Engineering topics

Andrews, Brett H.

Publications and source records attributed to Andrews, Brett H..

8 records

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

Are Milky-Way-like galaxies like the Milky Way? A view from SDSS-IV/MaNGA

ABSTRACT In this paper, we place the Milky Way (MW) in the context of similar-looking galaxies in terms of their star-formation and chemical evolution histories. We select a sample of 138 Milky Way analogues (MWAs) from the SDSS-IV/MaNGA survey based on their masses, Hubble types, and bulge-to-total ratios. To compare their chemical properties to the detailed spatially-resolved information available for the MW, we use a semi-analytic spectral fitting approach, which fits a self-consistent chemical-evolution and star-formation model directly to the MaNGA spectra. We model the galaxies’ inner and outer regions assuming that some of the material lost in stellar winds falls inwards. We also incorporate chemical enrichment from type II and Ia supernovae to follow the alpha-element abundance at different metallicities and locations. We find some MWAs where the stellar properties closely reproduce the distribution of age, metallicity, and alpha enhancement at both small and large radii in the MW. In these systems, the match is driven by the longer time-scale for star formation in the outer parts, and the inflow of enriched material to the central parts. However, other MWAs have very different histories. These divide into two categories: self-similar galaxies where the inner and outer parts evolve identically; and centrally-quenched galaxies where there is very little evidence of late-time central star formation driven by material accreted from the outer regions. We find that, although selected to be comparable, there are subtle morphological differences between galaxies in these different classes, and that the centrally-quenched galaxies formed their stars systematically earlier.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Photometric redshifts from SDSS images with an interpretable deep capsule network

ABSTRACT Studies of cosmology, galaxy evolution, and astronomical transients with current and next-generation wide-field imaging surveys like the Rubin Observatory Legacy Survey of Space and Time are all critically dependent on estimates of photometric redshifts. Capsule networks are a new type of neural network architecture that is better suited for identifying morphological features of the input images than traditional convolutional neural networks. We use a deep capsule network trained on ugriz images, spectroscopic redshifts, and Galaxy Zoo spiral/elliptical classifications of ∼400 000 Sloan Digital Sky Survey galaxies to do photometric redshift estimation. We achieve a photometric redshift prediction accuracy and a fraction of catastrophic outliers that are comparable to or better than current methods for SDSS main galaxy sample-like data sets (r ≤ 17.8 and zspec ≤ 0.4) while requiring less data and fewer trainable parameters. Furthermore, the decision-making of our capsule network is much more easily interpretable as capsules act as a low-dimensional encoding of the image. When the capsules are projected on a two-dimensional manifold, they form a single redshift sequence with the fraction of spirals in a region exhibiting a gradient roughly perpendicular to the redshift sequence. We perturb encodings of real galaxy images in this low-dimensional space to create synthetic galaxy images that demonstrate the image properties (e.g. size, orientation, and surface brightness) encoded by each dimension. We also measure correlations between galaxy properties (e.g. magnitudes, colours, and stellar mass) and each capsule dimension. We publicly release our code, estimated redshifts, and additional catalogues at https://biprateep.github.io/encapZulate-1.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

SDSS-IV MaNGA: How the Stellar Populations of Passive Central Galaxies Depend on Stellar and Halo Mass

We analyze spatially resolved and co-added SDSS-IV MaNGA spectra with signal-to-noise ratio ~100 from 2200 passive central galaxies (z ~ 0.05) to understand how central galaxy assembly depends on stellar mass (M*) and halo mass (M h ). We control for systematic errors in M h by employing a new group catalog from Tinker and the widely used Yang et al. catalog. At fixed M*, the strengths of several stellar absorption features vary systematically with M h . Completely model-free, this is one of the first indications that the stellar populations of centrals with identical M* are affected by the properties of their host halos. To interpret these variations, we applied full spectral fitting with the code alf. At fixed M*, centrals in more massive halos are older, show lower [Fe/H], and have higher [Mg/Fe] with 3.5σ confidence. We conclude that halos not only dictate how much M* galaxies assemble but also modulate their chemical enrichment histories. Turning to our analysis at fixed M h , high-M* centrals are older, show lower [Fe/H], and have higher [Mg/Fe] for M h > 10 12 h –1 M⊙ with confidence >4σ. While massive passive galaxies are thought to form early and rapidly, our results are among the first to distinguish these trends at fixed M h . They suggest that high-M* centrals experienced unique early formation histories, either through enhanced collapse and gas fueling or because their halos were early forming and highly concentrated, a possible signal of galaxy assembly bias.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

SDSS-IV MaNGA: Exploring the Local Scaling Relations for N/O

We present, for the first time, the relationship between local stellar mass surface density, Σ * , and N/O derived from SDSS-IV MaNGA data, using a sample of 792,765 high signal-to-noise ratio star-forming spaxels. Using a combination of phenomenological modeling and partial correlation analysis, we find that Σ * alone is insufficient to predict the N/O in MaNGA spaxels and that there is an additional dependence on the local star formation rate surface density, Σ SFR . This effect is a factor of 3 stronger than the dependence of 12+log(O/H) on Σ SFR . Surprisingly, we find that the local N/O scaling relations also depend on the total galaxy stellar mass at fixed Σ * and the galaxy size at fixed stellar mass. We find that more compact galaxies are more nitrogen rich, even when Σ * and Σ SFR are controlled for. We show that ~50% of the variance of N/O is explained by the total stellar mass and size. Thus, the evolution of nitrogen in galaxies is set by more than just local effects and does not simply track the buildup of oxygen in galaxies. The precise form of the N/O–O/H relation is therefore sensitive to the sample of galaxies from which it is derived. This result casts doubt on the universal applicability of nitrogen-based strong-line metallicity indicators derived in the local universe.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Constraining the Milky Way’s ultraviolet-to-infrared SED with Gaussian process regression

ABSTRACT Improving our knowledge of global Milky Way (MW) properties is critical for connecting the detailed measurements only possible from within our Galaxy to our understanding of the broader galaxy population. We here train Gaussian process regression (GPR) models on SDSS (Sloan Digital Sky Survey) galaxies to map from galaxy properties (stellar mass, apparent axial ratio, star formation rate, bulge-to-total ratio, disc scale length, and bar vote fraction) to ultraviolet (UV; GALEX FUV/NUV), optical (SDSS ugriz), and infrared (IR; 2MASS JHKs and WISE W1/W2/W3/W4) fluxes and uncertainties. With these models, we estimate the photometric properties of the MW, resulting in a full UV-to-IR spectral energy distribution (SED) as it would be measured externally, viewed face-on. We confirm that the MW lies in the green valley in optical diagnostic diagrams, but show for the first time that the MW is in the star-forming region in standard UV and IR diagnostics – characteristic of the population of red spiral galaxies. Although our GPR method predicts one band at a time, the resulting MW UV–IR SED is consistent with SEDs of local spirals with characteristics broadly similar to the MW, suggesting that these independent predictions can be combined reliably. Our UV–IR SED will be invaluable for reconstructing the MW’s star formation history using the same tools employed for external galaxies, allowing comparisons of results from in situ measurements to those from the methods used for extragalactic objects.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Machine-learning Classifiers for Intermediate Redshift Emission-line Galaxies

Classification of intermediate redshift (z = 0.3-0.8) emission line galaxies as star-forming galaxies, composite galaxies, active galactic nuclei (AGNs), or low-ionization nuclear emission regions (LINERs) using optical spectra alone was impossible because the lines used for standard optical diagnostic diagrams: [N ii], Hα, and [S ii] are redshifted out of the observed wavelength range. In this work, we address this problem using four supervised machine-learning classification algorithms: k-nearest neighbors (KNN), support vector classifier (SVC), random forest (RF), and a multilayer perceptron (MLP) neural network. For input features, we use properties that can be measured from optical galaxy spectra out to z < 0.8 - [O iii]/Hβ, [O ii]/Hβ, [O iii] line width, and stellar velocity dispersion - and four colors (u - g, g - r, r - i, and i - z) corrected to z = 0.1. The labels for the low redshift emission line galaxy training set are determined using standard optical diagnostic diagrams. RF has the best area under curve score for classifying all four galaxy types, meaning the highest distinguishing power. Both the AUC scores and accuracies of the other algorithms are ordered as MLP > SVC > KNN. The classification accuracies with all eight features (and the four spectroscopically determined features only) are 93.4% (92.3%) for star-forming galaxies, 69.4% (63.7%) for composite galaxies, 71.8% (67.3%) for AGNs, and 65.7% (60.8%) for LINERs. The stacked spectrum of galaxies of the same type as determined by optical diagnostic diagrams at low redshift and RF at intermediate redshift are broadly consistent. Our publicly available code (https://github.com/zkdtc/MLC_ELGs) and trained models will be instrumental for classifying emission line galaxies in upcoming wide-field spectroscopic surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2013

Exploring the Chemical Link Between Local Ellipticals and Their High-Redshift Progenitors

We present Keck/MOSFIRE K-band spectroscopy of the first mass-selected sample of galaxies at z approximately 2.3. Targets are selected from the 3D-Hubble Space Telescope Treasury survey. The six detected galaxies have a mean [N II]lambda6584/H-alpha ratio of 0.27 +/- 0.01, with a small standard deviation of 0.05. This mean value is similar to that of UV-selected galaxies of the same mass. The mean gas-phase oxygen abundance inferred from the [N II]/Halpha ratios depends on the calibration method, and ranges from 12+log(O/H)(sub gas) = 8.57 for the Pettini & Pagel calibration to 12+log(O/H)(sub gas) = 8.87 for the Maiolino et al. calibration. Measurements of the stellar oxygen abundance in nearby quiescent galaxies with the same number density indicate 12+log(O/H)(sub stars) = 8.95, similar to the gas-phase abundances of the z approximately 2.3 galaxies if the Maiolino et al. calibration is used. This suggests that these high-redshift star forming galaxies may be progenitors of today's massive early-type galaxies. The main uncertainties are the absolute calibration of the gas-phase oxygen abundance and the incompleteness of the z approximately 2.3 sample: the galaxies with detected Hα tend to be larger and have higher star formation rates than the galaxies without detected H-alpha, and we may still be missing the most dust-obscured progenitors.

Chemical↗