Bridging between the integrated and resolved main sequence of star formation

The position of galaxies on the stellar mass, star formation rate (SFR) plane with respect to the star-forming main sequence at each redshift is a convenient way to infer where the galaxy is in its evolution compared to the rest of the population. We use Hubble Space Telescope high-resolution images in the GOODS-S field from the the Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (CANDELS) and fit multiwavelength lights in resolution elements of galaxies with stellar population synthesis models. We then construct resolved kpc-scale stellar mass, SFR surface density curves for galaxies at z ~ 1. Fitting these resolved main sequence curves with Schechter functions, we parameterize and explain the multiwavelength structure of galaxies with three variables: φ*, α, and M*. For quenched galaxies below the main sequence, we find an average high-mass slope (α) of the resolved main sequence curves to be ~−0.4. The scatter of this slope is higher among the lower mass star-forming galaxies and those above the main sequence compared to quenched galaxies, due to lack of an evolved bulge. Our findings agree well with an inside-out quenching of star formation. We find that the knee of the Schechter fits (M*) for galaxies below the main sequence occurs at lower stellar mass surface densities compared to star-forming galaxies, which hints at how far quenching has proceeded outward

Bridging between the integrated and resolved main sequence of star formation

The position of galaxies on the stellar mass, star formation rate (SFR) plane with respect to the star-forming main sequence at each redshift is a convenient way to infer where the galaxy is in its evolution compared to the rest of the population. We use Hubble Space Telescope high-resolution images in the GOODS-S field from the the Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (CANDELS) and fit multiwavelength lights in resolution elements of galaxies with stellar population synthesis models. We then construct resolved kpc-scale stellar mass, SFR surface density curves for galaxies at z ~ 1. Fitting these resolved main sequence curves with Schechter functions, we parameterize and explain the multiwavelength structure of galaxies with three variables: φ*, α, and M*. For quenched galaxies below the main sequence, we find an average high-mass slope (α) of the resolved main sequence curves to be ~−0.4. The scatter of this slope is higher among the lower mass star-forming galaxies and those above the main sequence compared to quenched galaxies, due to lack of an evolved bulge. Our findings agree well with an inside-out quenching of star formation. We find that the knee of the Schechter fits (M*) for galaxies below the main sequence occurs at lower stellar mass surface densities compared to star-forming galaxies, which hints at how far quenching has proceeded outward

Deblending Galaxies with Generative Adversarial Networks

Deep generative models including generative adversarial networks (GANs) are powerful unsupervised tools in learning the distributions of data sets. Building a simple GAN architecture in PyTorch and training on the CANDELS data set, we generate galaxy images with the Hubble Space Telescope resolution starting from a noise vector. We proceed by modifying the GAN architecture to improve the Subaru Hyper Suprime-Cam ground-based images by increasing their resolution to the HST resolution. We use the super resolution GAN on a large sample of blended galaxies which we create using CANDELS cutouts. In our simulated blend sample, $\sim 20 \%$ would unrecognizably be blended even in the HST resolution cutouts. In the HSC-like cutouts this fraction rises to $\sim 90\%$. With our modified GAN we can lower this value to $\sim 50\%$. We quantify the blending fraction in the high, low and GAN resolutions over the whole manifold of angular separation, flux ratios, sizes and redshift difference between the two blended objects. The two peaks found by the GAN deblender result in ten times improvement in the photometry measurement of the blended objects. Modifying the architecture of the GAN, we also train a Multi-wavelength GAN with seven band optical+NIR HST cutouts. This multi-wavelength GAN improves the fraction of detected blends by another $\sim 10\%$ compared to the single-band GAN. This is most beneficial to the current and future precision cosmology experiments (e.g., LSST, SPHEREx, Euclid, Roman), specifically those relying on weak gravitational lensing, where blending is a major source of systematic error.Comment: 12 pages, 9 figures, accepted for publication in the Astrophysical Journa

Spectroscopic confirmation of a Coma Cluster progenitor at z~2.2

We report the spectroscopic confirmation of a new protocluster in the COSMOS field at z ∼ 2.2, originally identified as an overdensity of narrow-band selected Hα emitting candidates. With only two masks of Keck/MOSFIRE near-IR spectroscopy in both H (∼ 1.47-1.81 μm) and K (∼ 1.92- 2.40 μm) bands (∼ 1.5 hour each), we confirm 35 unique protocluster members with at least two emission lines detected with S/N > 3. Combined with 12 extra members from the zCOSMOS-deep spectroscopic survey (47 in total), we estimate a mean redshift, line-of-sight velocity dispersion, and total mass of zmean=2.23224 ± 0.00101, σlos=645 ± 69 km s−1, and Mvir ∼ (1 − 2)×10^14 M⊙ for this protocluster, respectively. We estimate a number density enhancement of δg ∼ 7 for this system and we argue that the structure is likely not virialized at z ∼ 2.2. However, in a spherical collapse model, δg is expected to grow to a linear matter enhancement of ∼ 1.9 by z=0, exceeding the collapse threshold of 1.69, and leading to a fully collapsed and virialized Coma-type structure with a total mass of Mdyn(z=0) ∼ 9.2×10^14 M⊙ by now. This observationally efficient confirmation suggests that large narrow-band emission-line galaxy surveys, when combined with ancillary photometric data, can be used to effectively trace the large-scale structure and protoclusters at a time when they are mostly dominated by star-forming galaxies

A Statistical Modeling Approach to Selection and Study of Galaxies at Different Phases of their Star-Formation Activity at High Redshift

This thesis focuses on the selection and study of galaxies based on their star formation activity at high redshift. I use multiple selection techniques from the traditional color selection to a Bayesian model averaging approach with Bayesian SED fitting to select the massive quiescent galaxies at $z \geq 3$ in the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS). I compare predictions from empirical to the latest cosmological hydrodynamical simulations and find that number and stellar mass density are higher than predictions. I estimate their halo mass using abundance matching, which results in massive enough halos that shock heating for some fraction of the gas can explain part of their quenching process. But as cold streams are expected to be significant even for massive halos at these redshifts, other quenching mechanisms such as AGN feedback must have the dominant role. I then develop models for selecting these objects using a statistical learning approach to allow a robust and computationally efficient selection in the upcoming extensive surveys. I train and validate different methods using the mock catalog from the semi-analytic models for the CANDELS. Many of these techniques outperform the generic SED-fitting approach applied on the large catalogs and make more robust samples in terms of completeness and purity. Finally, I build a probabilistic model for jointly describing the galaxies' stellar mass, star-formation rate, and local density contrast, using a mixture model while accounting for different sources of uncertainties. I find that the effect of the environment on the prediction of a galaxy's star-formation activity is different when moving towards higher redshifts. The impact of the environment on the odds of being quiescent versus star-forming has small interaction with stellar mass at low redshift but shows strong interaction at high redshift (> 1) such that the effect of the environment is larger for more massive galaxies. The effect of the mass also depends on the environment and becomes larger in a denser environment. This is partly consistent with the picture that galaxies with halted cosmological gas accretion can become quiescent by depleting their gas reservoir through star-formation and outflows

Bringing Manifold Learning and Dimensionality Reduction to SED Fitters

International audienceWe show that unsupervised machine learning techniques are a valuable tool for both visualizing and computationally accelerating the estimation of galaxy physical properties from photometric data. As a proof of concept, we use self-organizing maps (SOMs) to visualize a spectral energy distribution (SED) model library in the observed photometry space. The resulting visual maps allow for a better understanding of how the observed data maps to physical properties and allows for better optimization of the model libraries for a given set of observational data. Next, the SOMs are used to estimate the physical parameters of 14,000 z ∼ 1 galaxies in the COSMOS field and are found to be in agreement with those measured with SED fitting. However, the SOM method is able to estimate the full probability distribution functions for each galaxy up to ∼106 times faster than direct model fitting. We conclude by discussing how this acceleration, as well as learning how the galaxy data manifold maps to physical parameter space and visualizing this mapping in lower dimensions, helps overcome other challenges in galaxy formation and evolution

Data from: Deep phylogenetic incongruence in the angiosperm clade Rosidae

Analysis of large data sets can help resolve difficult nodes in the tree of life and also reveal complex evolutionary histories. The placement of the Celastrales-Oxalidales-Malpighiales (COM) clade within Rosidae remains one of the most confounding phylogenetic questions in angiosperms, with previous analyses placing it with either Fabidae or Malvidae. To elucidate the position of COM, we assembled multi-gene matrices of chloroplast, mitochondrial, and nuclear sequences, as well as large single- and multi-copy nuclear gene data sets. Analyses of multi-gene data sets demonstrate conflict between the chloroplast and both nuclear and mitochondrial data sets, and the results are robust to various character-coding and data-exclusion treatments. Analyses of single- and multi-copy nuclear loci indicate that most loci support the placement of COM with Malvidae, fewer loci support COM with Fabidae, and almost no loci support COM outside a clade of Fabidae and Malvidae. Although incomplete lineage sorting and ancient introgressive hybridization remain as plausible explanations for the conflict among loci, more complete sampling is necessary to evaluate these hypotheses fully. Our results emphasize the importance of genomic data sets for revealing deep incongruence and complex patterns of evolution

The Art of Measuring Physical Parameters in Galaxies: A Critical Assessment of Spectral Energy Distribution Fitting Techniques

The study of galaxy evolution hinges on our ability to interpret multiwavelength galaxy observations in terms of their physical properties. To do this, we rely on spectral energy distribution (SED) models, which allow us to infer physical parameters from spectrophotometric data. In recent years, thanks to wide and deep multiwave band galaxy surveys, the volume of high-quality data have significantly increased. Alongside the increased data, algorithms performing SED fitting have improved, including better modeling prescriptions, newer templates, and more extensive sampling in wavelength space. We present a comprehensive analysis of different SED-fitting codes including their methods and output with the aim of measuring the uncertainties caused by the modeling assumptions. We apply 14 of the most commonly used SED-fitting codes on samples from the CANDELS photometric catalogs at z ∼ 1 and z ∼ 3. We find agreement on the stellar mass, while we observe some discrepancies in the star formation rate (SFR) and dust-attenuation results. To explore the differences and biases among the codes, we explore the impact of the various modeling assumptions as they are set in the codes (e.g., star formation histories, nebular, dust and active galactic nucleus models) on the derived stellar masses, SFRs, and A _V values. We then assess the difference among the codes on the SFR–stellar mass relation and we measure the contribution to the uncertainties by the modeling choices (i.e., the modeling uncertainties) in stellar mass (∼0.1 dex), SFR (∼0.3 dex), and dust attenuation (∼0.3 mag). Finally, we present some resources summarizing best practices in SED fitting