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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The satellite population around luminous red galaxies in the 25 deg2 DESI Legacy Imaging Surveys Early Data Release

ABSTRACT Luminous red galaxies, or LRGs, are representative of the most massive galaxies and were originally selected in the Sloan Digital Sky Survey as good tracers of large-scale structure. They are dominated by by uniformly old stellar populations, have low star formation rates, early type morphologies, and little cold gas. Despite having old stellar populations and little in situ star formation, studies have shown that they have grown their stellar mass since z = 1, implying that they grow predominantly via the accretion of satellites. Tests of this picture have been limited because of the lack of deep imaging data sets that both covers a large enough area of the sky to contain substantial numbers of LRGs and that also is deep enough to detect faint satellites. We use the 25 deg2 Early Data Release (EDR) of the DESI Legacy Imaging Surveys to characterize the satellite galaxy population of LRGs out to z = 0.65. The DESI Legacy Imaging Surveys are comprised of grz imaging to 2–2.5 mag deeper than SDSS and with better image quality. We use a new statistical background technique to identify excess populations of putative satellite galaxies around 1823 LRGs at 0.2 < z < 0.65. In three redshift and luminosity bins we measure the numbers of satellite galaxies and their r−z colour distribution down to rest-frame g-band luminosity limits at least 3.6 times fainter than L*. In addition, we develop a forward modeling technique and apply it to constrain the mean number of satellites in each of our redshift and luminosity bins. Finally, we use these estimates to determine the amount of stellar mass growth in LRGs down to the local Universe.

Astronomy & Astrophysics↗
DOE OSTI2026

Gravitational Lenses in UNIONS and Euclid (GLUE). I. A Search for Strong Gravitational Lenses in UNIONS with Subaru, CFHT, and Pan-STARRS Data

We present the results of our pipeline for discovering strong gravitational lenses in the ongoing Ultraviolet Near-Infrared Optical Northern Survey (UNIONS). We successfully train a deep residual convolutional neural network based on CMU-Deeplens architecture, which is designed to detect strong lenses in ground-based imaging surveys. We train on images of real strong lenses and deploy on a sample of 8 million galaxies in areas with full coverage in the g, r, and i filters—the first multiband search for strong gravitational lenses in UNIONS. Following human inspection and grading, we report the discovery of a total of 1346 new strong-lens candidates, of which 146 are Grade A, 199 are Grade B, and 1001 are Grade C. Of these candidates, 283 have lens galaxy spectroscopic redshifts from the Sloan Digital Sky Survey, and an additional 297 have them from the Dark Energy Spectroscopic Instrument Data Release 1. We find 15 of these systems display evidence of both lens and source galaxy redshifts in spectral superposition. We also report the spectroscopic confirmation of seven lensed sources in high-quality systems, all with z > 2.1, using the Keck Near Infrared Echellette Spectrograph and the Gemini Near-Infrared Spectrograph.

Storfer, Christopher J. [University of Hawaii, Hon↗
DOE OSTI2025

Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty

Inverse problems, which aim to infer unknown properties of a system using experimental and observational data, are central to addressing many of the U.S. Department of Energy’s (DOE) most critical scientific and engineering challenges. Accurate, computationally efficient, and data-efficient solutions to inverse problems are essential for advancing DOE mission-critical science drivers, including analyzing data from large-scale experimental facilities, optimizing fusion reactor performance, accelerating materials discovery, enhancing geophysical imaging, improving wildfire predictions, and enabling autonomous systems and digital twins. However, these problems are becoming increasingly complex, often involving nonlinear, highdimensional, and interconnected systems and models that span multiple physics and scales, while relying on data with varying quantity, quality, and information content. Compounding these challenges is the uncertainty inherent in DOE-relevant systems, where errors in inputs, noise in data, incompleteness of data, and discrepancies between models and reality constrain the accuracy and precision of solutions. At the same time, the convergence of recent scientific computing trends—scientific machine learning, artificial intelligence, and computing advances such as exascale computing—is creating unprecedented opportunities for tackling these challenges. The cross-cutting nature of inverse problems, combined with their growing complexity and rapidly evolving data and algorithmic demands, strongly motivates the formulation of a prioritized research agenda to maximize their capabilities and impact. In response to this need, DOE’s Advanced Scientific Computing Research (ASCR) program in the Office of Science convened the Workshop on Basic Research Needs for Inverse Problems for Complex Systems Under Uncertainty in June 2025. This workshop brought together experts across disciplines to identify grand challenges and major opportunities in the field. Through collaborative discussions, the workshop defined transformative research directions aimed at addressing the mathematical, statistical, and computational challenges posed by inverse problems under uncertainty. As a result of these efforts, four priority research directions (PRDs) were identified to guide future research and development in this area. These PRDs, summarized below, represent a roadmap for advancing the foundational science and mathematics of inverse problems, enabling robust, scalable, and uncertainty-aware solutions that are critical for DOE applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

All optical half subtractor using photonic crystal based nonlinear ring resonators

All optical subtractors are one of the optical digital devices that will play crucial roles in the next generation all optical data processing. In this paper we used three nonlinear photonic crystal based ring resonators for designing a novel structure for realizing all optical half subtractor. In the proposed structure X and Y are the input ports and D and B are the output ports. D is on when one of the input ports is ON and the other is OFF, however B is ON only when X is ON and Y is OFF. The maximum rise times for D and B are 2 ps and 3 ps respectively. Also the maximum fall times for D and B both are about 1 ps.

42 ENGINEERING↗
DOE OSTI2019

A new method of relative radiometric calibration for hyperspectral imaging based on skylight monitor

A new method of relative radiometric calibration based on the skylight monitor is presented for airborne hyperspectral remote sensing to eliminate the negative effect on data resulting from the time-varying skylight during a long flight. In terms of large area operation of mapping or spectral imaging, a static radiation model hardly reveals actual cases as external environment keeps changing. Compared with common considerable factors, fluctuation of skylight is the primary one that may observably affect spectral imaging continuously leading to radiation changes upon flight lines. To get rid of the influence of skylight on spectral imagery and monitoring the variation during the flight, we assemble a set of skylight monitor fixed to the aircraft cabin for the sake of recording the semi-spherical skylight keeping synchronization with hyperspectral imaging system and the position and orientation system. The skylight monitor consists of a cosine corrector on the top receiving the skylight, a stable optical fiber and a visible and near-infrared linear array modular spectrometer. The corresponding matched data of skylight with each frame of images is used to correct related digital number after stripe non-uniformity correction, achieving mean relative standard deviation (coefficient of variation or CV) of under 6% for all 256 bands. The final result shows a satisfactory calibrated image of an appropriate evaluation criteria with contrast of the original image.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

A mathematical discussion concerning the performance of multilayer perceptron-type artificial neural networks through use of orthogonal bipolar vectors

This work proposes the use of orthogonal bipolar vectors (OBV) as targets for multilayer perceptron (MLP) Artificial neural networks pertaining to the pattern recognition field. Implications involving this proposal are supported by a proper mathematical discussion. This study shows that the use of OBV as targets improves the class identification performance of MLP networks for pattern recognition with more than two classes when compared to the widely used conventional target bipolar vector (CBV). In addition to the overall performance improvement, it is possible to obtain high recognition rates with fewer training epochs, while keeping a feasible generalization with other data. This behavior is related to the Euclidean distance of the points in the output space, which is much higher in the case of OBVs. Therefore, with a greater distance between the points in the output space, the MLP network becomes less prone to associate trained outputs with incorrect targets. The mathematical discussion in this study is based on the backpropagation training algorithm. In addition to the mathematical discussion, results from experiments with real data are presented. These results follow those obtained by mathematical derivations. Real data sets used in the experiments are available from: (a) the Semeion Handwritten Digit of Machine Learning Repository, international repository; (b) Iris Image Database from the Chinese Academy of Sciences—CASIA; (c) Australian Sign Language, signs of Machine Learning Repository, international repository. Experiments were conducted to measure the mean Euclidean distance between the MLP network output subjected to a given input pattern and outputs related to other pattern classifications. Experimental results show that MLP networks trained with OBVs were able to ensure a Euclidean distance between the output and incorrect targets greater than MLP networks trained with CBVs (by up to 4 times for handwritten, by up to 12 times for iris images and by up to 10 times for Auslan signs language). As such, the adoption of OBVs as target vectors for MLPs reduces the computational effort for training the network.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A linked-scale coupled model of mass erosion and redistribution in plasma-exposed micro-foam surfaces

Surface evolution due to exposure to harsh environments is of importance in many scientific and technological applications. In plasma-exposed materials, the surface receives charged particles from the plasma, leading to a series of processes that drive the system far from equilibrium and may lead to the severe deterioration of the surface properties. Although surface morphological changes are driven by atomic collisions taking place over picoseconds and nanometers, these processes result in mass loss and redistribution of matter over much larger length and time scales. This necessitates a multi-scale approach capable of capturing the range of processes linking primary atomic collision events with engineering-level surface geometry changes. In this paper, we develop a computational model to simulate the morphological evolution and effective erosion rate of micro-architected tungsten foams during low-energy plasma ion bombardment. Furthermore, the model acts on several length scales, with the energy and angular dependence of the sputtering yield of flat tungsten surfaces determined using the SRIM code based on the binary collision approximation. This information is introduced into a low-fluence, short-term Monte Carlo raytracing model, and further into a high-fluence, long-term particle transport model. In the latter, material particles representing billions of atoms are described in a digitized 3-D representation of the foam structure from X-ray tomography data, and are sputtered off and redeposited using the atomistic information. We show that the redeposition of sputtered atoms leads to partial self-healing in the bottom layers with a sharp reduction in the sputtering coefficient of low density foams, roughly 25% of the solid W value. This is in qualitative agreement with recent experiments on low porosity W structures. At high fluence, the foam structure degrades considerably as there are fewer ligaments available to recapture sputtered atoms and, consequently, the sputtering rate increases again.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling as Catalysts for Next-Generation Breakthroughs

The Presidential Symposium (PRES) at the 2025 Fall Meeting, hosted by the President’s Office and Energy and Fuels Division, American Chemical Society (ACS) in Washington, DC, brought together a diverse group of chemists, engineers, and materials scientists working in battery materials & systems, automation and artificial intelligence from academia, industry, and national laboratories. The accelerating demand for high-performance, scalable, and sustainable energy storage has catalyzed a paradigm shift in how materials are dis-covered, devices are engineered, and systems are optimized. This Presidential Symposium, entitled “Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling Driving Next-Gen Breakthroughs”, brings together global leaders to unveil transformative strategies anchored in the AAA framework: Artificial Intelligence, Automation, and Advanced Modeling. Artificial Intelligence is redefining the frontiers of energy storage by enabling predictive design, real-time optimization, and intelligent control across diverse chemistries and architectures. Automation is streamlining the synthesis, characterization, and testing of battery materials, dramatically accelerating innovation cycles and unlocking scalable solutions for grid and mobility applications. Advanced Modeling, spanning atomic to system-level scales, provides unprecedented insight into electrochemical dynamics, degradation pathways, and thermal behavior, particularly when coupled with physics-informed machine learning and digital twin technologies. Digital twins, in turn, leverage the AAA framework by integrating real-time data, physics-based models, and AI predictions into dynamic virtual replicas, enabling proactive diagnostics, optimization, and system resilience. Together, these synergistic pillars are not only re-shaping the scientific landscape but also forging a new era of reproducible, data-driven, and resilient energy storage innovation. In conclusion, this symposium marks a pivotal moment in the convergence of computational intelligence and experimental rigor, charting the course for next-generation breakthroughs in lithium-ion, solid-state, and flow battery technologies.

Artificial Intelligence (AI)↗
DOE OSTI2026

Energy-efficient scientific computing using chemical reservoirs

The rapid growth of computing demands driven by scientific computing, data analytics, and artificial intelligence (AI) advancements has exposed the limitations of traditional digital processing systems. These systems are nearing physical energy barriers, making significant gains in energy efficiency increasingly unattainable. As we advance toward post-exascale computing, disruptive approaches are critical to overcoming these limitations. Among emerging analog solutions, biochemical computing offers a transformative path for achieving orders-of-magnitude improvements in energy efficiency. By leveraging the natural optimization capabilities of chemical reaction networks (CRNs), biochemical systems have the potential to meet high-performance computing needs through natural scalability. However, numerous challenges remain, including theoretical limitations in mapping computational problems to CRNs and practical barriers in implementing biochemical computing devices. In this paper, we present a framework for chemical computation using biochemical systems and introduce key components of our approach for energy-efficient scientific computing. We showcase the feasibility of this framework by solving a system of ordinary differential equations by emulating a chemical reservoir device, demonstrating its potential for addressing modern computing challenges. This work lays a foundational step toward harnessing the computational power of chemistry to design energy-efficient, scalable, high-performance next-generation computing systems.

Johnson, Connah G. M. [Pacific Northwest National ↗
DOE OSTI2016

Isotope ratio characteristics and sensitivity for uranium determinations using a liquid sampling-atmospheric pressure glow discharge ion source coupled to an Orbitrap mass analyzer

Abstract The continued development of the liquid sampling-atmospheric pressure glow discharge (LS-APGD) microplasma as an ion source for diverse, elemental/isotopic analysis applications continues. To this end, characterization of the capabilities in performing precise and accurate isotope ratio (IR) determinations is essential. Based on past experience with the Thermo Exactive Orbitrap mass analyzer, the LS-APGD was interfaced with this instrument for these tests. While the Orbitrap platform has demonstrated excellent mass resolution and accuracy in “organic” mass spectrometry (MS) applications, work using an Orbitrap for IR analysis is very sparse. These efforts build off previous work in this coupling, where the importance of a few of the LS-APGD discharge parameters and Orbitrap data acquisition methods on IR precision and accuracy were probed. Presented here are the results of a study that evaluated the analytical precision for natural uranium sample (assumed 235U/238U = 0.0072) determinations. The instrumental parameters evaluated include the number of microscans and scans making up a data acquisition set, uranium concentration/signal level, sample make-up, and Fourier transform digitization window. Ultimately, a precision of 0.41% relative standard deviation (RSD) can be achieved for a single determination, with a reproducibility of 1.63 %RSD over 10 separate analytical measurements. A preliminary study of matrix effects on IR measurements of U is presented, highlighting the importance of pre-mass selection before injection into the Orbitrap. The analytical system sensitivity is suggested with the ability to produce a calibration function having an R2 value of >0.99 over a range of 4 orders of magnitude of concentration (~1 – 1000 ng mL-1). These efforts demonstrate the very promising pairing of the LS-APGD ionization source and the Orbitrap, pointing as well to definitive paths forward to better utilize both components in high quality isotope ratio mass spectrometry (IRMS).

Environmental Molecular Sciences Laboratory↗
DOE OSTI2018

Upgrading Limiting Peak-Power Analysis Techniques with Modern Validation and Uncertainty Quantification for the Advanced Test Reactor

Here, this work demonstrates the acceptability of the 2D deterministic transport code, HELIOS, to replace the legacy diffusion code, PDQ, for computing the peak-power performance parameters of the Advanced Test Reactor (ATR). The 95% Confidence Rule, commonly used in the commercial reactor sector, is explored to develop the so-called “reliability factors” which provide statistical confidence that the peak-power limits within the hottest location along a fuel plate, referred to as the hot-stripe, will not be exceeded. Additionally, an alternative “legacy” methodology was explored that attempts to mimic the exact PDQ analysis process used for defining the peak-power limits. The legacy methodology, involves interpolating power between regions at azimuthal boundaries subtending the regions of interest. In order to apply the 95% Confidence Rule, a statistically significant calculation-to-measurement bias must first be established. Unlike the commercial world where thousands of power observations can be collected every cycle using on-line flux mapping instrumentation, the ATR power distribution must be measured during “depressurized” zero-power measurements using fission wires in polyethylene wands. In 2012, fission wire activation data was collected during a flux run in the Advanced Test Reactor – Critical facility. Also to improve statistical validity, archival data from ATR zero power flux runs from 1977, 1986, and 1994 were digitized from scanned reports and used to create new benchmark models. Borrowing from least-squares adjustment methods commonly used for neutron activation spectroscopy, adjusted fission wire powers were calculated for all four datasets. The mean and standard deviation of the bias between a priori calculated and adjusted wire-powers was then taken as the bias and uncertainty used in the 95% Confidence Rule.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2013

A COMPARATIVE STUDY OF OPTICAL/ULTRAVIOLET VARIABILITY OF NARROW-LINE SEYFERT 1 AND BROAD-LINE SEYFERT 1 ACTIVE GALACTIC NUCLEI

The ensemble optical/ultraviolet (UV) variability of narrow-line Seyfert 1 (NLS1)-type active galactic nuclei (AGNs) is investigated, based on a sample selected from the Sloan Digital Sky Survey (SDSS) Stripe 82 region with multi-epoch photometric scanning data. As a comparison, a control sample of broad-line Seyfert 1 (BLS1)-type AGNs is also incorporated. To quantify properly the intrinsic variation amplitudes and their uncertainties, a novel method of parametric maximum likelihood is introduced that has, as we argued, certain virtues over previously used methods. The majority of NLS1-type AGNs exhibit significant variability on timescales from about 10 days to a few years with, however, smaller amplitudes on average compared to BLS1-type AGNs. About 20 NLS1-type AGNs that show relatively large variations are presented and may deserve future monitoring observations, for instance, reverberation mapping. The averaged structure functions of variability, constructed using the same maximum likelihood method, show remarkable similarity in shape for the two types of AGNs on timescales longer than about 10 days, which can be approximated by a power law or an exponential function. This, along with other similar properties, such as the wavelength-dependent variability, is indicative of a common dominant mechanism responsible for the long-term optical/UV variability of both NLS1- and BLS1-type AGNs. Toward the short timescales, however, there is tentative evidence that the structure function of NLS1-type AGNs continues to decline, whereas that of BLS1-type AGNs flattens with some residual variability on timescales of days. If this can be confirmed, it may suggest that an alternative mechanism, such as X-ray reprocessing, starts to dominate in BLS1-type AGNs, but not in NLS1-type AGNs, on such timescales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

THE VERTICAL MOTIONS OF MONO-ABUNDANCE SUB-POPULATIONS IN THE MILKY WAY DISK

We present the vertical kinematics of stars in the Milky Way's stellar disk inferred from Sloan Digital Sky Survey/Sloan Extension for Galactic Understanding and Exploration (SDSS/SEGUE) G-dwarf data, deriving the vertical velocity dispersion, {sigma}{sub z}, as a function of vertical height |z| and Galactocentric radius R for a set of 'mono-abundance' sub-populations of stars with very similar elemental abundances [{alpha}/Fe] and [Fe/H]. We find that all mono-abundance components exhibit nearly isothermal kinematics in |z|, and a slow outward decrease of the vertical velocity dispersion: {sigma}{sub z}(z, R | [{alpha}/Fe], [Fe/H]) Almost-Equal-To {sigma}{sub z}([{alpha}/Fe], [Fe/H]) Multiplication-Sign exp (- (R - R{sub 0})/7 kpc). The characteristic velocity dispersions of these components vary from {approx}15 km s{sup -1} for chemically young, metal-rich stars with solar [{alpha}/Fe], to {approx}> 50 km s{sup -1} for metal-poor stars that are strongly [{alpha}/Fe]-enhanced, and hence presumably very old. The mean {sigma}{sub z} gradient (d{sigma}{sub z}/dz) away from the mid-plane is only 0.3 {+-} 0.2 km s{sup -1} kpc{sup -1}. This kinematic simplicity of the mono-abundance components mirrors their geometric simplicity; we have recently found their density distribution to be simple exponentials in both the z- and R-directions. We find a continuum of vertical kinetic temperatures ({proportional_to}{sigma}{sup 2}{sub z}) as a function of ([{alpha}/Fe], [Fe/H]), which contribute to the total stellar surface-mass density approximately as {Sigma}{sub R{sub 0}}({sigma}{sup 2}{sub z}){proportional_to} exp(-{sigma}{sup 2}{sub z}). This and the existence of isothermal mono-abundance populations with intermediate dispersions (30-40 km s{sup -1}) reject the notion of a thin-thick-disk dichotomy. This continuum of disk components, ranging from old, 'hot', and centrally concentrated ones to younger, cooler, and radially extended ones, argues against models where the thicker disk portions arise from massive satellite infall or heating; scenarios where either the oldest disk portion was born hot, or where internal evolution plays a major role, seem the most viable. In addition, the wide range of {sigma}{sub z}([{alpha}/Fe], [Fe/H]) combined with a constant {sigma}{sub z}(z) for each abundance bin provides an independent check on the precision of the SEGUE-derived abundances: {delta}{sub [{alpha}/Fe]} Almost-Equal-To 0.07 dex and {delta}{sub [Fe/H]} Almost-Equal-To 0.15 dex. The slow radial decline of the vertical dispersion presumably reflects the decrease in disk surface-mass density. This measurement constitutes a first step toward a purely dynamical estimate of the mass profile of the stellar and gaseous disk in our Galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Alcock-paczynski cosmological test

In order to test the expansion of the universe and its geometry, we carry out an Alcock-Paczyński cosmological test, that is, an evaluation of the ratio of observed angular size to radial/redshift size. The main advantage of this test is that it does not depend on the evolution of the galaxies but only on the geometry of the universe. However, the redshift distortions produced by the peculiar velocities of the gravitational infall also have an influence, which should be separated from the cosmological effect. We derive the anisotropic correlation function of sources in three surveys within the Sloan Digital Sky Survey (SDSS): galaxies from SDSS-III/Baryon Oscillation Spectroscopic Survey Data Release 10 (BOSS-DR10) and QSOs from SDSS-II and SDSS-III/BOSS-DR10. From these, we are able to disentangle the dynamic and geometric distortions and thus derive the ratio of observed angular size to radial/redshift size at different redshifts. We also add some other values available in the literature. Then we use the data to evaluate which cosmological model fits them. We used six different models: concordance ΛCDM, Einstein-de Sitter, open-Friedman cosmology without dark energy, flat quasi-steady state cosmology, a static universe with a linear Hubble law, and a static universe with tired-light redshift. Only two of the six models above fit the data of the Alcock-Paczyński test: concordance ΛCDM and static universe with tired-light redshift, whereas the rest of them are excluded at a >95% confidence level. If we assume that ΛCDM is the correct one, the best fit with a free Ω {sub m} is produced for Ω{sub m}=0.24{sub −0.07}{sup +0.10}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

STELLAR LOCI II. A MODEL-FREE ESTIMATE OF THE BINARY FRACTION FOR FIELD FGK STARS

We propose a stellar locus outlier (SLOT) method to determine the binary fraction of main-sequence stars statistically. The method is sensitive to neither the period nor mass ratio distributions of binaries and is able to provide model-free estimates of binary fraction for large numbers of stars of different populations in large survey volumes. We have applied the SLOT method to two samples of stars from the Sloan Digital Sky Survey (SDSS) Stripe 82, constructed by combining the recalibrated SDSS photometric data with the spectroscopic information from the SDSS and LAMOST surveys. For the SDSS spectroscopic sample, we find an average binary fraction for field FGK stars of 41% ± 2%. The fractions decrease toward late spectral types and are 44% ± 5%, 43% ± 3%, 35% ± 5%, and 28% ± 6% for stars with g – i colors in the range 0.3-0.6 mag, 0.6-0.9 mag, 0.9-1.2 mag, and 1.2-1.6 mag, respectively. A modest metallicity dependence is also found. The fraction decreases with increasing metallicity. For stars with [Fe/H] between –0.5 and 0.0 dex, –1.0 and –0.5 dex, –1.5 and –1.0 dex, and –2.0 and –1.5 dex, the inferred binary fractions are 37% ± 3%, 39% ± 3%, 50% ± 9%, and 53% ± 20%, respectively. We have further divided the sample into stars from the thin disk, the thick disk, the transition zone between them, and the halo. The results suggest that the Galactic thin and thick disks have comparable binary fractions, whereas the Galactic halo contains a significantly larger fraction of binaries. Applying the method to the LAMOST spectroscopic sample yields consistent results. Finally, other potential applications and future work with the method are discussed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Characterizing unknown systematics in large scale structure surveys

Photometric large scale structure (LSS) surveys probe the largest volumes in the Universe, but are inevitably limited by systematic uncertainties. Imperfect photometric calibration leads to biases in our measurements of the density fields of LSS tracers such as galaxies and quasars, and as a result in cosmological parameter estimation. Earlier studies have proposed using cross-correlations between different redshift slices or cross-correlations between different surveys to reduce the effects of such systematics. In this paper we develop a method to characterize unknown systematics. We demonstrate that while we do not have sufficient information to correct for unknown systematics in the data, we can obtain an estimate of their magnitude. We define a parameter to estimate contamination from unknown systematics using cross-correlations between different redshift slices and propose discarding bins in the angular power spectrum that lie outside a certain contamination tolerance level. We show that this method improves estimates of the bias using simulated data and further apply it to photometric luminous red galaxies in the Sloan Digital Sky Survey as a case study.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

How does the radio enhancement of broad absorption line quasars relate to colour and accretion rate?

ABSTRACT The origin of radio emission in different populations of radio-quiet quasars is relatively unknown, but recent work has uncovered various drivers of increased radio-detection fraction. In this work, we pull together three known factors: optical colour (g − i), C iv distance (a proxy for L/LEdd), and whether or not the quasar contains broad absorption lines (BALQSOs) which signify an outflow. We use SDSS (Sloan Digital Sky Survey) DR14 spectra along with the LOFAR Two Metre Sky Survey Data Release 2 and find that each of these properties have an independent effect. BALQSOs are marginally more likely to be radio-detected than non-BALQSOs at similar colours and L/LEdd, moderate reddening significantly increases the radio-detection fraction and the radio detection increases with L/LEdd above a threshold for all populations. We test a widely used simple model for radio wind shock emission and calculate energetic efficiencies that would be required to reproduce the observed radio properties. We discuss interpretations of these results concerning radio-quiet quasars more generally. We suggest that radio emission in BALQSOs is connected to a different physical origin than the general quasar population since they show different radio properties independent of colour and C iv distance.

Petley, James W. (ORCID:0000000244960754)↗
DOE OSTI2016

SU-F-J-180: A Reference Data Set for Testing Two Dimension Registration Algorithms

Purpose: To create and characterize a reference data set for testing image registration algorithms that transform portal image (PI) to digitally reconstructed radiograph (DRR). Methods: Anterior-posterior (AP) and Lateral (LAT) projection and DRR image pairs from nine cases representing four different anatomical sites (head and neck, thoracic, abdominal, and pelvis) were selected for this study. Five experts will perform manual registration by placing landmarks points (LMPs) on the DRR and finding their corresponding points on the PI using computer assisted manual point selection tool (CAMPST), a custom-made MATLAB software tool developed in house. The landmark selection process will be repeated on both the PI and the DRR in order to characterize inter- and -intra observer variations associated with the point selection process. Inter and an intra observer variation in LMPs was done using Bland-Altman (B&A) analysis and one-way analysis of variance. We set our limit such that the absolute value of the mean difference between the readings should not exceed 3mm. Later on in this project we will test different two dimension (2D) image registration algorithms and quantify the uncertainty associated with their registration. Results: Using one-way analysis of variance (ANOVA) there was no variations within the readers. When Bland-Altman analysis was used the variation within the readers was acceptable. The variation was higher in the PI compared to the DRR.ConclusionThe variation seen for the PI is because although the PI has a much better spatial resolution the poor resolution on the DRR makes it difficult to locate the actual corresponding anatomical feature on the PI. We hope this becomes more evident when all the readers complete the point selection. The reason for quantifying inter- and -intra observer variation tells us to what degree of accuracy a manual registration can be done. Research supported by William Beaumont Hospital Research Start Up Fund.

60 APPLIED LIFE SCIENCES↗