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At least 1,045 records · Page 58

A machine-learning approach to measuring the escape of ionizing radiation from galaxies in the reionization epoch

Recent observations of galaxies at z≳7, along with the low value of the electron scattering optical depth measured by the Planck mission, make galaxies plausible as dominant sources of ionizing photons during the epoch of reionization. However, scenarios of galaxy-driven reionization hinge on the assumption that the average escape fraction of ionizing photons is significantly higher for galaxies in the reionization epoch than in the local universe. The NIRSpec instrument on the James Webb Space Telescope (JWST) will enable spectroscopic observations of large samples of reionization-epoch galaxies. While the leakage of ionizing photons will not be directly measurable from these spectra, the leakage is predicted to have an indirect effect on the spectral slope and the strength of nebular emission lines in the rest-frame ultraviolet and optical. Here, we apply a machine learning technique known as lasso regression on mock JWST/NIRSpec observations of simulated z = 7 galaxies in order to obtain a model that can predict the escape fraction from JWST/NIRSpec data. Barring systematic biases in the simulated spectra, our method is able to retrieve the escape fraction with a mean absolute error of Δf{sub esc}≈0.12 for spectra with signal-to-noise ratio ≈ 5 at a rest-frame wavelength of 1500 Å for our fiducial simulation. This prediction accuracy represents a significant improvement over previous similar approaches.

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Impact of cosmic variance on the galaxy-halo connection for Lyα emitters

In this paper we study the impact of cosmic variance and observational uncertainties in constraining the mass and occupation fraction, f{sub occ}, of dark matter (DM) halos hosting Lyα-emitting galaxies (LAEs) at high redshift. To this end, we construct mock catalogs from an N-body simulation to match the typical size of observed fields at z = 3.1 (∼1 deg{sup 2}). In our model a DM halo with mass in the range M{sub min}<M{sub h}<M{sub max} can only host one detectable LAE at most. We proceed to explore the parameter space determined by M{sub min}, M{sub max}, and f{sub occ} with a Markov Chain Monte Carlo algorithm using the angular correlation function and the LAEs’ number density as observational constraints. We find that the preferred minimum and maximum masses in our model span a wide range 10{sup 10.0}h{sup −1}M{sub ⊙}⩽M{sub min}⩽10{sup 11.1}h{sup −1}M{sub ⊙}, 10{sup 11.0}h{sup −1}M{sub ⊙}⩽M{sub max}⩽10{sup 13.0}h{sup −1}M{sub ⊙}, followed by a wide range in the occupation fraction 0.02⩽f{sub occ}⩽0.30. As a consequence, the median mass, M {sub 50}, of all the consistent models has a large uncertainty M{sub 50}=3.16{sub −2.37}{sup +9.34}×10{sup 10} h{sup −1}M{sub ⊙}. However, we find that the same individual models have a relatively tight 1σ scatter around the median mass ΔM{sub 1σ}=0.55{sub −0.31}{sup +0.11} dex. We are also able to show that f{sub occ} is uniquely determined by M{sub min}, regardless of M{sub max}. We argue that upcoming large surveys covering at least 25 deg{sup 2} should be able to put tighter constraints on M{sub min} and f{sub occ} through the LAE number density distribution width constructed over several fields of ∼1 deg{sup 2}.

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Action-based dynamical modeling for the Milky Way disk

We present RoadMapping, a full-likelihood dynamical modeling machinery that aims to recover the Milky Way’s (MW) gravitational potential from large samples of stars in the Galactic disk. RoadMapping models the observed positions and velocities of stars with a parameterized, three-integral distribution function (DF) in a parameterized axisymmetric potential. We investigate through differential test cases with idealized mock data how the breakdown of model assumptions and data properties affect constraints on the potential and DF. Our key results are: (i) If the MW’s true potential is not included in the assumed model potential family, we can—in the axisymmetric case—still find a robust estimate for the potential, with only ≲10% difference in surface density within |z|⩽1.1 kpc inside the observed volume. (ii) Modest systematic differences between the true and model DF are inconsequential. E.g., when binning stars to define sub-populations with simple DFs, binning errors do not affect the modeling as long as the DF parameters of neighboring bins differ by <20%. In addition, RoadMapping ensures unbiased potential estimates for either (iii) small misjudgements of the spatial selection function (i.e., ≲15% at the survey volume’s edge), (iv) if distances are known to within 10%, or (v) if proper motion uncertainties are known within 10% or are smaller than δμ≲1 mas yr{sup −1}. Challenges are the rapidly increasing computational costs for large sample sizes. Overall, RoadMapping is well suited to making precise new measurements of the MW’s potential with data from the upcoming Gaia releases.

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Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Machine Learning

We study dynamical mass measurements of galaxy clusters contaminated by interlopers and show that a modern machine learning algorithm can predict masses by better than a factor of two compared to a standard scaling relation approach. We create two mock catalogs from Multidark's publicly available N-body MDPL1 simulation, one with perfect galaxy cluster membership information and the other where a simple cylindrical cut around the cluster center allows interlopers to contaminate the clusters. In the standard approach, we use a power-law scaling relation to infer cluster mass from galaxy line-of-sight (LOS) velocity dispersion. Assuming perfect membership knowledge, this unrealistic case produces a wide fractional mass error distribution, with a width of ${\rm{\Delta }}\epsilon \approx 0.87$. Interlopers introduce additional scatter, significantly widening the error distribution further (${\rm{\Delta }}\epsilon \approx 2.13$). We employ the support distribution machine (SDM) class of algorithms to learn from distributions of data to predict single values. Applied to distributions of galaxy observables such as LOS velocity and projected distance from the cluster center, SDM yields better than a factor-of-two improvement (${\rm{\Delta }}\epsilon \approx 0.67$) for the contaminated case. Remarkably, SDM applied to contaminated clusters is better able to recover masses than even the scaling relation approach applied to uncontaminated clusters. We show that the SDM method more accurately reproduces the cluster mass function, making it a valuable tool for employing cluster observations to evaluate cosmological models.

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Galaxy groups in the 2MASS redshift survey

A galaxy group catalog is constructed from the 2MASS Redshift Survey (2MRS) with the use of a halo-based group finder. The halo mass associated with a group is estimated using a “GAP” method based on the luminosity of the central galaxy and its gap with other member galaxies. Tests using mock samples show that this method is reliable, particularly for poor systems containing only a few members. On average, 80% of all the groups have completeness >0.8, and about 65% of the groups have zero contamination. Halo masses are estimated with a typical uncertainty of ∼0.35 dex. The application of the group finder to the 2MRS gives 29,904 groups from a total of 43,246 galaxies at z⩽0.08, with 5286 groups having two or more members. Some basic properties of this group catalog is presented, and comparisons are made with other group catalogs in overlap regions. With a depth to z∼0.08 and uniformly covering about 91% of the whole sky, this group catalog provides a useful database to study galaxies in the local cosmic web, and to reconstruct the mass distribution in the local universe.

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BOW TIES IN THE SKY. I. THE ANGULAR STRUCTURE OF INVERSE COMPTON GAMMA-RAY HALOS IN THE FERMI SKY

Extended inverse Compton halos are generally anticipated around extragalactic sources of gamma rays with energies above 100 GeV. These result from inverse Compton scattered cosmic microwave background photons by a population of high-energy electron/positron pairs produced by the annihilation of the high-energy gamma rays on the infrared background. Despite the observed attenuation of the high-energy gamma rays, the halo emission has yet to be directly detected. Here, we demonstrate that in most cases these halos are expected to be highly anisotropic, distributing the upscattered gamma rays along axes defined either by the radio jets of the sources or oriented perpendicular to a global magnetic field. We present a pedagogical derivation of the angular structure in the inverse Compton halo and provide an analytic formalism that facilitates the generation of mock images. We discuss exploiting this fact for the purpose of detecting gamma-ray halos in a set of companion papers.

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BAYESIAN TECHNIQUES FOR COMPARING TIME-DEPENDENT GRMHD SIMULATIONS TO VARIABLE EVENT HORIZON TELESCOPE OBSERVATIONS

The Event Horizon Telescope (EHT) is a millimeter-wavelength, very-long-baseline interferometry (VLBI) experiment that is capable of observing black holes with horizon-scale resolution. Early observations have revealed variable horizon-scale emission in the Galactic Center black hole, Sagittarius A* (Sgr A*). Comparing such observations to time-dependent general relativistic magnetohydrodynamic (GRMHD) simulations requires statistical tools that explicitly consider the variability in both the data and the models. We develop here a Bayesian method to compare time-resolved simulation images to variable VLBI data, in order to infer model parameters and perform model comparisons. We use mock EHT data based on GRMHD simulations to explore the robustness of this Bayesian method and contrast it to approaches that do not consider the effects of variability. We find that time-independent models lead to offset values of the inferred parameters with artificially reduced uncertainties. Moreover, neglecting the variability in the data and the models often leads to erroneous model selections. We finally apply our method to the early EHT data on Sgr A*.

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BAYESIAN SEMI-BLIND COMPONENT SEPARATION FOR FOREGROUND REMOVAL IN INTERFEROMETRIC 21 cm OBSERVATIONS

In this paper, we present a new Bayesian semi-blind approach for foreground removal in observations of the 21 cm signal measured by interferometers. The technique, which we call H i Expectation–Maximization Independent Component Analysis (HIEMICA), is an extension of the Independent Component Analysis technique developed for two-dimensional (2D) cosmic microwave background maps to three-dimensional (3D) 21 cm cosmological signals measured by interferometers. This technique provides a fully Bayesian inference of power spectra and maps and separates the foregrounds from the signal based on the diversity of their power spectra. Relying only on the statistical independence of the components, this approach can jointly estimate the 3D power spectrum of the 21 cm signal, as well as the 2D angular power spectrum and the frequency dependence of each foreground component, without any prior assumptions about the foregrounds. This approach has been tested extensively by applying it to mock data from interferometric 21 cm intensity mapping observations under idealized assumptions of instrumental effects. We also discuss the impact when the noise properties are not known completely. As a first step toward solving the 21 cm power spectrum analysis problem, we compare the semi-blind HIEMICA technique to the commonly used Principal Component Analysis. Under the same idealized circumstances, the proposed technique provides significantly improved recovery of the power spectrum. This technique can be applied in a straightforward manner to all 21 cm interferometric observations, including epoch of reionization measurements, and can be extended to single-dish observations as well.

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Rotational Spectral Unmixing of Exoplanets: Degeneracies between Surface Colors and Geography

Unmixing the disk-integrated spectra of exoplanets provides hints about heterogeneous surfaces that we cannot directly resolve in the foreseeable future. It is particularly important for terrestrial planets with diverse surface compositions like Earth. Although previous work on unmixing the spectra of Earth from disk-integrated multi-band light curves appeared successful, we point out a mathematical degeneracy between the surface colors and their spatial distributions. Nevertheless, useful constraints on the spectral shape of individual surface types may be obtained from the premise that albedo is everywhere between 0 and 1. We demonstrate the degeneracy and the possible constraints using both mock data based on a toy model of Earth, as well as real observations of Earth. Despite the severe degeneracy, we are still able to recover an approximate albedo spectrum for an ocean. In general, we find that surfaces are easier to identify when they cover a large fraction of the planet and when their spectra approach zero or unity in certain bands.

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Approximating Photo- z PDFs for Large Surveys

Modern galaxy surveys produce redshift probability density functions (PDFs) in addition to traditional photometric redshift (photo-z) point estimates. However, the storage of photo-z PDFs may present a challenge with increasingly large catalogs, as we face a trade-off between the accuracy of subsequent science measurements and the limitation of finite storage resources. This work presents qp, a Python package for manipulating parameterizations of one-dimensional PDFs, as suitable for photo-z PDF compression. We use qp to investigate the performance of three simple PDF storage formats (quantiles, samples, and step functions) as a function of the number of stored parameters on two realistic mock data sets, representative of upcoming surveys with different data qualities. We propose some best practices for choosing a photo-z PDF approximation scheme and demonstrate the approach on a science case using performance metrics on both ensembles of individual photo-z PDFs and an estimator of the overall redshift distribution function. We show that both the properties of the set of PDFs we wish to approximate and the fidelity metric(s) chosen affect the optimal parameterization. Additionally, we find that quantiles and samples outperform step functions, and we encourage further consideration of these formats for PDF approximation.

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nbodykit: An Open-source, Massively Parallel Toolkit for Large-scale Structure

We present nbodykit, an open-source, massively parallel Python toolkit for analyzing large-scale structure (LSS) data. Using Python bindings of the Message Passing Interface, we provide parallel implementations of many commonly used algorithms in LSS. nbodykit is both an interactive and scalable piece of scientific software, performing well in a supercomputing environment while still taking advantage of the interactive tools provided by the Python ecosystem. Existing functionality includes estimators of the power spectrum, two- and three-point correlation functions, a friends-of-friends grouping algorithm, mock catalog creation via the halo occupation distribution technique, and approximate N-body simulations via the FastPM scheme. The package also provides a set of distributed data containers, insulated from the algorithms themselves, that enables nbodykit to provide a unified treatment of both simulation and observational data sets. nbodykit can be easily deployed in a high-performance computing environment, overcoming some of the traditional difficulties of using Python on supercomputers. We provide performance benchmarks illustrating the scalability of the software. The modular, component-based approach of nbodykit allows researchers to easily build complex applications using its tools. The package is extensively documented at http://nbodykit.readthedocs.io, which also includes an interactive set of example recipes for new users to explore. As open-source software, we hope nbodykit provides a common framework for the community to use and develop in confronting the analysis challenges of future LSS surveys.

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The Photometric LSST Astronomical Time-series Classification Challenge PLAsTiCC: Selection of a Performance Metric for Classification Probabilities Balancing Diverse Science Goals

Classification of transient and variable light curves is an essential step in using astronomical observations to develop an understanding of the underlying physical processes from which they arise. However, upcoming deep photometric surveys, including the Large Synoptic Survey Telescope (LSST), will produce a deluge of low signal-to-noise data for which traditional type estimation procedures are inappropriate. Probabilistic classification is more appropriate for such data but is incompatible with the traditional metrics used on deterministic classifications. Furthermore, large survey collaborations like LSST intend to use the resulting classification probabilities for diverse science objectives, indicating a need for a metric that balances a variety of goals. We describe the process used to develop an optimal performance metric for an open classification challenge that seeks to identify probabilistic classifiers that can serve many scientific interests. The Photometric LSST Astronomical Time-series Classification Challenge (PLASTICC) aims to identify promising techniques for obtaining classification probabilities of transient and variable objects by engaging a broader community beyond astronomy. Using mock classification probability submissions emulating realistically complex archetypes of those anticipated of PLASTICC, we compare the sensitivity of two metrics of classification probabilities under various weighting schemes, finding that both yield results that are qualitatively consistent with intuitive notions of classification performance. We thus choose as a metric for PLASTICC a weighted modification of the cross-entropy because it can be meaningfully interpreted in terms of information content. Finally, we propose extensions of our methodology to ever more complex challenge goals and suggest some guiding principles for approaching the choice of a metric of probabilistic data products.

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Brought to Light. I. Quantification of Disk Substructure in Dwarf Early-type Galaxies

Dwarf early-type galaxies (ETGs) display a rich diversity in their photometric, structural, and dynamical properties. In this work, we address their structural complexity by studying with deep imaging a sample of nine dwarf ETGs from the Virgo galaxy cluster, characterized by having faint disk features such as bars and spiral arms, that lie mostly hidden within the bright diffuse light of the galaxies. We present a new, robust method that aims to identify and extract the disk substructure embedded in these dwarf ETGs. The method consists in an iterative procedure that gradually separates a galaxy image into two components: the bright, dominant, diffuse component, and the much fainter, underlying disk component. By applying it to the dwarf ETG sample, we quantify their disk substructure and find that its relative contribution to the total galaxy light ranges between 2.2% and 6.4% within two effective radii. We test the reliability of the method, and prove that it is accurate in recovering the disk substructure we introduce in mock galaxy images, even at low disk-to-total light fractions of a few percent. As a potential application of the method, we perform a Fourier analysis on the extracted disk substructures and measure the orientation, length, and strength of the bars, as well as the pitch angle and strength of the spiral arms. We also briefly discuss a scenario based on the numerical simulations presented in our companion paper, Brought to Light II: Smith et al., in which we investigate the origins of the substructure in such dwarf systems.

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Mapping the real-space distributions of galaxies in SDSS DR7. I. Two-point correlation functions

Using a method to correct redshift-space distortion (RSD) for individual galaxies, we mapped the real-space distributions of galaxies in the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7). We use an ensemble of mock catalogs to demonstrate the reliability of our method. Here, in the first paper in a series, we focus mainly on the two-point correlation function (2PCF) of galaxies. Overall the 2PCF measured in the reconstructed real space for galaxies brighter than {sup 0.1}M{sub r}−5logh=−19.0 agrees with the direct measurement to an accuracy better than the measurement error due to cosmic variance, if the reconstruction uses the correct cosmology. Applying the method to the SDSS DR7, we construct a real-space version of the main galaxy catalog, which contains 396,068 galaxies in the North Galactic Cap with redshifts in the range 0.01 ≤ z ≤ 0.12. The Sloan Great Wall, the largest known structure in the nearby universe, is not as dominant an overdense structure as it appears to be in redshift space. We measure the 2PCFs in reconstructed real space for galaxies of different luminosities and colors. All of them show clear deviations from single power-law forms, and reveal clear transitions from one-halo to two-halo terms. A comparison with the corresponding 2PCFs in redshift space nicely demonstrates how RSDs boost the clustering power on large scales (by about 40%–50% at scales ∼10 h{sup −1} Mpc) and suppress it on small scales (by about 70%–80% on a scale of 0.3 h{sup −1} Mpc).

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Measurement of a redshift-space power spectrum for BOSS galaxies and the growth rate at redshift 0.57

We present a measurement of the two-dimensional (2D) redshift-space power spectrum for the Baryon Oscillation Spectroscopic Survey Data Release 11 CMASS galaxies in the northern Galactic cap based on the method developed by Jing and Börner. In this method, we first measure the 2D redshift-space correlation function and obtain the 2D power spectrum based on Fourier transform of the correlation function. The method is tested with an N-body mock galaxy catalog, which demonstrates that the method can yield an accurate and unbiased measurement of the redshift-space power spectrum given that the input 2D correlation function is correct. Compared with previous measurements in literature that are usually based on direct Fourier transform in redshift space, our method has the advantages that the window function and shot noise are fully corrected. Thus, our measurement can facilitate a direct comparison with the theoretical predictions. Our 2D power spectrum, by construction, can reproduce the 2D correlation function, and it can reproduce, for example, the 2D power spectrum of Beutler et al. accurately if ours is convolved with the window function they provided. We then develop a method to measure the structure growth rate, by separating the anisotropic redshift-space power spectrum from the isotropic real-space power spectrum. We have carefully corrected for the nonlinearities in the mapping from real space to redshift space, according to the theoretical model of Zhang et al. Finally, we obtain the measurement of structure growth rate f(z{sub eff})σ {sub 8}(z{sub eff}) = 0.438 ± 0.037 at the effective redshift z{sub eff} = 0.57. The result is useful for constraining cosmological parameters. The measurements of the 2D power spectrum will be released soon.

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Probing WHIM around galaxy clusters with fast radio bursts and the Sunyaev–Zel’dovich effect

We propose a new method to probe the warm hot intergalactic medium (WHIM) beyond the virial radius (R{sub 200}) of a cluster of galaxies, where X-ray observations are not easily achievable. In this method, we use dispersion measures (DMs) of fast radio bursts (FRBs) that appear behind the cluster and the Sunyaev–Zel’dovich (SZ) effect toward the cluster. The DMs reflect the density of the intracluster medium including the WHIM. If we observe a sufficient number of FRBs in the direction of the cluster, we can derive the density profile from the DMs. Similarly, we can derive the pressure profile from the SZ effect. By combining the density and the pressure profiles, the temperature profile can be obtained. Based on mock observations of nearby clusters, we find that the density of the WHIM will be determined even at >2 R{sub 200} from the cluster center when FRB observations with the Square Kilometre Array become available. The temperature can be derived out to r∼1.5 R{sub 200}, and the radius is limited by the current sensitivity of SZ observations.

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Quantifying Environmental and Line-of-sight Effects in Models of Strong Gravitational Lens Systems

Matter near a gravitational lens galaxy or projected along the line of sight (LOS) can affect strong lensing observables by more than contemporary measurement errors. We simulate lens fields with realistic three-dimensional mass configurations (self-consistently including voids), and then fit mock lensing observables with increasingly complex lens models to quantify biases and uncertainties associated with different ways of treating the lens environment (ENV) and LOS. We identify the combination of mass, projected offset, and redshift that determines the importance of a perturbing galaxy for lensing. Foreground structures have a stronger effect on the lens potential than background structures, due to nonlinear effects in the foreground and downweighting in the background. There is dramatic variation in the net strength of ENV/LOS effects across different lens fields; modeling fields individually yields stronger priors for H {sub 0} than ray tracing through N-body simulations. Models that ignore mass outside the lens yield poor fits and biased results. Adding external shear can account for tidal stretching from galaxies at redshifts z⩾z{sub lens}, but it requires corrections for external convergence and cannot reproduce nonlinear effects from foreground galaxies. Using the tidal approximation is reasonable for most perturbers as long as nonlinear redshift effects are included. Even then, the scatter in H {sub 0} is limited by the lens profile degeneracy. Asymmetric image configurations produced by highly elliptical lens galaxies are less sensitive to the lens profile degeneracy, so they offer appealing targets for precision lensing analyses in future surveys like LSST and Euclid.

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The Stellar Initial Mass Function in Early-type Galaxies from Absorption Line Spectroscopy. IV. A Super-Salpeter IMF in the Center of NGC 1407 from Non-parametric Models

It is now well-established that the stellar initial mass function (IMF) can be determined from the absorption line spectra of old stellar systems, and this has been used to measure the IMF and its variation across the early-type galaxy population. Previous work focused on measuring the slope of the IMF over one or more stellar mass intervals, implicitly assuming that this is a good description of the IMF and that the IMF has a universal low-mass cutoff. In this work we consider more flexible IMFs, including two-component power laws with a variable low-mass cutoff and a general non-parametric model. We demonstrate with mock spectra that the detailed shape of the IMF can be accurately recovered as long as the data quality is high (S/N ≳ 300 Å{sup −1}) and cover a wide wavelength range (0.4–1.0 μ m). We apply these flexible IMF models to a high S/N spectrum of the center of the massive elliptical galaxy NGC 1407. Fitting the spectrum with non-parametric IMFs, we find that the IMF in the center shows a continuous rise extending toward the hydrogen-burning limit, with a behavior that is well-approximated by a power law with an index of −2.7. These results provide strong evidence for the existence of extreme (super-Salpeter) IMFs in the cores of massive galaxies.

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