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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The effect of spiral arms on the Sérsic photometry of galaxies

The Sérsic profile is a widely used model for describing the surface brightness distribution of galaxies. Spiral galaxies, however, are qualitatively different from a Sérsic model. Aims. The goal of this study is to assess how accurately the total flux and half-light radius of a galaxy with spiral arms can be recovered when fitted with a Sérsic profile. Methods. I selected a sample of bulge-dominated galaxies with spiral arms. Using photometric data from the Hyper Suprime-Cam survey, I estimated the contribution of the spiral arms to their total flux. Then I generated simulated images of galaxies with similar characteristics, fitted them with a Sérsic model, and quantified the error on the determination of the total flux and half-light radius. Results. Spiral arms can introduce biases on the photometry of galaxies in a way that depends on the underlying smooth surface brightness profile, the location of the arms, and the depth of the photometric data. A set of spiral arms accounting for 10% of the flux of a bulge-dominated galaxy typically causes the total flux and the half-light radius to be overestimated by 15% and 30%, respectively. This bias, however, is much smaller if the galaxy is disk-dominated. Conclusions. Galaxies with a prominent bulge and a non-zero contribution from spiral arms are the most susceptible to biases in the total flux and half-light radius when fitted with a Sérsic profile. If photometric measurements with high accuracy are required, then measurements over finite apertures are to be preferred over global estimates of the flux.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

DSPS: Differentiable stellar population synthesis

ABSTRACT Models of stellar population synthesis (SPS) are the fundamental tool that relates the physical properties of a galaxy to its spectral energy distribution (SED). In this paper, we present DSPS: a python package for SPS. All of the functionality in DSPS is implemented natively in the JAX library for automatic differentiation, and so our predictions for galaxy photometry are fully differentiable, and directly inherit the performance benefits of JAX, including portability onto GPUs. DSPS also implements several novel features, such as i) a flexible empirical model for stellar metallicity that incorporates correlations with stellar age, ii) support for the Diffstar model that provides a physically-motivated connection between the star formation history of a galaxy (SFH) and the mass assembly of its underlying dark matter halo. We detail a set of theoretical techniques for using autodiff to calculate gradients of predictions for galaxy SEDs with respect to SPS parameters that control a range of physical effects, including SFH, stellar metallicity, nebular emission, and dust attenuation. When forward modelling the colours of a synthetic galaxy population, we find that DSPS can provide a factor of 5 speed-up over standard SPS codes on a CPU, and a factor of 300-400 on a modern GPU. When coupled with gradient-based techniques for optimization and inference, DSPS makes it practical to conduct expansive likelihood analyses of simulation-based models of the galaxy–halo connection that fully forward model galaxy spectra and photometry.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Dark Energy Survey Year 3 results: redshift calibration of the weak lensing source galaxies

ABSTRACT Determining the distribution of redshifts of galaxies observed by wide-field photometric experiments like the Dark Energy Survey (DES) is an essential component to mapping the matter density field with gravitational lensing. In this work we describe the methods used to assign individual weak lensing source galaxies from the DES Year 3 Weak Lensing Source Catalogue to four tomographic bins and to estimate the redshift distributions in these bins. As the first application of these methods to data, we validate that the assumptions made apply to the DES Y3 weak lensing source galaxies and develop a full treatment of systematic uncertainties. Our method consists of combining information from three independent likelihood functions: self-organizing map p(z) (sompz), a method for constraining redshifts from galaxy photometry; clustering redshifts (WZ), constraints on redshifts from cross-correlations of galaxy density functions; and shear ratios (SRs), which provide constraints on redshifts from the ratios of the galaxy-shear correlation functions at small scales. Finally, we describe how these independent probes are combined to yield an ensemble of redshift distributions encapsulating our full uncertainty. We calibrate redshifts with combined effective uncertainties of σ〈z〉 ∼ 0.01 on the mean redshift in each tomographic bin.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Self-supervised Representation Learning for Astronomical Images

Sky surveys are the largest data generators in astronomy, making automated tools for extracting meaningful scientific information an absolute necessity. We show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks. These representations can be directly used as features, or fine-tuned, to outperform supervised methods trained only on labeled data. We apply a contrastive learning framework on multiband galaxy photometry from the Sloan Digital Sky Survey (SDSS), to learn image representations. We then use them for galaxy morphology classification and fine-tune them for photometric redshift estimation, using labels from the Galaxy Zoo 2 data set and SDSS spectroscopy. In both downstream tasks, using the same learned representations, we outperform the supervised state-of-the-art results, and we show that our approach can achieve the accuracy of supervised models while using 2-4 times fewer labels for training. The codes, trained models, and data can be found at https://portal.nersc.gov/project/dasrepo/self-supervised-learning-sdss.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

DIGS: deep inference of galaxy spectra with neural posterior estimation

Abstract With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quantification. The combination of simulation-based inference (SBI) and amortized neural posterior estimation (NPE) has been successfully used to analyse simulated and real galaxy photometry both precisely and efficiently. In this work, we utilise this combination and build on existing literature to analyse simulated noisy galaxy spectra. Here, we demonstrate a proof-of-concept study of spectra that is (a) an efficient analysis of galaxy SEDs and inference of galaxy parameters with physically interpretable uncertainties; and (b) amortized calculations of posterior distributions of said galaxy parameters at the modest cost of a few galaxy fits with Markov chain Monte Carlo (MCMC) methods. We utilise the SED generator and inference framework Prospector to generate simulated spectra, and train a dataset of 2 × 10 6 spectra (corresponding to a five-parameter SED model) with NPE. We show that SBI—with its combination of fast and amortized posterior estimations—is capable of inferring accurate galaxy stellar masses and metallicities. Our uncertainty constraints are comparable to or moderately weaker than traditional inverse-modelling with Bayesian MCMC methods (e.g. 0.17 and 0.26 dex in stellar mass and metallicity for a given galaxy, respectively). We also find that our inference framework conducts rapid SED inference (0.9–1.2 × 10 5 galaxy spectra via SBI/NPE at the cost of 1 MCMC-based fit). With this work, we set the stage for further work that focuses of SED fitting of galaxy spectra with SBI, in the era of JWST galaxy survey programs and the wide-field Roman Space Telescope spectroscopic surveys.

spectroscopy↗
DOE OSTI2023

First Impressions: Early-time Classification of Supernovae Using Host-galaxy Information and Shallow Learning

Substantial effort has been devoted to the characterization of transient phenomena from photometric information. Automated approaches to this problem have taken advantage of complete phase coverage of an event, limiting their use for triggering rapid follow-up of ongoing phenomena. In this work, we introduce a neural network with a single recurrent layer designed explicitly for early photometric classification of supernovae (SNe). Our algorithm leverages transfer learning to account for model misspecification, host-galaxy photometry to solve the data-scarcity problem soon after discovery, and a custom weighted loss to prioritize accurate early classification. We first train our algorithm using state-of-the-art transient and host-galaxy simulations, then adapt its weights and validate it on the spectroscopically confirmed SNe Ia, SNe II, and SNe Ib/c from the Zwicky Transient Facility Bright Transient Survey. On observed data, our method achieves an overall accuracy of 82% ± 2% within 3 days of an event’s discovery, and an accuracy of 87% ± 5% within 30 days of discovery. At both early and late phases, our method achieves comparable or superior results to the leading classification algorithms with a simpler network architecture. These results help pave the way for rapid photometric and spectroscopic follow-up of scientifically valuable transients discovered in massive synoptic surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

A composite likelihood approach for inference under photometric redshift uncertainty

ABSTRACT Obtaining accurately calibrated redshift distributions of photometric samples is one of the great challenges in photometric surveys like LSST, Euclid, HSC, KiDS, and DES. We present an inference methodology that combines the redshift information from the galaxy photometry with constraints from two-point functions, utilizing cross-correlations with spatially overlapping spectroscopic samples, and illustrate the approach on CosmoDC2 simulations. Our likelihood framework is designed to integrate directly into a typical large-scale structure and weak lensing analysis based on two-point functions. We discuss efficient and accurate inference techniques that allow us to scale the method to the large samples of galaxies to be expected in LSST. We consider statistical challenges like the parametrization of redshift systematics, discuss and evaluate techniques to regularize the sample redshift distributions, and investigate techniques that can help to detect and calibrate sources of systematic error using posterior predictive checks. We evaluate and forecast photometric redshift performance using data from the CosmoDC2 simulations, within which we mimic a DESI-like spectroscopic calibration sample for cross-correlations. Using a combination of spatial cross-correlations and photometry, we show that we can provide calibration of the mean of the sample redshift distribution to an accuracy of at least 0.002(1 + z), consistent with the LSST-Y1 science requirements for weak lensing and large-scale structure probes.

(cosmology:) large-scale structure of Universe↗
DOE OSTI2024

Emission line predictions for mock galaxy catalogues: a new differentiable and empirical mapping from DESI

ABSTRACT We present a simple, differentiable method for predicting emission line strengths from rest-frame optical continua using an empirically determined mapping. Extensive work has been done to develop mock galaxy catalogues that include robust predictions for galaxy photometry, but reliably predicting the strengths of emission lines has remained challenging. Our new mapping is a simple neural network implemented using the JAX Python automatic differentiation library. It is trained on Dark Energy Spectroscopic Instrument Early Release data to predict the equivalent widths (EWs) of the eight brightest optical emission lines (including H α, H β, [O ii], and [O iii]) from a galaxy’s rest-frame optical continuum. The predicted EW distributions are consistent with the observed ones when noise is accounted for, and we find Spearman’s rank correlation coefficient ρs > 0.87 between predictions and observations for most lines. Using a non-linear dimensionality reduction technique, we show that this is true for galaxies across the full range of observed spectral energy distributions. In addition, we find that adding measurement uncertainties to the predicted line strengths is essential for reproducing the distribution of observed line-ratios in the BPT diagram. Our trained network can easily be incorporated into a differentiable stellar population synthesis pipeline without hindering differentiability or scalability with GPUs. A synthetic catalogue generated with such a pipeline can be used to characterize and account for biases in the spectroscopic training sets used for training and calibration of photo-z’s, improving the modelling of systematic incompleteness for the Rubin Observatory LSST and other surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Discovery of compact disc galaxies with high-surface brightness in the Sloan Digital Sky Survey

Compact disc galaxies (CDGs) with high-surface brightness were identified in the Sloan Digital Sky Survey data. We determined the surface profiles of the CDGs and compared them to those of normal-sized disc galaxies (NDGs). The CDGs have higher central brightness and older stellar age than the NDGs. Furthermore, the brightness profiles of the CDGs fit a Sérsic model with n ≈ 2.11 and have a zero g′ − r′ colour gradient on average. By contrast, the NDGs fit an exponential profile and have a negative colour gradient on average. These results indicate that the structure and stellar population of the CDGs and NDGs differ. We suggest that the CDGs are ancient galaxies in the quenching phase following the initial central starburst.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Two rest-frame wavelength measurements of galaxy sizes at z < 1: the evolutionary effects of emerging bulges and quenched newcomers

We analyse the size evolution of 16 000 star-forming galaxies (SFGs) and 5000 quiescent galaxies (QGs) with mass M * > 10 9.5 M ⊙ at 0.1 < z < 0.9 from the COSMOS field using deep CLAUDS + HSC imaging in two rest-frame wavelengths, 3000 Å (UV light) and 5000 Å (visible light). With half-light radius (R e ) as proxy for size, SFGs at characteristic mass M 0 = 5 × 10 10 M ⊙ grow by 20 per cent (30 per cent) in UV (visible) light since z ~ 1 and the strength of their size evolution increases with stellar mass. After accounting for mass growth due to star formation, we estimate that SFGs grow by 75 per cent in all stellar mass bins and in both rest-frame wavelengths. Redder SFGs are more massive, smaller and more concentrated than bluer SFGs and the fraction of red SFGs increases with time. These results point to the emergence of bulges as the dominant mechanism for the average size growth of SFGs. We find two threshold values for the stellar mass density within central 1 kpc (Σ 1 ): all SFGs with log Σ 1 ≳ 9 are red and only QGs have log Σ 1 ≳ 9.7. The size of M * = M 0 QGs grows by 50 per cent (110 per cent) in the UV (visible) light. Up to $\sim 20~{{\rm per\,cent}}$ of this increase in size of massive QGs is due to newcomers (recently quenched galaxies). However, newcomers cannot explain the observed pace in the size growth of QGs; that trend has to be dominated by processes affecting individual galaxies, such as minor mergers and accretion.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Quiescent Galaxy Size, Velocity Dispersion, and Dynamical Mass Evolution

Abstract We use surveys covering the redshift range 0.05 < z < 3.8 to explore quiescent galaxy scaling relations and the redshift evolution of the velocity dispersion, size, and dynamical mass at fixed stellar mass. For redshift z < 0.6, we derive mass-limited samples and demonstrate that these large samples enhance constraints on the evolution of the quiescent population. The constraints include 2985 new velocity dispersions from the SHELS F2 survey. In contrast with the known substantial evolution of size with redshift, evolution in the velocity dispersion is negligible. The dynamical-to-stellar-mass ratio increases significantly as the universe ages, in agreement with recent results that combine high-redshift data with the Sloan Digital Sky Survey. Like other investigators, we interpret this result as an indication that the dark matter fraction within the effective radius increases as a result of the impact of the minor mergers that are responsible for size growth. We emphasize that dense redshift surveys covering the range 0.07 < z < 1 along with strong and weak lensing measurements could remove many ambiguities in evolutionary studies of the quiescent population.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Size and Spectroscopic Evolution of HectoMAP Quiescent Galaxies

Abstract The HectoMAP survey provides a complete, mass-limited sample of 30,231 quiescent galaxies with i -band Hyper Suprime-Cam Subaru Strategic Program (HSC SSP) imaging that spans the redshift range 0.2 < z < 0.6. We combine half-light radii based on HSC SSP imaging with redshifts and D n 4000 to explore the size–mass relation, R e = A × M * α , and its evolution for the entire HectoMAP quiescent population and for two subsets of the data. Newcomers with 1.5 < D n 4000 < 1.6 at each redshift show a steeper increase in A as the universe ages than the population that descends from galaxies that are already quiescent at the survey limit, z ∼ 0.6 (the resident population). In broad agreement with previous studies, evolution in the size–mass relation both for the entire HectoMAP sample and for the resident population (but not for the newcomers alone) is consistent with minor merger driven growth. For the resident population, the evolution in the size–mass relation is independent of the population age at z ∼ 0.6. The contrast between the sample of newcomers and the resident population provides insight into the role of commonly termed “progenitor bias” on the evolution of the size–mass relation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Herschel Photometric Observations of Little Things Dwarf Galaxies

We present here far-infrared photometry of galaxies in a sample that is relatively unexplored at these wavelengths: low-metallicity dwarf galaxies with moderate star formation rates. Four dwarf irregular galaxies from the Little Things survey are considered, with deep Herschel PACS and SPIRE observations at 100, 160, 250, 350, and 500 μm. Results from modified blackbody fits indicate that these galaxies have low dust masses and cooler dust temperatures than more actively star-forming dwarfs, occupying the lowest L {sub TIR} and M {sub dust} regimes seen among these samples. Dust-to-gas mass ratios of ∼10{sup −5} are lower, overall, than in more massive and active galaxies but are roughly consistent with the broken power-law relation between the dust-to-gas ratio and metallicity found for other low-metallicity systems. Chemical evolution modeling suggests that these dwarf galaxies are likely forming very little dust via stars or grain growth and have very high dust destruction rates.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Color Transformations of Photometric Measurements of Galaxies in Optical and Near-infrared Wide-field Imaging Surveys*

Abstract Over the past 2 decades, wide-field photometric surveys in optical and infrared domains reached a nearly all-sky coverage thanks to numerous observational facilities operating in both hemispheres. However, subtle differences among exact realizations of Johnson and SDSS photometric systems require one to convert photometric measurements into the same system prior to analysis of composite data sets originating from multiple surveys. It turns out that the published photometric transformations lead to substantial biases when applied to integrated photometry of galaxies from the corresponding catalogs. Here we present photometric transformations based on piece-wise linear approximations of integrated photometry of galaxies in the optical surveys SDSS, DECaLS, BASS, MzLS, DES, DELVE, KiDS, VST ATLAS, and the near-infrared surveys UKIDSS, UHS, VHS, and VIKING. We validate our transformations by constructing k -corrected color–magnitude diagrams of non-active galaxies and measuring the position and tightness of the “red sequence.” We also provide transformations for aperture magnitudes and show how they are affected by the image quality difference among the surveys. We present the implementation of the derived transformations in python and idl and also a web-based color transformation calculator for galaxies. By comparing DECaLS and DES, we identified systematic issues in DECaLS photometry for extended galaxies, which we attribute to the photometric software package used by DECaLS. As an application of our method, we compiled two multi-wavelength photometric catalogs for over 200,000 low- and intermediate-redshift galaxies originating from CfA FAST and Hectospec spectral archives.

Astronomy & Astrophysics↗
DOE OSTI2021

SDSS-IV DR17: final release of MaNGA PyMorph photometric and deep-learning morphological catalogues

ABSTRACT We present the MaNGA PyMorph photometric Value Added Catalogue (MPP-VAC-DR17) and the MaNGA Deep Learning Morphological VAC (MDLM-VAC-DR17) for the final data release of the MaNGA survey, which is part of the SDSS Data Release 17 (DR17). The MPP-VAC-DR17 provides photometric parameters from Sérsic and Sérsic+Exponential fits to the two-dimensional surface brightness profiles of the MaNGA DR17 galaxy sample in the g, r, and i bands (e.g. total fluxes, half-light radii, bulge-disc fractions, ellipticities, position angles, etc.). The MDLM-VAC-DR17 provides deep-learning-based morphological classifications for the same galaxies. The MDLM-VAC-DR17 includes a number of morphological properties, for example, a T-Type, a finer separation between elliptical and S0, as well as the identification of edge-on and barred galaxies. While the MPP-VAC-DR17 simply extends the MaNGA PyMorph photometric VAC published in the SDSS Data Release 15 (MPP-VAC-DR15) to now include galaxies that were added to make the final DR17, the MDLM-VAC-DR17 implements some changes and improvements compared to the previous release (MDLM-VAC-DR15): Namely, the low end of the T-Types is better recovered in this new version. The catalogue also includes a separation between early or late type, which classifies the two populations in a complementary way to the T-Type, especially at the intermediate types (−1 < T-Type < 2), where the T-Type values show a large scatter. In addition, k-fold-based uncertainties on the classifications are also provided. To ensure robustness and reliability, we have also visually inspected all the images. We describe the content of the catalogues and show some interesting ways in which they can be combined.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

How to Obtain the Redshift Distribution from Probabilistic Redshift Estimates

Abstract A reliable estimate of the redshift distribution n ( z ) is crucial for using weak gravitational lensing and large-scale structures of galaxy catalogs to study cosmology. Spectroscopic redshifts for the dim and numerous galaxies of next-generation weak-lensing surveys are expected to be unavailable, making photometric redshift (photo- z ) probability density functions (PDFs) the next best alternative for comprehensively encapsulating the nontrivial systematics affecting photo- z point estimation. The established stacked estimator of n ( z ) avoids reducing photo- z PDFs to point estimates but yields a systematically biased estimate of n ( z ) that worsens with a decreasing signal-to-noise ratio, the very regime where photo- z PDFs are most necessary. We introduce Cosmological Hierarchical Inference with Probabilistic Photometric Redshifts ( CHIPPR ), a statistically rigorous probabilistic graphical model of redshift-dependent photometry that correctly propagates the redshift uncertainty information beyond the best-fit estimator of n ( z ) produced by traditional procedures and is provably the only self-consistent way to recover n ( z ) from photo- z PDFs. We present the chippr prototype code, noting that the mathematically justifiable approach incurs computational cost. The CHIPPR approach is applicable to any one-point statistic of any random variable, provided the prior probability density used to produce the posteriors is explicitly known; if the prior is implicit, as may be the case for popular photo- z techniques, then the resulting posterior PDFs cannot be used for scientific inference. We therefore recommend that the photo- z community focus on developing methodologies that enable the recovery of photo- z likelihoods with support over all redshifts, either directly or via a known prior probability density.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

SITCOMTN-165: Surface brightness profiles around massive galaxies in LSSTComCam data

Low surface brightness photometry of massive galaxies can probe the extended stellar halos and intracluster light but is often limited by background subtraction and masking systematics. Here we present radial surface brightness profiles for the brightest cluster galaxy of Abell 360 and 2dFGRS TGS323Z113 using Rubin Observatory Commissioning Camera (LSSTComCam) Data Preview 1 in the g, r, and z bands. Our results show that the LSSTComCam data can go down to ~28 mag arcsec^-2 in the r band. Comparison to DECaLS measurements processed with Legacyhalos shows good agreement of surface brightness profiles at small radii while the LSSTComCam data extend deeper at larger radii.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

The ALFALFA-SDSS Galaxy Catalog

We present a H i optical catalog of ~30,000 galaxies based on the 100% complete Arecibo Legacy Fast Arecibo L-band Feed Array (ALFALFA) survey combined with data from the Sloan Digital Sky Survey (SDSS). Our goal is to facilitate the public use of the completed ALFALFA catalog by providing carefully determined matches to SDSS counterparts, including matches for ~10,000 galaxies that do not have SDSS spectra. These identifications can provide a basis for further crossmatching with other surveys using SDSS photometric IDs as a reference point. We derive absolute magnitudes and stellar masses for each galaxy using optical colors combined with an internal reddening correction designed for small- and intermediate-mass galaxies with active star formation. We also provide measures of stellar masses and star formation rates based on infrared and/or ultraviolet photometry for galaxies that are detected by the Wide-field Infrared Survey Explorer and/or the Galaxy Evolution Explorer. Finally, we compare the galaxy population in the ALFALFA-SDSS sample with the populations in several other publicly available galaxy catalogs and confirm that ALFALFA galaxies typically have lower masses and bluer colors.

79 ASTRONOMY AND ASTROPHYSICS↗