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At least 739 records · Page 41
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NASA NTRS2000

Sunyaev-Zeldovich Effect-Derived Distances to the High-Redshift Clusters

We determine the distances to the z approximately equals 0.55 galaxy clusters MS 0451.6 - 0305 and Cl 0016 + 16 from a maximum-likelihood joint fit to interferometric Sunyaev-Zeldovich effect (SZE) and X-ray observations. We model the intracluster medium (ICM) using a spherical isothermal beta model. We quantify the statistical and systematic uncertainties inherent to these direct distance measurements, and we determine constraints on the Hubble parameter for three different cosmologies. For an Omega(sub M) = 0.3, Omega(sub lambda) = 0.7 cosmology, these distances imply a Hubble constant of 63(sup +12) (sub -9) (sup + 21) (sub -21) km/s Mp/c, where the uncertainties correspond to statistical followed by systematic at 68% confidence. The best-fit H(sub 0) is 57 km/s Mp/c for an open (Omega(sub M) = 0.3) universe and 52 km/s Mp/c for a flat (Omega(sub M) = 1) universe.

Reese, Erik D.↗
DOE OSTI2021

Clustering of CODEX clusters

The clustering of galaxy clusters links the spatial nonuniformity of dark matter halos to the growth of the primordial spectrum of perturbations. The amplitude of the clustering signal is widely used to estimate the halo mass of astrophysical objects. The advent of cluster mass calibrations enables using clustering in cosmological studies. Aims. We analyze the autocorrelation function of a large contiguous sample of galaxy clusters, the Constrain Dark Energy with X-ray (CODEX) sample, in which we take particular care of cluster definition. These clusters were X-ray selected using the ROentgen SATellite All-Sky Survey and then identified as galaxy clusters using the code redMaPPer run on the photometry of the Sloan Digital Sky Survey. We develop methods for precisely accounting for the sample selection effects on the clustering and demonstrate their robustness using numerical simulations. Methods. Using the clean CODEX sample, which was obtained by applying a redshift-dependent richness selection, we computed the two-point autocorrelation function of galaxy clusters in the 0.1 < z < 0.3 and 0.3 < z < 0.5 redshift bins. We compared the bias in the measured correlation function with values obtained in numerical simulations using a similar cluster mass range. Results. By fitting a power law, we measured a correlation length r 0 = 18.7 ± 1.1 and slope γ = 1.98 ± 0.14 for the correlation function in the full redshift range. By fixing the other cosmological parameters to their nine-year Wilkinson Microwave Anisotropy Probe values, we reproduced the observed shape of the correlation function under the following cosmological conditions: Ω m0 = $0.22_{-0.03}^{+0.04}$ and S 8 = σ 8 (Ω m0 /0.3)0.5 = $0.85_{-0.08}^{+0.10}$ with estimated additional systematic errors of σ Ω m0 = 0.02 and σS 8 = 0.20. We illustrate the complementarity of clustering constraints by combining them with CODEX cosmological constraints based on the X-ray luminosity function, deriving Ωm = 0.25 ± 0.01 and σ 8 = $0.81_{-0.02}^{+0.01}$ with an estimated additional systematic error of σ Ω m0 = 0.07 and σ σ8 = 0.04. The mass calibration and statistical quality of the mass tracers are the dominant source of uncertainty.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The ASAS-SN bright supernova catalogue – V. 2018–2020

We catalogue the 443 bright supernovae (SNe) discovered by the All-Sky Automated Survey for Supernovae (ASAS-SN) in 2018−2020 along with the 519 SNe recovered by ASAS-SN and 516 additional m peak ≤ 18 mag SNe missed by ASAS-SN. Our statistical analysis focuses primarily on the 984 SNe discovered or recovered in ASAS-SN g-band observations. The complete sample of 2427 ASAS-SN SNe includes earlier V-band samples and unrecovered SNe. For each SN, we identify the host galaxy, its UV to mid-IR photometry, and the SN’s offset from the centre of the host. Updated peak magnitudes, redshifts, spectral classifications, and host galaxy identifications supersede earlier results. With the increase of the limiting magnitude to g ≤ 18 mag, the ASAS-SN sample is nearly complete up to m peak = 16.7 mag and is 90 per cent complete for m peak ≤ 17.0 mag. This is an increase from the V-band sample, where it was roughly complete up to m peak = 16.2 mag and 70 per cent complete for m peak ≤ 17.0 mag.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Modeling iterative reconstruction and displacement field in the large scale structure

The next generation of galaxy surveys like the Dark Energy Spectroscopic Instrument and Euclid will provide datasets orders of magnitude larger than anything available to date. Our ability to model nonlinear effects in late time matter perturbations will be a key to unlock the full potential of these datasets, and the area of initial condition reconstruction is attracting growing attention. Iterative reconstruction developed by Marcel Schmittfull et al. is a technique designed to reconstruct the displacement field from the observed galaxy distribution. The nonlinear displacement field and initial linear density field are highly correlated. Therefore, reconstructing the nonlinear displacement field enables us to extract the primordial cosmological information better than from the late time density field at the level of the two-point statistics. This paper will test to what extent the iterative reconstruction can recover the true displacement field and construct a perturbation theory model for the postreconstructed field. We model the iterative reconstruction process with Lagrangian perturbation theory (LPT) up to third order for dark matter in real space and compare it with N-body simulations. Here we find that the simulated iterative reconstruction does not converge to the nonlinear displacement field, and the discrepancy mainly appears in the shift term, i.e., the term correlated directly with the linear density field. On the contrary, our 3LPT model predicts that the iterative reconstruction should converge to the nonlinear displacement field. We discuss the sources of discrepancy, including numerical noise/artifacts on small scales, and present an ad hoc phenomenological model that improves the agreement.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

A Cross-correlation Study of High-energy Neutrinos and Tracers of Large-scale Structure

The origin of the bulk of the astrophysical neutrinos detected by the IceCube Observatory remains a mystery. Previous source-finding analyses compared the directions of IceCube events and individual sources in astrophysical catalogs. The source association method is technically challenging when the number of source candidates is much larger than the number of the observed astrophysical neutrinos. In this paper, we show that in this large source number regime, a two-point cross-correlation analysis of neutrino data and source catalog can instead be used to constrain potential source populations for the high-energy astrophysical neutrinos, and provide spatial evidence for the existence of astrophysical neutrinos. We present an analysis of the cross-correlation of the IceCube 2010–2012 point-source data and a WISE–2MASS galaxy sample. While we find no significant detection of cross-correlation with the publicly available neutrino data set, we show that, when applied to the full IceCube data, which has a longer observation time and higher astrophysical neutrino purity, our method has sufficient statistical power to detect a cross-correlation signal if the neutrino sources trace the large-scale structure of the universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Inpainting Galactic Foreground Intensity and Polarization Maps Using Convolutional Neural Networks

The Deep Convolutional Neural Networks (DCNNs) have been a popular tool for image generation and restoration. In this work, we applied DCNNs to the problem of inpainting non-Gaussian astrophysical signal, in the context of Galactic diffuse emissions at the millimetric and submillimetric regimes, specifically Synchrotron and Thermal Dust emissions. Both signals are affected by contamination at small angular scales due to extragalactic radio sources (the former) and dusty star-forming galaxies (the latter). Furthermore, we compare the performance of the standard diffusive inpainting with that of two novel methodologies relying on DCNNs, namely Generative Adversarial Networks and Deep-Prior. We show that the methods based on the DCNNs are able to reproduce the statistical properties of the ground-truth signal more consistently with a higher confidence level.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1997

Multiwavelength and Statistical Research in Space Astrophysics

The accomplishments in the following three research areas are summarized: multiwavelength study of active galactic nuclei; magnetic activity of young stellar objects; and statistical methodology for astronomical data analysis. The research is largely based on observations of the ROSAT and ASCA X-ray observatories, complemented by ground-based optical and radio studies. Major findings include: discovery of inverse Compton X-ray emission from radio galaxy lobes; creation of the largest and least biased available sample of BL Lac objects; characterization of X-ray and nonthermal radio emission from T Tauri stars; obtaining an improved census of young stars in a star forming region and modeling the star formation history and kinematics; discovery of X-ray emission from protostars; development of linear regression methods and codes for interpreting astronomical data; and organization of the first cross-disciplinary conferences for astronomers and statisticians.

Feigelson, Eric D.↗
NASA NTRS2000

Sunyaev-Zel'dovich Effect Derived Distance to the High Redshift Clusters MS 0451.6-0305 and CL 0016+16

We determine the distances to the z approximately equal to 0.55 galaxy clusters MS 0451.6-0305 and CL 0016+16 from a maximum likelihood joint fit to interferometric Sunyaev-Zel'dovich effect (SZE) and X-ray observations. We model the intracluster medium (ICM) using a spherical isothermal beta-model. We quantify the statistical and systematic uncertainties inherent to these direct distance measurements, and we determine constraints on the Hubble parameter for three different cosmologies. For an OmegaM = 0.3, OmegaL = 0.7 cosmology, these distances imply a Hubble constant of 63(exp 12)(sub -9)(exp +21)(sub -21) km/s/Mpc, where the uncertainties correspond to statistical followed by systematic at 68% confidence. The best fit H(sub o) is 57 km/sec/Mpc for an open OmegaM = 0.3 universe and 52 km/s/Mpc for a flat Omega = 1 universe.

Reese, E. D.↗
DOE OSTI2023

Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets

State of the art astronomical simulations have provided datasets which enabled the training of novel deep learning techniques for constraining cosmological parameters. However, differences in subgrid physics implementation and numerical approximations among simulation suites lead to differences in simulated datasets, which pose a hard challenge when trying to generalize across diverse data domains and ultimately when applying models to observational data. Recent work reveals deep learning algorithms are able to extract more information from complex cosmological simulations than summary statistics like power spectra. We introduce Domain Adaptive Graph Neural Networks (DA-GNNs), trained on CAMELS data, inspired by CosmoGraphNet (Villanueva-Domingo et al 2023). By utilizing GNNs, we can capitalize on their capacity to capture both astrophysical and topological features of galaxy distributions. Mixing these capabilities with domain adaptation techniques such as Maximum Mean Discrepancy (MMD), which enable extraction of domain-invariant features, our framework demonstrates enhanced accuracy and robustness. We present experimental results, including the alignment of distributions across domains through data visualization. These findings suggest that DA-GNNs are an efficient way of extracting domain independent cosmological information, a vital step toward robust deep learning for real cosmic survey data.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2001

Galaxy Cluster Gas Mass Fractions From Sunyaev-Zel'dovich Effect Measurement: Constraints on Omega_M

Using sensitive centimeter-wave receivers mounted on the Owens Valley Radio Observatory and Berkeley-Illinois-Maryland-Association millimeter arrays, we have obtained interferometric measurements of the Sunyaev-Zei'dovich (SZ) effect toward massive galaxy clusters. We use the SZ data to determine the pressure distribution of the cluster gas and, in combination with published X-ray temperatures, to infer the gas mass and total gravitational mass of 18 clusters. The gas mass fraction, f~g, is calculated for each cluster, and is extrapolated to the fiducial radius r_{500} using the results of numerical simulations. The mean f_g within r_{500} is 0.081 + 0.009 - 0.011/(h_{100} (statistical uncertainty at 68% confidence level, assuming OmegaM=0.3, OmegaL=0.7). We discuss possible sources of systematic errors in the mean f 9 measurement. We derive an upper limit for OmegaM from this sample under the assumption that the mass composition of clusters within r_{500} reflects the universal mass composition: Omega_M h <Omega_B/f_g.The gas mass f on cosmology through the angular diameter distance and the r_{500} correction factors. For a flat universe (Omegal, = 1 - OmegaM) and h=0.7, we find the measured gas mass fractions are consistent with OmegaM less than 0.40, at 68% confidence. Including estimates of the baryons contained in galaxies and the baryons which failed to become bound during the cluster formation process, we find OmegaM\approximately 0.25.

Grego, Laura↗
DOE OSTI2022

The DESI N -body simulation project – I. Testing the robustness of simulations for the DESI dark time survey

ABSTRACT Analysis of large galaxy surveys requires confidence in the robustness of numerical simulation methods. The simulations are used to construct mock galaxy catalogues to validate data analysis pipelines and identify potential systematics. We compare three N-body simulation codes, abacus, gadget-2, and swift, to investigate the regimes in which their results agree. We run N-body simulations at three different mass resolutions, 6.25 × 108, 2.11 × 109, and 5.00 × 109 h−1 M⊙, matching phases to reduce the noise within the comparisons. We find systematic errors in the halo clustering between different codes are smaller than the Dark Energy Spectroscopic Instrument (DESI) statistical error for $s\ \gt\ 20\ h^{-1}$ Mpc in the correlation function in redshift space. Through the resolution comparison we find that simulations run with a mass resolution of 2.1 × 109 h−1 M⊙ are sufficiently converged for systematic effects in the halo clustering to be smaller than the DESI statistical error at scales larger than $20\ h^{-1}$ Mpc. These findings show that the simulations are robust for extracting cosmological information from large scales which is the key goal of the DESI survey. Comparing matter power spectra, we find the codes agree to within 1 per cent for k ≤ 10 h Mpc−1. We also run a comparison of three initial condition generation codes and find good agreement. In addition, we include a quasi-N-body code, FastPM, since we plan use it for certain DESI analyses. The impact of the halo definition and galaxy–halo relation will be presented in a follow-up study.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2003

Constraints on the Energy Content of the Universe from a Combination of Galaxy Cluster Observables

We demonstrate that constraints on cosmological parameters from the distribution of clusters as a function of redshift (dN/dz) are complementary to accurate angular diameter distance (D(sub A)) measurements to clusters, and their combination significantly tightens constraints on the energy density content of the Universe. The number counts can be obtained from X-ray and/or SZ (Sunyaev-Ze'dovich effect) surveys, and the angular diameter distances can be determined from deep observations of the intra-cluster gas using their thermal bremsstrahlung X-ray emission and the SZ effect. We combine constraints from simulated cluster number counts expected from a 12 deg(sup 2) SZ cluster survey and constraints from simulated angular diameter distance measurements based on the X-ray/SZ method assuming a statistical accuracy of 10% in the angular diameter distance determination of 100 clusters with redshifts less than 1.5. We find that Omega(sub m), can be determined within about 25%, Omega(sub lambda) within 20% and w within 16%. We show that combined dN/dz+(sub lambda) constraints can be used to constrain the different energy densities in the Universe even in the presence of a few percent redshift dependent systematic error in D(sub lambda). We also address the question of how best to select clusters of galaxies for accurate diameter distance determinations. We show that the joint dN/dz+ D(lambda) constraints on cosmological parameters for a fixed target accuracy in the energy density parameters are optimized by selecting clusters with redshift upper cut-offs in the range 0.55 approx. less than 1. Subject headings: cosmological parameters - cosmology: theory - galaxies:clusters: general

Molnar, Sandor M.↗
DOE OSTI2024

Tracing back the birth environments of Type Ia supernova progenitor stars: a pilot study based on 44 early-type host galaxies

The environmental dependence of Type Ia supernova (SN Ia) luminosities is well established, and efforts are being made to find its origin. Previous studies typically use the currently observed status of the host galaxy. However, given the delay time between the birth of the progenitor star and the SN Ia explosion, the currently observed status may differ from the birth environment of the SN Ia progenitor star. In this paper, employing the chemical evolution and accurately determined stellar population properties of 44 early-type host galaxies, we, for the first time, estimate the SN Ia progenitor star birth environment, specifically [Fe/H] Birth and [α/Fe] Birth . We show that [α/Fe] Birth has a $30.4^{\text{+10.6}}_{-10.1}{{\ \rm per\ cent}}$ wider range than the currently observed [α/Fe] Current , while the range of [Fe/H] Birth is not statistically different ($17.9^{\text{+26.0}}_{-27.1}{{\ \rm per\ cent}}$) to that of [Fe/H] Current . The birth and current environments of [Fe/H] and [α/Fe] are sampled from different populations (p-values of the Kolmogorov–Smirnov test <0.01). We find that light-curve fit parameters are insensitive to [Fe/H] Birth (<0.9σ for the non-zero slope), while a linear trend is observed with Hubble residuals (HRs) at the 2.4σ significance level. With [α/Fe] Birth , no linear trends (<1.1σ) are observed. Interestingly, we find that [α/Fe] Birth clearly splits the SN Ia sample into two groups: SN Ia exploded in [α/Fe] Birth -rich or [α/Fe ]Birth -poor environments. SNe Ia exploded in different [α/Fe] Birth groups have different weighted-means of light-curve shape parameters: 0.81 ± 0.33 (2.5σ). They are thought to be drawn from different populations (p-value = 0.01). Regarding SN Ia colour and HRs, there is no difference (<1.0σ) in the weighted-means and distribution (p-value > 0.27) of each [α/Fe] Birth group.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

A Hybrid Deep Learning Approach to Cosmological Constraints from Galaxy Redshift Surveys

We present a deep machine learning (ML)–based technique for accurately determining σ g and Ω m from mock 3D galaxy surveys. The mock surveys are built from the AbacusCosmos suite of N -body simulations, which comprises 40 cosmological volume simulations spanning a range of cosmological parameter values, and we account for uncertainties in galaxy formation scenarios through the use of generalized halo occupation distributions (HODs). We explore a trio of ML models: a 3D convolutional neural network (CNN), a power spectrum–based fully connected network, and a hybrid approach that merges the two to combine physically motivated summary statistics with flexible CNNs. We describe best practices for training a deep model on a suite of matched-phase simulations, and we test our model on a completely independent sample that uses previously unseen initial conditions, cosmological parameters, and HOD parameters. Despite the fact that the mock observations are quite small (~0.07 h -3 Gpc 3 ) and the training data span a large parameter space (six cosmological and six HOD parameters), the CNN and hybrid CNN can constrain estimates of σ g and Ω m to ~3% and ~4%, respectively.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2012

Cosmic Ray Studies with the Fermi Gamma-ray Space Telescope Large Area Telescope

The Large Area Telescope (LAT) on the Fermi Gamma-ray Space Telescope provides both direct and indirect measurements of galactic cosmic rays (CR). The LAT high-statistics observations of the 7 GeV - 1 TeV electron plus positron spectrum and limits on spatial anisotropy constrain models for this cosmic-ray component. On a galactic scale, the LAT observations indicate that cosmic-ray sources may be more plentiful in the outer Galaxy than expected or that the scale height of the cosmic-ray diffusive halo is larger than conventional models. Production of cosmic rays in supernova remnants (SNR) is supported by the LAT gamma-ray studies of several of these, both young SNR and those interacting with molecular clouds.

Thompson, David J.↗
NASA NTRS2011

Cosmic Ray Studies with the Fermi Gamma-ray Space Telescope Large Area Telescope

The Large Area Telescope (LAT) on the Fermi Gamma-ray Space Telescope provides both direct and indirect measurements of Galactic cosmic rays (CR). The LAT high-statistics observations of the 7 GeV - 1 TcV electron plus positron spectrum and limits on spatial anisotropy constrain models for this cosmic-ray component. On a Galactic scale, the LAT observations indicate that cosmic-ray sources may be more plentiful in the outer Galaxy than expected or that the scale height of the cosmic-ray diffusive halo is larger than conventional models. Production of cosmic rays in supernova remnants (SNR) is supported by the LAT gamma-ray studies of several of these, both young SNR and those interacting with molecular clouds.

Thompson, D. J.↗
NASA NTRS1994

Multivariate Statistical Analysis Software Technologies for Astrophysical Research Involving Large Data Bases

We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complex database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects of the SKICAT system, and of some of the scientific results achieved to date. We also developed a user-friendly package for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications and has produced real, published results.

Djorgovski, S. G.↗
DOE OSTI2022

The Low-redshift Lyman Continuum Survey. II. New Insights into LyC Diagnostics

The Lyman continuum (LyC) cannot be observed at the epoch of reionization (z ≳ 6) owing to intergalactic H I absorption. To identify LyC emitters (LCEs) and infer the fraction of escaping LyC, astronomers have developed various indirect diagnostics of LyC escape. Using measurements of the LyC from the Low-redshift Lyman Continuum Survey (LzLCS), we present the first statistical test of these diagnostics. While optical depth indicators based on Lyα, such as peak velocity separation and equivalent width, perform well, we also find that other diagnostics, such as the [O III ]/[O II ] flux ratio and star formation rate surface density, predict whether a galaxy is an LCE. The relationship between these galaxy properties and the fraction of escaping LyC flux suggests that LyC escape depends strongly on H I column density, ionization parameter, and stellar feedback. We find that LCEs occupy a range of stellar masses, metallicities, star formation histories, and ionization parameters, which may indicate episodic and/or different physical causes of LyC escape.

79 ASTRONOMY AND ASTROPHYSICS↗