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Spacepy, Open-source software for space science data analysis and modelling
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Vicente Pena Perez 08739 Extreme-Scale Data Science & Analytics, Sandia National Laboratories.
Abstract not provided.
DEVELOPMENT OF A MULTI-SENSOR DATA SCIENCE SYSTEM USED FOR SIGNATURE DEVELOPMENT ON SOLVENT EXTRACTION PROCESSES IN SUPPORT OF SAFEGUARDS – AN OVERVIEW (Presentation)
This presentation is for the paper INL/CON-22-65800
RAPIDS-2: a SciDAC Institute for Resource and Application Productivity through Computation, Information, and Data Science Final Report (University of Oregon)
Final report
Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences
Data science has primarily focused on big data, but for many physics, chemistry, and engineering applications, data are often small, correlated and, thus, low dimensional, and sourced from both computations and experiments with various levels of noise. Typical statistics and machine learning methods do not work for these cases. Expert knowledge is essential, but a systematic framework for incorporating it into physics-based models under uncertainty is lacking. Here, we develop a mathematical and computational framework for probabilistic artificial intelligence (AI)–based predictive modeling combining data, expert knowledge, multiscale models, and information theory through uncertainty quantification and probabilistic graphical models (PGMs). We apply PGMs to chemistry specifically and develop predictive guarantees for PGMs generally. Our proposed framework, combining AI and uncertainty quantification, provides explainable results leading to correctable and, eventually, trustworthy models. The proposed framework is demonstrated on a microkinetic model of the oxygen reduction reaction.
The Redmapper galaxy cluster catalog from DES science verification data
We describe updates to the redMaPPer algorithm, a photometric red-sequence cluster finder specifically designed for large photometric surveys. The updated algorithm is applied to 150 deg{sup 2} of Science Verification (SV) data from the Dark Energy Survey (DES), and to the Sloan Digital Sky Survey (SDSS) DR8 photometric data set. The DES SV catalog is locally volume limited and contains 786 clusters with richness λ>20 (roughly equivalent to M{sub 500c}≳10{sup 14} h{sub 70}{sup −1} M{sub ⊙}) and 0.2<z<0.9. The DR8 catalog consists of 26,311 clusters with 0.08<z<0.6, with a sharply increasing richness threshold as a function of redshift for z≳0.35. The photometric redshift performance of both catalogs is shown to be excellent, with photometric redshift uncertainties controlled at the σ{sub z}/(1+z)∼0.01 level for z≲0.7, rising to ∼0.02 at z∼0.9 in DES SV. We make use of Chandra and XMM X-ray and South Pole Telescope Sunyaev–Zeldovich data to show that the centering performance and mass–richness scatter are consistent with expectations based on prior runs of redMaPPer on SDSS data. We also show how the redMaPPer photo-z and richness estimates are relatively insensitive to imperfect star/galaxy separation and small-scale star masks.
Weak lensing by galaxy troughs in DES Science Verification data
Abstract Not Provided
COVID-19 Evidence Accelerator: A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients
Background: The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments. Methods: Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined. Results: Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events. Conclusion: Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.
A semi-agnostic ansatz with variable structure for variational quantum algorithms
Quantum machine learning—and specifically Variational Quantum Algorithms (VQAs)—offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, materials science, data science, and mathematics. Here, one trains an ansatz, in the form of a parameterized quantum circuit, to accomplish a task of interest. However, challenges have recently emerged suggesting that deep ansatzes are difficult to train, due to flat training landscapes caused by randomness or by hardware noise. This motivates our work, where we present a variable structure approach to build ansatzes for VQAs. Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization. Consequently, VAns is ideally suited to mitigate trainability and noise-related issues by keeping the ansatz shallow. We employ VAns in the variational quantum eigensolver for condensed matter and quantum chemistry applications, in the quantum autoencoder for data compression and in unitary compilation problems showing successful results in all cases.
Mass and galaxy distributions of four massive galaxy clusters from Dark Energy Survey Science Verification data
We measure the weak lensing masses and galaxy distributions of four massive galaxy clusters observed during the Science Verification phase of the Dark Energy Survey (DES). This pathfinder study is meant to (1) validate the Dark Energy Camera (DECam) imager for the task of measuring weak lensing shapes, and (2) utilize DECam's large field of view to map out the clusters and their environments over 90 arcmin. We conduct a series of rigorous tests on astrometry, photometry, image quality, point spread function (PSF) modelling, and shear measurement accuracy to single out flaws in the data and also to identify the optimal data processing steps and parameters. We find Science Verification data from DECam to be suitable for the lensing analysis described in this paper. The PSF is generally well behaved, but the modelling is rendered difficult by a flux-dependent PSF width and ellipticity. We employ photometric redshifts to distinguish between foreground and background galaxies, and a red-sequence cluster finder to provide cluster richness estimates and cluster-galaxy distributions. By fitting Navarro-Frenk-White profiles to the clusters in this study, we determine weak lensing masses that are in agreement with previous work. For Abell 3261, we provide the first estimates of redshift, weak lensing mass, and richness. In addition, the cluster-galaxy distributions indicate the presence of filamentary structures attached to 1E 0657-56 and RXC J2248.7-4431, stretching out as far as 1. (approximately 20 Mpc), showcasing the potential of DECam and DES for detailed studies of degree-scale features on the sky.
What is Data-Intensive Science?
What is Data Intensive Science? Today we are living in a digital world, where scientists often no longer interact directly with the physical object of their research, but do so via digitally captured, reduced, calibrated, analyzed, synthesized and, at times, visualized data. Advances in experimental and computational technologies have lead to an exponential growth in the volumes, variety and complexity of this data and while the deluge is not happening everywhere in an absolute sense, it is in a relative one. Science today is data intensive. Data intensive science has the potential to transform not only how we do science, but how quickly we can translate scientific progress into complete solutions, policies, decisions and ultimately economic success. Critically, data intensive science touches some of the most important challenges we are facing. Consider a few of the grand challenges outlined by the U.S. National Academy of Engineering: make solar energy economical, provide energy from fusion, develop carbon sequestration methods, advance health informatics, engineer better medicines, secure cyberspace, and engineer the tools of scientific discovery. Arguably, meeting any of these challenges requires the collaborative effort of trans-disciplinary teams, but also significant contributions from enabling data intensive technologies. Indeed for many of them, advances in data intensive research will be the single most important factor in developing successful and timely solutions. Simple extrapolations of how we currently interact with and utilize data and knowledge are not sufficient to meet this need. Given the importance of these challenges, a new, bold vision for the role of data in science, and indeed how research will be conducted in a data intensive environment is evolving.
Photometric redshift analysis in the Dark Energy Survey Science Verification data
In this study, we present results from a study of the photometric redshift performance of the Dark Energy Survey (DES), using the early data from a Science Verification period of observations in late 2012 and early 2013 that provided science-quality images for almost 200 sq. deg. at the nominal depth of the survey. We assess the photometric redshift (photo-z) performance using about 15 000 galaxies with spectroscopic redshifts available from other surveys. These galaxies are used, in different configurations, as a calibration sample, and photo-z's are obtained and studied using most of the existing photo-z codes. A weighting method in a multidimensional colour–magnitude space is applied to the spectroscopic sample in order to evaluate the photo-z performance with sets that mimic the full DES photometric sample, which is on average significantly deeper than the calibration sample due to the limited depth of spectroscopic surveys. In addition, empirical photo-z methods using, for instance, artificial neural networks or random forests, yield the best performance in the tests, achieving core photo-z resolutions σ68 ~ 0.08. Moreover, the results from most of the codes, including template-fitting methods, comfortably meet the DES requirements on photo-z performance, therefore, providing an excellent precedent for future DES data sets.
Combining Dark Energy Survey Science Verification data with near-infrared data from the ESO VISTA Hemisphere Survey
We present the combination of optical data from the Science Verification phase of the Dark Energy Survey (DES) with near-infrared (NIR) data from the European Southern Observatory VISTA Hemisphere Survey (VHS). The deep optical detections from DES are used to extract fluxes and associated errors from the shallower VHS data. Joint seven-band ( grizYJK ) photometric catalogues are produced in a single 3 sq-deg dedicated camera field centred at 02h26m-04d36m where the availability of ancillary multiwavelength photometry and spectroscopy allows us to test the data quality. Dual photometry increases the number of DES galaxies with measured VHS fluxes by a factor of ~4.5 relative to a simple catalogue level matching and results in a ~1.5 mag increase in the 80 per cent completeness limit of the NIR data. Almost 70 per cent of DES sources have useful NIR flux measurements in this initial catalogue. Photometric redshifts are estimated for a subset of galaxies with spectroscopic redshifts and initial results, although currently limited by small number statistics, indicate that the VHS data can help reduce the photometric redshift scatter at both z < 0.5 and z > 1. We present example DES+VHS colour selection criteria for high-redshift luminous red galaxies (LRGs) at z ~ 0.7 as well as luminous quasars. Using spectroscopic observations in this field we show that the additional VHS fluxes enable a cleaner selection of both populations with <10 per cent contamination from galactic stars in the case of spectroscopically confirmed quasars and <0.5 per cent contamination from galactic stars in the case of spectroscopically confirmed LRGs. The combined DES+VHS data set, which will eventually cover almost 5000 sq-deg, will therefore enable a range of new science and be ideally suited for target selection for future wide-field spectroscopic surveys.
redMaGiC: Selecting luminous red galaxies from the DES Science Verification data
Here, we introduce redMaGiC, an automated algorithm for selecting luminous red galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in photometric large-scale structure studies. redMaGiC achieves this by self-training the colour cuts necessary to produce a luminosity-thresholded LRG sample of constant comoving density. We demonstrate that redMaGiC photo-zs are very nearly as accurate as the best machine learning-based methods, yet they require minimal spectroscopic training, do not suffer from extrapolation biases, and are very nearly Gaussian. We apply our algorithm to Dark Energy Survey (DES) Science Verification (SV) data to produce a redMaGiC catalogue sampling the redshift range z ϵ [0.2, 0.8]. Our fiducial sample has a comoving space density of 10 –3 (h –1 Mpc) –3 , and a median photo-z bias (zspec – zphoto) and scatter (σz/(1 + z)) of 0.005 and 0.017, respectively. The corresponding 5σ outlier fraction is 1.4 per cent. We also test our algorithm with Sloan Digital Sky Survey Data Release 8 and Stripe 82 data, and discuss how spectroscopic training can be used to control photo-z biases at the 0.1 per cent level.
Balancing Open Science and Data Privacy in the Water Sciences
Open science practices such as publishing data and code are transforming water science by enabling synthesis and enhancing reproducibility. However, as research increasingly bridges the physical and social science domains (e.g., socio-hydrology), there is the potential for well-meaning researchers to unintentionally violate the privacy and security of individuals or communities by sharing sensitive information. Here we identify the contexts in which privacy violations are most likely to occur, such as working with high-resolution spatial data (e.g., from remote sensing), consumer data (e.g., from smart meters), and/or digital trace data (e.g., from social media). We also suggest practices for identifying and addressing privacy concerns at the individual, institutional, and disciplinary levels. We strongly advocate that the water science community continue moving toward open science and socio-environmental research and that progress toward these goals be rooted in open and ethical data management.
OBSERVATION AND CONFIRMATION OF SIX STRONG-LENSING SYSTEMS IN THE DARK ENERGY SURVEY SCIENCE VERIFICATION DATA
We report the observation and confirmation of the first group- and cluster-scale strong gravitational lensing systems found in Dark Energy Survey data. Through visual inspection of data from the Science Verification season, we identified 53 candidate systems. We then obtained spectroscopic follow-up of 21 candidates using the Gemini Multi-object Spectrograph at the Gemini South telescope and the Inamori-Magellan Areal Camera and Spectrograph at the Magellan/Baade telescope. With this follow-up, we confirmed six candidates as gravitational lenses: three of the systems are newly discovered, and the remaining three were previously known. Of the 21 observed candidates, the remaining 15 either were not detected in spectroscopic observations, were observed and did not exhibit continuum emission (or spectral features), or were ruled out as lensing systems. The confirmed sample consists of one group-scale and five galaxy-cluster-scale lenses. The lensed sources range in redshift z ∼ 0.80–3.2 and in i -band surface brightness i {sub SB} ∼ 23–25 mag arcsec{sup −2} (2″ aperture). For each of the six systems, we estimate the Einstein radius θ {sub E} and the enclosed mass M {sub enc}, which have ranges θ {sub E} ∼ 5″–9″ and M {sub enc} ∼ 8 × 10{sup 12} to 6 × 10{sup 13} M {sub ⊙}, respectively.
Weak Lensing Analysis of SPT selected Galaxy Clusters using Dark Energy Survey Science Verification Data
We present weak-lensing (WL) mass constraints for a sample of massive galaxy clusters detected by the South Pole Telescope (SPT) via the Sunyaev-Zel'dovich effect (SZE). We use griz imaging data obtained from the Science Verification (SV) phase of the Dark Energy Survey (DES) to fit the WL shear signal of 33 clusters in the redshift range 0.25 <= z <= 0.8 with NFW profiles and to constrain a four-parameter SPT mass-observable relation. To account for biases in WL masses, we introduce a WL mass to true mass scaling relation described by a mean bias and an intrinsic, lognormal scatter. We allow for correlated scatter within the WL and SZE mass-observable relations and use simulations to constrain priors on nuisance parameters related to bias and scatter from WL. We constrain the normalization of the zeta-M-500 relation, A(SZ) = 12.0(-6.7)(+2.6) when using a prior on the mass slope B-SZ from the latest SPT cluster cosmology analysis. Without this prior, we recover A(SZ) = 10.8(-5.2)(+2.3) and B-SZ = 1.30(-0.44)(+0.22). Results in both cases imply lower cluster masses than measured in previous work with and without WL, although the uncertainties are large. The WL derived value of B-SZ is approximate to 20 per cent lower than the value preferred by the most recent SPT cluster cosmology analysis. The method demonstrated in this work is designed to constrain cluster masses and cosmological parameters simultaneously and will form the basis for subsequent studies that employ the full SPT cluster sample together with the DES data.