arXiv subjects

Jean-Paul Kneib

Publications and source records attributed to Jean-Paul Kneib.

At least 109 records · Page 6Linked to original sources

arXiv2020

Unveiling the Intrinsic Alignment of Galaxies with Self-Calibration and DECaLS DR3 data

Galaxy intrinsic alignment (IA) is both a source of systematic contamination to cosmic shear measurement and its cosmological applications, and a source of valuable information on the large scale structure of the universe and galaxy formation. The self-calibration (SC) method \citep{SC2008} was designed to separate IA from cosmic shear, free of IA modeling. It was first successfully applied to the KiDS450 and KV450 data \citep{Yao2019}. We apply the SC method to the DECaLS DR3 shear + photo-z catalog and significantly improve the IA detection to $\sim 14σ$. We find a strong dependence of IA on galaxy color, with strong IA signal ($\sim17.6σ$) for red galaxies, while the IA signal for blue galaxies is consistent with zero. The detected IA for red galaxies are in reasonable agreement with the non-linear tidal alignment model and the inferred IA amplitude increases with redshift. We address the systematics in the SC method carefully and performed several sanity checks. We discuss various caveats and possible improvements in the measurement, theory and parameter fitting that will be addressed in future works.

astro-ph.CO↗

arXiv2020

LESSER: A Catalogue of Spectroscopically selected sample of Lyman-$α$ Emitters Lensed By Galaxies

We introduce the LEnSed laeS in the Eboss suRvey (LESSER) project, which aims to search for lensed Lyman-$α$ Emitters (LAEs) in the Extended Baryon Oscillation Spectroscopic Survey (eBOSS). The final catalog contains 361 candidate lensing systems. The lens galaxies are luminous red galaxies (LRGs) at redshift $0.4 < z < 0.8$, and the source galaxies are LAEs at redshift $2 < z < 3$. The spectral resolution of eBOSS ($\sim$2000) allows us to further identify the fine structures of Lyman-$α$ ($\rm Lyα$) emissions. Among our lensed LAE candidates, 281 systems present single-peaked line profiles while 80 systems show double-peaked features. Future spectroscopic/imaging follow-up observations of the catalog may shed light on the origin of diverse $\rm Lyα$ line morphology, and provide promising labs for studying low mass dark matter haloes/subhaloes.

astro-ph.GA↗

arXiv2020

The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Large-scale Structure Catalogs for Cosmological Analysis

We present large-scale structure catalogs from the completed extended Baryon Oscillation Spectroscopic Survey (eBOSS). Derived from Sloan Digital Sky Survey (SDSS) -IV Data Release 16 (DR16), these catalogs provide the data samples, corrected for observational systematics, and random positions sampling the survey selection function. Combined, they allow large-scale clustering measurements suitable for testing cosmological models. We describe the methods used to create these catalogs for the eBOSS DR16 Luminous Red Galaxy (LRG) and Quasar samples. The quasar catalog contains 343,708 redshifts with $0.8 < z < 2.2$ over 4,808\,deg$^2$. We combine 174,816 eBOSS LRG redshifts over 4,242\,deg$^2$ in the redshift interval $0.6 < z < 1.0$ with SDSS-III BOSS LRGs in the same redshift range to produce a combined sample of 377,458 galaxy redshifts distributed over 9,493\,deg$^2$. Improved algorithms for estimating redshifts allow that 98 per cent of LRG observations result in a successful redshift, with less than one per cent catastrophic failures ($Δz > 1000$ ${\rm km~s}^{-1}$). For quasars, these rates are 95 and 2 per cent (with $Δz > 3000$ ${\rm km~s}^{-1}$). We apply corrections for trends between the number densities of our samples and the properties of the imaging and spectroscopic data. For example, the quasar catalog obtains a $χ^2$/DoF$= 776/10$ for a null test against imaging depth before corrections and a $χ^2$/DoF$=6/8$ after. The catalogs, combined with careful consideration of the details of their construction found here-in, allow companion papers to present cosmological results with negligible impact from observational systematic uncertainties.

astro-ph.CO↗

arXiv2020

The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: measurement of the BAO and growth rate of structure of the luminous red galaxy sample from the anisotropic correlation function between redshifts 0.6 and 1

We present the cosmological analysis of the configuration-space anisotropic clustering in the completed Sloan Digital Sky Survey IV (SDSS-IV) extended Baryon Oscillation Spectroscopic Survey (eBOSS) DR16 galaxy sample. This sample consists of luminous red galaxies (LRGs) spanning the redshift range $0.6 < z < 1$, at an effective redshift of $z_{\rm eff}=0.698$. It combines 174 816 eBOSS LRGs and 202 642 BOSS CMASS galaxies. We extract and model the baryon acoustic oscillations (BAO) and redshift-space distortions (RSD) features from the galaxy two-point correlation function to infer geometrical and dynamical cosmological constraints. The adopted methodology is extensively tested on a set of realistic simulations. The correlations between the inferred parameters from the BAO and full-shape correlation function analyses are estimated. This allows us to derive joint constraints on the three cosmological parameter combinations: $D_M(z)/r_d$, $D_H(z)/r_d$ and $fσ_8(z)$, where $D_M$ is the comoving angular diameter distance, $D_H$ is Hubble distance, $r_d$ is the comoving BAO scale, $f$ is the linear growth rate of structure, and $σ_8$ is the amplitude of linear matter perturbations. After combining the results with those from the parallel power spectrum analysis of Gil-Marin et al. 2020, we obtain the constraints: $D_M/r_d = 17.65 \pm 0.30$, $D_H/r_d = 19.77 \pm 0.47$, $fσ_8 = 0.473 \pm 0.044$. These measurements are consistent with a flat $Λ$CDM model with standard gravity.

astro-ph.CO↗

arXiv2020

The distribution of dark matter and gas spanning six megaparsecs around the post-merger galaxy cluster MS0451-03

Using the largest mosaic of Hubble Space Telescope images around a galaxy cluster, we map the distribution of dark matter throughout a $\sim$$6\times6$ Mpc$^2$ area centred on the cluster MS 0451-03 ($z=0.54$, $M_{200}=1.65\times10^{15} \rm{M}_\odot$). Our joint strong- and weak-lensing analysis shows three possible filaments extending from the cluster, encompassing six group-scale substructures. The dark-matter distribution in the cluster core is elongated, consists of two distinct components, and is characterized by a concentration parameter of $c_{200}=3.79\pm0.36$. By contrast, XMM-Newton observations show the gas distribution to be more spherical, with excess entropy near the core, and a lower concentration of $c_{200}=2.35^{+0.89}_{-0.70}$ (assuming hydrostatic equilibrium). Such a configuration is predicted in simulations of major mergers 2-7Gyr after the first core passage, when the two dark-matter halos approach second turnaround, and before their gas has relaxed. This post-merger scenario finds further support in optical spectroscopy of the cluster's member galaxies, which shows that star formation was abruptly quenched 5 Gyr ago. MS 0451-03 will be an ideal target for future studies of the growth of structure along filaments, star-formation processes after a major merger, and the late-stage evolution of cluster collisions.

astro-ph.CO↗

arXiv2020

The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Growth rate of structure measurement from anisotropic clustering analysis in configuration space between redshift 0.6 and 1.1 for the Emission Line Galaxy sample

We present the anisotropic clustering of emission line galaxies (ELGs) from the Sloan Digital Sky Survey IV (SDSS-IV) extended Baryon Oscillation Spectroscopic Survey (eBOSS) Data Release 16 (DR16). Our sample is composed of 173,736 ELGs covering an area of 1170 deg$^2$ over the redshift range $0.6 \leq z \leq 1.1$. We use the Convolution Lagrangian Perturbation Theory in addition to the Gaussian Streaming Redshift-Space Distortions to model the Legendre multipoles of the anisotropic correlation function. We show that the eBOSS ELG correlation function measurement is affected by the contribution of a radial integral constraint that needs to be modelled to avoid biased results. To mitigate the effect from unknown angular systematics, we adopt a modified correlation function estimator that cancels out the angular modes from the clustering. At the effective redshift, $z_{\rm eff}=0.85$, including statistical and systematical uncertainties, we measure the linear growth rate of structure $fσ_8(z_{\rm eff}) = 0.35\pm0.10$, the Hubble distance $D_H(z_{\rm eff})/r_{\rm drag} = 19.1^{+1.9}_{-2.1}$ and the comoving angular diameter distance $D_M(z_{\rm eff})/r_{\rm drag} = 19.9\pm1.0$. These results are in agreement with the Fourier space analysis, leading to consensus values of: $fσ_8(z_{\rm eff}) = 0.315\pm0.095$, $D_H(z_{\rm eff})/r_{\rm drag} = 19.6^{+2.2}_{-2.1}$ and $D_M(z_{\rm eff})/r_{\rm drag} = 19.5\pm1.0$, consistent with $Λ$CDM model predictions with Planck parameters.

astro-ph.CO↗

arXiv2020

The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Large-scale Structure Catalogues and Measurement of the isotropic BAO between redshift 0.6 and 1.1 for the Emission Line Galaxy Sample

We present the Emission Line Galaxy (ELG) sample of the extended Baryon Oscillation Spectroscopic Survey (eBOSS) from the Sloan Digital Sky Survey IV Data Release 16 (DR16). After describing the observations and redshift measurement for the 269,243 observed ELG spectra over 1170 deg$^2$, we present the large-scale structure catalogues, which are used for the cosmological analysis. These catalogues contain 173,736 reliable spectroscopic redshifts between 0.6 and 1.1, along with the associated random catalogues quantifying the extent of observations, and the appropriate weights to correct for non-cosmological fluctuations. We perform a spherically averaged baryon acoustic oscillations (BAO) measurement in configuration space, with density field reconstruction: the data 2-point correlation function shows a feature consistent with that of the BAO, providing a 3.2-percent measurement of the spherically averaged BAO distance $D_V(z_{\rm eff})/r_{\rm drag} = 18.23\pm 0.58$ at the effective redshift $z_{\rm eff}=0.845$.

astro-ph.CO↗

arXiv2020

The Sloan Digital Sky Survey Reverberation Mapping Project: Photometric g and i Light Curves

The Sloan Digital Sky Survey Reverberation Mapping (SDSS-RM) program monitors 849 active galactic nuclei (AGN) both spectroscopically and photometrically. The photometric observations used in this work span over four years and provide an excellent baseline for variability studies of these objects. We present the photometric light curves from 2014 to 2017 obtained by the Steward Observatory's Bok telescope and the CFHT telescope with MegaCam. We provide details on the data acquisition and processing of the data from each telescope, the difference imaging photometry used to produce the light curves, and the calculation of a variability index to quantify each AGN's variability. We find that the Welch-Stetson J-index provides a useful characterization of AGN variability and can be used to select AGNs for further study.

astro-ph.GA↗

arXiv2020

Data-Driven Convergence Prediction of Astrobots Swarms

Astrobots are robotic artifacts whose swarms are used in astrophysical studies to generate the map of the observable universe. These swarms have to be coordinated with respect to various desired observations. Such coordination are so complicated that distributed swarm controllers cannot always coordinate enough astrobots to fulfill the minimum data desired to be obtained in the course of observations. Thus, a convergence verification is necessary to check the suitability of a coordination before its execution. However, a formal verification method does not exist for this purpose. In this paper, we instead use machine learning to predict the convergence of astrobots swarm. In particular, we propose a weighted $k$-NN-based algorithm which requires the initial status of a swarm as well as its observational targets to predict its convergence. Our algorithm learns to predict based on the coordination data obtained from previous coordination of the desired swarm. This method first generates a convergence probability for each astrobot based on a distance metric. Then, these probabilities are transformed to either a complete or an incomplete categorical result. The method is applied to two typical swarms including 116 and 487 astrobots. It turns out that the correct prediction of successful coordination may be up to 80% of overall predictions. Thus, these results witness the efficient accuracy of our predictive convergence analysis strategy.

cs.RO↗

arXiv2020

Complete coordination of robotic fiber positioners for massive spectroscopic surveys

Robotic fiber positioners play a vital role in the generation of massive spectroscopic surveys. The more complete a positioners set is coordinated, the more information its corresponding spectrograph receives during an observation. The complete coordination problem of positioners sets is studied in this paper. We first define the local and the global completeness problems and determine their relationship. We then propose a new artificial potential field according to which the convergences of a positioner and its neighboring positioners are cooperatively taken into account. We also discover the required condition for a complete coordination. We finally explain how the modifications of some of the parameters of a positioners set may resolve its incompleteness coordination scenarios. We verify our accomplishments using simulations.

astro-ph.IM↗

arXiv2020

Optimal target assignment for massive spectroscopic surveys

Robotics have recently contributed to cosmological spectroscopy to automatically obtain the map of the observable universe using robotic fiber positioners. For this purpose, an assignment algorithm is required to assign each robotic fiber positioner to a target associated with a particular observation. The assignment process directly impacts on the coordination of robotic fiber positioners to reach their assigned targets. In this paper, we establish an optimal target assignment scheme which simultaneously provides the fastest coordination accompanied with the minimum of colliding scenarios between robotic fiber positioners. In particular, we propose a cost function by whose minimization both of the cited requirements are taken into account in the course of a target assignment process. The applied simulations manifest the improvement of convergence rates using our optimal approach. We show that our algorithm scales the solution in quadratic time in the case of full observations. Additionally, the convergence time and the percentage of the colliding scenarios are also decreased in both supervisory and hybrid coordination strategies.

astro-ph.IM↗

arXiv2020

The e-MERLIN Galaxy Evolution Survey (e-MERGE): Overview and Survey Description

We present an overview and description of the eMERLIN Galaxy Evolution survey (eMERGE) Data Release 1 (DR1), a large program of high-resolution 1.5 GHz radio observations of the GOODS-N field comprising $\sim140$ hours of observations with eMERLIN and $\sim40$ hours with the Very Large Array (VLA). We combine the long baselines of eMERLIN (providing high angular resolution) with the relatively closely-packed antennas of the VLA (providing excellent surface brightness sensitivity) to produce a deep 1.5 GHz radio survey with the sensitivity ($\sim 1.5μ$Jy beam$^{-1}$), angular resolution ($0.2"$--$0.7"$) and field-of-view ($\sim15' \times 15'$) to detect and spatially resolve star-forming galaxies and AGN at $z\gtrsim 1$. The goal of eMERGE is to provide new constraints on the deep, sub-arcsecond radio sky which will be surveyed by SKA1-mid. In this initial publication, we discuss our data analysis techniques, including steps taken to model in-beam source variability over a $\sim20$ year baseline and the development of new point spread function/primary beam models to seamlessly merge eMERLIN and VLA data in the $uv$ plane. We present early science results, including measurements of the luminosities and/or linear sizes of $\sim500$ galaxes selected at 1.5 GHz. In combination with deep Hubble Space Telescope observations, we measure a mean radio-to-optical size ratio of $r_{\rm eMERGE}/r_{\rm HST}\sim1.02\pm0.03$, suggesting that in most high-redshift galaxies, the $\sim$GHz continuum emission traces the stellar light seen in optical imaging. This is the first in a series of papers which will explore the $\sim$kpc-scale radio properties of star-forming galaxies and AGN in the GOODS-N field observed by eMERGE DR1.

astro-ph.GA↗

arXiv2020

Lenstool-HPC: A High Performance Computing based mass modelling tool for cluster-scale gravitational lenses

With the upcoming generation of telescopes, cluster scale strong gravitational lenses will act as an increasingly relevant probe of cosmology and dark matter. The better resolved data produced by current and future facilities requires faster and more efficient lens modeling software. Consequently, we present Lenstool-HPC, a strong gravitational lens modeling and map generation tool based on High Performance Computing (HPC) techniques and the renowned Lenstool software. We also showcase the HPC concepts needed for astronomers to increase computation speed through massively parallel execution on supercomputers. Lenstool-HPC was developed using lens modelling algorithms with high amounts of parallelism. Each algorithm was implemented as a highly optimised CPU, GPU and Hybrid CPU-GPU version. The software was deployed and tested on the Piz Daint cluster of the Swiss National Supercomputing Centre (CSCS). Lenstool-HPC perfectly parallel lens map generation and derivative computation achieves a factor 30 speed-up using only 1 GPUs compared to Lenstool. Lenstool-HPC hybrid Lens-model fit generation tested at Hubble Space Telescope precision is scalable up to 200 CPU-GPU nodes and is faster than Lenstool using only 4 CPU-GPU nodes.

astro-ph.IM↗

arXiv2020

The BUFFALO HST Survey

The Beyond Ultra-deep Frontier Fields and Legacy Observations (BUFFALO) is a 101 orbit + 101 parallel Cycle 25 Hubble Space Telescope Treasury program taking data from 2018-2020. BUFFALO will expand existing coverage of the Hubble Frontier Fields (HFF) in WFC3/IR F105W, F125W, and F160W and ACS/WFC F606W and F814W around each of the six HFF clusters and flanking fields. This additional area has not been observed by HST but is already covered by deep multi-wavelength datasets, including Spitzer and Chandra. As with the original HFF program, BUFFALO is designed to take advantage of gravitational lensing from massive clusters to simultaneously find high-redshift galaxies which would otherwise lie below HST detection limits and model foreground clusters to study properties of dark matter and galaxy assembly. The expanded area will provide a first opportunity to study both cosmic variance at high redshift and galaxy assembly in the outskirts of the large HFF clusters. Five additional orbits are reserved for transient followup. BUFFALO data including mosaics, value-added catalogs and cluster mass distribution models will be released via MAST on a regular basis, as the observations and analysis are completed for the six individual clusters.

astro-ph.GA↗

arXiv2020

hybrid-Lenstool: A self-consistent algorithm to model galaxy clusters with strong- and weak-lensing simultaneously

We present a new galaxy cluster lens modeling approach, hybrid-Lenstool, that is implemented in the publicly available modeling software Lenstool. hybrid-Lenstool combines a parametric approach to model the core of the cluster, and a non-parametric (free-form) approach to model the outskirts. hybrid-Lenstool optimizes both strong- and weak-lensing constraints simultaneously (Joint-Fit), providing a self-consistent reconstruction of the cluster mass distribution on all scales. In order to demonstrate the capabilities of the new algorithm, we tested it on a simulated cluster. hybrid-Lenstool yields more accurate reconstructed mass distributions than the former Sequential-Fit approach where the parametric and the non-parametric models are optimized successively. Indeed, we show with the simulated cluster that the mass density profile reconstructed with a Sequential-Fit deviates form the input by $2-3σ$ at all scales while the Joint-Fit gives a profile that is within $1-1.5σ$ of the true value. This gain in accuracy is consequential for recovering mass distributions exploiting cluster lensing and therefore for all applications of clusters as cosmological probes. Finally we found that the Joint-Fit approach yields shallower slope of the inner density profile than the Sequential-Fit approach, thus revealing possible biases in previous lensing studies.

astro-ph.CO↗

arXiv2019

The Mass-Metallicity Relation at $z\sim0.8$: Redshift Evolution and Parameter Dependency

The spectra of emission-line galaxies (ELGs) from the extended Baryon Oscillation Spectroscopic Survey (eBOSS) of the Sloan Digit Sky Survey (SDSS) are used to study the mass-metallicity relation (MZR) at $z\sim0.8$. The selected sample contains about 180,000 massive star-forming galaxies with $0.6 < z < 1.05$ and $9 < {\rm log}(M_{\star}/M_{\odot}) < 12$. The spectra are stacked in bins of different parameters including redshift, stellar mass, star formation rate (SFR), specific star formation rate (sSFR), half-light radius, mass density, and optical color. The average MZR at $z\sim0.83$ has a downward evolution in the MZR from local to high-redshift universe, which is consistent with previous works. At a specified stellar mass, galaxies with higher SFR/sSFR and larger half-light radius have systematically lower metallicity. This behavior is reversed for galaxies with larger mass density and optical color. Among the above physical parameters, the MZR has the most significant dependency on SFR. Our galaxy sample at $0.6<z<1.05$ approximately follows the fundamental metallicity relation (FMR) in the local universe, although the sample inhomogeneity and incompleteness might have effect on our MZR and FMR.

astro-ph.GA↗

arXiv2019

Molecular clouds in the Cosmic Snake normal star-forming galaxy 8 billion years ago

The cold molecular gas in contemporary galaxies is structured in discrete cloud complexes. These giant molecular clouds (GMCs), with $10^4$-$10^7$ solar masses and radii of 5-100 parsecs, are the seeds of star formation. Highlighting the molecular gas structure at such small scales in distant galaxies is observationally challenging. Only a handful of molecular clouds were reported in two extreme submillimetre galaxies at high redshift. Here we search for GMCs in a typical Milky Way progenitor at z = 1.036. Using the Atacama Large Millimeter/submillimeter Array (ALMA), we mapped the CO(4-3) emission of this gravitationally lensed galaxy at high resolution, reading down to 30 parsecs, which is comparable to the resolution of CO observations of nearby galaxies. We identify 17 molecular clouds, characterized by masses, surface densities and supersonic turbulence all of which are 10-100 times higher than present-day analogues. These properties question the universality of GMCs and suggest that GMCs inherit their properties from ambient interstellar medium. The measured cloud gas masses are similar to the masses of stellar clumps seen in the galaxy in comparable numbers. This corroborates the formation of molecular clouds by fragmentation of distant turbulent galactic gas disks, which then turn into stellar clumps ubiquitously observed in galaxies at cosmic noon.

astro-ph.GA↗

arXiv2019

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 (AGN), or low-ionization nuclear emission regions (LINERs) using optical spectra alone was impossible because the lines used for standard optical diagnostic diagrams: [NII], H$α$, and [SII] 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 multi-layer perceptron (MLP) neural network. For input features, we use properties that can be measured from optical galaxy spectra out to $z < 0.8$---[OIII]/H$β$, [OII]/H$β$, [OIII] 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 (AUC) score for classifying all four galaxy types, meaning 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.

astro-ph.GA↗