 10,313 square degree catalog constructed using Addgals with magnitudes appropriate for several existing and planned surveys, including SDSS, DES, VISTA, Wide-field Infrared Survey Explorer, and Rubin Observatory’s Legacy Survey of Space and Time.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

The Compilation and Validation of the Spectroscopic Redshift Catalogs for the DESI-COSMOS and DESI-XMM-LSS Fields

Over several dedicated programs that include targets beyond the main cosmological samples, the Dark Energy Spectroscopic Instrument collected spectra for 304,970 unique objects in two fields centered on the COSMOS and XMM-LSS fields. In this work, we develop spectroscopic redshift robustness criteria for those spectra, validate these criteria using visual inspection, and provide two custom value-added catalogs with our redshift characterizations. With these criteria, we reliably classify 212,935 galaxies below z < 1.6, 9713 quasars, and 35,222 stars. The resulting catalogs achieve a redshift purity exceeding 99.4% across all galaxy samples. As a critical element in characterizing the selection function, we provide the description of 70 different algorithms that were used to select these targets from imaging data. To facilitate joint imaging/spectroscopic analyses, we provide row-matched photometry from the Dark Energy Camera, Hyper-Suprime Cam, and public COSMOS2020 photometric catalogs. Finally, we demonstrate example applications of these large catalogs to photometric redshift estimation, cluster finding, and completeness studies.

Ratajczak, J. [Univ. of Utah, Salt Lake City, UT (↗

DOE OSTI2023

Weak Gravitational Lensing of Low Surface Brightness Galaxies in the DES Year 3 Catalog

We present galaxy-galaxy lensing measurements of a sample of low surface brightness galaxies (LSBGs) drawn from the Dark Energy Survey Year 3 (Y3) data. LSBGs are diffuse galaxies with a surface brightness dimmer than the ambient night sky. Given their faintness, the use of standard observational techniques proves challenging. Weak gravitational lensing probes both the baryonic and dark matter content of galaxies, rendering it a powerful technique to estimate LSBG masses. The LSBG lens sample consists of 23, 790 total extended galaxies separated into red and blue color types at $g-i\ge 0.60$ and $g-i\le 0.60$, respectively. We use the Y3 \metacal\ shape catalog as the source sample, with a number density of {\color{red}$5.59 \mathrm{gal}/\mathrm{arcmin}^2$}. We measure the tangential shear around the lens galaxies across angular scales of $0.25-400$ arcmin and find a signal-to-noise of 6.67 for red galaxies, 2.17 for blue galaxies, and 5.30 for the total sample. W e fit a model built from two NFW profiles corresponding to the LSBG dark matter subhalo and host halo to the red LSBG shear measurements with an MCMC. We estimate the host halo mass at $7.3 ^{+2.0}_{-1.6}\times 10^{12} M_\mathrm{\odot}$. We place a 95\% upper bound on the subhalo mass at $2.8\times 10^{11} M_\mathrm{\odot}$. We utilize the lens sample photometry to obtain an estimate of the red LSBG stellar mass distribution. We compare the ratio between the stellar mass and the subhalo mass to the parameterized, satellite-specific SHMR \citep{Moster2010}. This work represents the first example of an attempted constraint on the masses of LSBGs using weak gravitational lensing.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Machine-Learning-Based Analysis of the Guy-Greenbrier, Arkansas Earthquakes: A Tale of Two Sequences

Here, we revisited the June 2010 to October 2011 Guy-Greenbrier earthquake sequence in central Arkansas using PhaseNet, a deep neural network trained to pick P and S arrival times. We applied PhaseNet to continuous waveform data and used phase association and hypocenter relocation to locate nearly 90,000 events. Our catalog suggests that the sequence consists of two adjacent earthquake sequences on the same fault and that the second sequence may be associated with the wastewater disposal well to the west of the Guy-Greenbrier Fault, rather than the wells to the north and the east that were previously implicated. We find that each sequence is composed of many small clusters that exhibit diffusion along the fault at shorter timescales. Our study demonstrates that machine-learning-based earthquake catalog development is now feasible and will yield new insights into earthquake behavior.

58 GEOSCIENCES↗

DOE OSTI2021

November 2021 Operational Highlights

Completed: 1. the initial tests of document workflows for Titan on the Red, which included standing up a software encryption capability to test data ingestion; 2. a software encryption capability in order to ingest the first data source by the end of the month; 3. a stand-alone computer for classified ontology data entry. Titan on the Red is an artificial intelligence/machine learning system to make digitizing, cataloging, and searching NSRC collections easier and more efficient. Created Online Vault backups, with one copy stored at LANL and one shipped to Lawrence Livermore National Laboratory. The Online Vault is a classified, searchable library of LANL’s nuclear weapons design and test history. Imported new Laboratory Directed Research and Development (LDRD) documents based on revised access categories. Bulk ingested nearly 10,000 documents via java-based PowerLoader. This application ingests metadata and content into the Online Vault to meet the requirements for the NSRC collections. Rehoused 300 linear feet of weapons physics documents in archival, acid-free storage folders and boxes.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2020

Where do $AMS-02$ anti-helium events come from?

We discuss the origin of the anti-helium-3 and -4 events possibly detected by AMS-02. Using up-to-date semi-analytical tools, we show that spallation from primary hydrogen and helium nuclei onto the ISM predicts a anti-helium-3 flux typically one to two orders of magnitude below the sensitivity of AMS-02 after 5 years, and a anti-helium-4 flux roughly 5 orders of magnitude below the AMS-02 sensitivity. We argue that dark matter annihilations face similar difficulties in explaining this event. We then entertain the possibility that these events originate from anti-matter-dominated regions in the form of anti-clouds or anti-stars. In the case of anti-clouds, we show how the isotopic ratio of anti-helium nuclei might suggest that BBN has happened in an inhomogeneous manner, resulting in anti-regions with a anti-baryon-to-photon ratio of one in a thousand of the baryon-to-photon ratio. We discuss properties of these regions, as well as relevant constraints on the presence of anti-clouds in our Galaxy. We present constraints from the survival of anti-clouds in the Milky-Way and in the early Universe, as well as from gamma-ray observations. In particular, these require the anti-clouds to be almost free of normal matter. We also discuss an alternative where anti-domains are dominated by surviving anti-stars. We suggest that part of the unindentified sources in the 3FGL catalog can originate from anti-clouds or anti-stars. AMS-02 and GAPS data could further probe this scenario.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Weak Gravitational Lensing of Low Surface Brightness Galaxies in the Dark Energy Year 3 Catalog

We present galaxy-galaxy lensing measurements of a sample of low surface brightness galaxies (LSBGs) drawn from the Dark Energy Survey Year 3 (Y3) data. LSBGs are diffuse galaxies with a surface brightness dimmer than the ambient night sky. Given their faintness, the use of standard observational techniques proves challenging. Weak gravitational lensing probes both the baryonic and dark matter content of galaxies, rendering it a powerful technique to estimate LSBG masses. The LSBG lens sample consists of 23, 790 total extended galaxies separated into red and blue color types at $g-i\ge 0.60$ and $g-i\le 0.60$, respectively. We use the Y3 \sc{metacalibration} shape catalog as the source sample, with a number density of $5.59 \mathrm{gal}/\mathrm{arcmin}^2$. We measure the tangential shear around the lens galaxies across angular scales of $0.25-400$ arcmin and find a signal-to-noise of 6.67 for red galaxies, 2.17 for blue galaxies, and 5.30 for the total sample. We fit a model built from two NFW profiles corresponding to the LSBG dark matter subhalo and host halo to the red LSBG shear measurements with an MCMC. We estimate the host halo mass at $7.3 ^{+2.0}_{-1.6}\times 10^{12} M_\mathrm{\odot}$. We place a 95\% upper bound on the subhalo mass at $3\times 10^{11} M_\mathrm{\odot}$. We utilize the lens sample photometry to obtain an estimate of the red LSBG stellar mass distribution. We compare the ratio between the stellar mass and the subhalo mass to the parameterized, satellite-specific SHMR. This work represents the first example of an attempted constraint on the masses of LSBGs using weak gravitational lensing.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Strong Lens Discoveries in DESI Legacy Imaging Surveys DR10 with Two Deep Learning Architectures

Abstract We have conducted a search for strong gravitational lensing systems in the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys Data Release 10 (DR10). This paper is the fourth in a series of searches. This is the first catalog of lens candidates covering nearly the entirety of the extragalactic sky south of declination δ ≈ +32 ∘ , all observed by DECam, covering ∼14,000 deg 2 . We impose a z -band magnitude cut of <20 in AB magnitude. We deploy a residual neural network and EfficientNet as an ensemble trained on a compilation of known lensing systems and high-grade candidates as well as nonlenses in the same footprint. The predictions from these two base models are aggregated using a meta-learner. After applying our ensemble to the survey data, we exclude known candidates and systems, and use our own visual inspection portal to rank images in the top 0.01 percentile of all neural network recommendations. We have found 811 lens candidates, five of which are confirmed through Euclid Quick Data Release (Q1). These include 484 new candidates in the Legacy Surveys DR9 footprint, all parts of which have been searched for strong lenses at least once before, either by our group or others. Combining the discoveries from this work with those from the first three papers in this series (335, 1210, and 1512), we have discovered a total of 3868 new candidates in the DESI Legacy Surveys.

Inchausti, Jose Carlos [University of San Francisc↗