The Las Campanas Redshift Survey (LCRS) consists of 26418 redshifts of galaxies selected from a CCD-based catalog obtained in the $R$ band. The survey covers over 700 square degrees in 6 strips, each 1.5$\arcdeg$ x 80$\arcdeg$, three each in the North and South galactic caps. The median redshift in the survey is about 30000 km~s$^{-1}$. Essential features of the galaxy selection and redshift measurement methods are described and tabulated here. These details are important for subsequent analysis of the LCRS data. Two dimensional representations of the redshift distributions reveal many repetitions of voids, on the scale of about 5000 km~s$^{-1}$, sharply bounded by large walls of galaxies as seen in nearby surveys. Statistical investigations of the mean galaxy properties and of clustering on the large scale are reported elsewhere. These include studies of the luminosity function, power spectrum in two and three dimensions, correlation function, pairwise velocity distribution, identification of large scale structures, and a group catalog. The LCRS redshift catalog will be made available to interested investigators at an internet web site and in archival form as an Astrophysical Journal CD-ROM.

/pdf/the-las-campanas-redshift-survey-klbz32bfaw.pdf

The Las Campanas Redshift Survey

Author(s): Huang, L; Dey, A; Schlegel, DJ; Lang, D; Blum, R; Burleigh, K; Fan, X; Findlay, JR; Finkbeiner, D; Herrera, D; Juneau, S; Landriau, M; Levi, M; McGreer, I; Meisner, A; Myers, AD; Moustakas, J; Nugent, P; Patej, A; Schlafly, EF; Walker, AR; Valdes, F; Weaver, BA; Yeche, C; Zou, H; Zhou, X; Abareshi, B; Abbott, TMC; Abolfathi, B; Aguilera, C; Alam, S; Allen, L; Alvarez, A; Annis, J; Ansarinejad, B; Aubert, M; Beechert, J; Bell, EF; Benzvi, SY; Beutler, F; Bielby, RM; Bolton, AS; Briceno, C; Buckley-Geer, EJ; Butler, K; Calamida, A; Carlberg, RG; Carter, P; Casas, R; Castander, FJ; Choi, Y; Comparat, J; Cukanovaite, E; Delubac, T; Devries, K; Dey, S; Dhungana, G; Dickinson, M; Ding, Z; Donaldson, JB; Duan, Y; Duckworth, CJ; Eftekharzadeh, S; Eisenstein, DJ; Etourneau, T; Fagrelius, PA; Farihi, J; Fitzpatrick, M; Font-Ribera, A; Fulmer, L; Gansicke, BT; Gaztanaga, E; George, K; Gerdes, DW; A Gontcho, SG; Gorgoni, C; Green, G; Guy, J; Harmer, D; Hernandez, M; Honscheid, K; Huang, LW; James, DJ; Jannuzi, BT; Jiang, L | Abstract: © 2019. The American Astronomical Society. All rights reserved.. The DESI Legacy Imaging Surveys (http://legacysurvey.org/) are a combination of three public projects (the Dark Energy Camera Legacy Survey, the Beijing-Arizona Sky Survey, and the Mayall z-band Legacy Survey) that will jointly image ≈14,000 deg2 of the extragalactic sky visible from the northern hemisphere in three optical bands (g, r, and z) using telescopes at the Kitt Peak National Observatory and the Cerro Tololo Inter-American Observatory. The combined survey footprint is split into two contiguous areas by the Galactic plane. The optical imaging is conducted using a unique strategy of dynamically adjusting the exposure times and pointing selection during observing that results in a survey of nearly uniform depth. In addition to calibrated images, the project is delivering a catalog, constructed by using a probabilistic inference-based approach to estimate source shapes and brightnesses. The catalog includes photometry from the grz optical bands and from four mid-infrared bands (at 3.4, 4.6, 12, and 22 μm) observed by the Wide-field Infrared Survey Explorer satellite during its full operational lifetime. The project plans two public data releases each year. All the software used to generate the catalogs is also released with the data. This paper provides an overview of the Legacy Surveys project.

Overview of the DESI Legacy Imaging Surveys

Westudytheluminosityandcolordependenceofthegalaxytwo-pointcorrelationfunctionintheSloanDigitalSky Survey, starting from a sample of � 200,000 galaxies over 2500 deg 2 . We concentrate our analysis on volume-limited subsamples of specified luminosity ranges, for which we measure the projected correlation function wp(rp), which is directly related to the real-space correlation function � (r). The amplitude of wp(rp) rises continuously with luminosity from Mr �� 17: 5t oMr �� 22:5, with the most rapid increase occurring above the characteristic luminosity L� (Mr �� 20:5). Over the scales 0:1 h � 1 Mpc � 22 can be approximated, imperfectly, by power-law three-dimensional correlation functions � (r) ¼ (r/r0) � � with � � 1:8 and r0(L� ) � 5:0 h � 1 Mpc. The brightest subsample, � 23 < Mr < � 22, has a significantly steeper � (r). When we divide samples by color, redder galaxies exhibit a higher amplitude and steeper correlation function at all luminosities. The correlation amplitude of blue galaxies increases continuously with luminosity, but the luminosity dependence for red galaxies is less regular, with bright red galaxies exhibiting the strongest clustering at large scales and faint red galaxies exhibiting the strongest clustering at small scales. We interpret these results using halo occupation distribution (HOD) models assuming concordance cosmological parameters. For most samples, an HOD model with two adjustable parameters fits the wp(rp) data better than a power law, explaining inflections at rp � 1 3 h � 1 Mpc as the transition between the one-halo and two-halo regimes of � (r). The implied minimum mass for a halo hosting a central galaxy more luminous than L grows steadily, with Mmin / L at low luminosities and a steeper dependence above L� . The mass at which a halo has, on average, one satellite galaxy brighter than L is M1 � 23Mmin(L), at all luminosities. These results imply a conditional luminosity function (at fixed halo mass) in which central galaxies lie far above a Schechter function extrapolation of the satellite population. The HOD model fits nicely explain the color dependence of wp(rp) and the cross correlation between red and blue galaxies. For galaxies with Mr < � 21, halos slightly above Mmin have blue central galaxies, while more massive halos have red central galaxies and predominantly red satellite populations. The fraction of blue central galaxies increases steadily with decreasing luminosity and host halo mass. The strong clustering offaint red galaxies follows from the fact that nearly all of them are satellite systems in high-mass halos. The HOD fitting results are in good qualitative agreement with the predictions of numerical and semianalytic models of galaxy formation. Subject headingg cosmology: observations — cosmology: theory — galaxies: distances and redshifts — galaxies: halos — galaxies: statistics — large-scale structure of universe

The Luminosity and Color Dependence of the Galaxy Correlation Function

Abstract We provide a method for estimating the projected density distribution <?CDATA ${\bar{n}}_{2}{w}_{p}({r}_{p})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mover accent="true"> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>¯</mml:mo> </mml:mrow> </mml:mover> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> <mml:msub> <mml:mrow> <mml:mi>w</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>p</mml:mi> </mml:mrow> </mml:msub> <mml:mo stretchy="false">(</mml:mo> <mml:msub> <mml:mrow> <mml:mi>r</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>p</mml:mi> </mml:mrow> </mml:msub> <mml:mo stretchy="false">)</mml:mo> </mml:math> of photometric objects around spectroscopic objects in a spectroscopic survey. This quantity describes the distribution of photometric sources with certain physical properties (e.g., luminosity, mass, and color) around cosmic webs (PAC) traced by the spectroscopic objects. The method can make full use of current and future deep and wide photometric surveys to explore the formation of galaxies up to medium redshift ( z s &lt; 2) 3 3 Throughout the paper, we use z s for redshift, z for the z -band magnitude, z p for the photometric redshift. with the aid of cosmological spectroscopic surveys that sample only a fairly limited species of objects (e.g., emission line galaxies). As an example, we apply the PAC method to the CMASS spectroscopic and HSC-SSP PDR2 photometric samples to explore the distribution of galaxies for a wide range of stellar masses from 10 9.0 to 10 12.0 M ⊙ around massive galaxies at z s ≈ 0.6. Using the abundance-matching method, we model <?CDATA ${\bar{n}}_{2}{w}_{p}({r}_{p})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mover accent="true"> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>¯</mml:mo> </mml:mrow> </mml:mover> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msub> <mml:msub> <mml:mrow> <mml:mi>w</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>p</mml:mi> </mml:mrow> </mml:msub> <mml:mo stretchy="false">(</mml:mo> <mml:msub> <mml:mrow> <mml:mi>r</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>p</mml:mi> </mml:mrow> </mml:msub> <mml:mo stretchy="false">)</mml:mo> </mml:math> in N -body simulation using Markov chain Monte Carlo sampling, and we accurately measure the stellar–halo mass relation and stellar mass function for the whole mass range. We can also measure the conditional stellar mass function of satellites for central galaxies of different mass. The PAC method has many potential applications for studying the evolution of galaxies. 

Photometric Objects around Cosmic Webs (PAC) Delineated in a Spectroscopic Survey. I. Methods

In a novel approach employing implicit likelihood inference (ILI), also known as likelihood-free inference, we calibrate the parameters of cosmological hydrodynamic simulations against observations, which has previously been unfeasible due to the high computational cost of these simulations. For computational efficiency, we train neural networks as emulators on ∼1000 cosmological simulations from the CAMELS project to estimate simulated observables, taking as input the cosmological and astrophysical parameters, and use these emulators as surrogates for the cosmological simulations. Using the cosmic star formation rate density (SFRD) and, separately, the stellar mass functions (SMFs) at different redshifts, we perform ILI on selected cosmological and astrophysical parameters (Ω m , σ 8, stellar wind feedback, and kinetic black hole feedback) and obtain full six-dimensional posterior distributions. In the performance test, the ILI from the emulated SFRD (SMFs) can recover the target observables with a relative error of 0.17% (0.4%). We find that degeneracies exist between the parameters inferred from the emulated SFRD, confirmed with new full cosmological simulations. We also find that the SMFs can break the degeneracy in the SFRD, which indicates that the SMFs provide complementary constraints for the parameters. Further, we find that a parameter combination inferred from an observationally inferred SFRD reproduces the target observed SFRD very well, whereas, in the case of the SMFs, the inferred and observed SMFs show significant discrepancies that indicate potential limitations of the current galaxy formation modeling and calibration framework, and/or systematic differences and inconsistencies between observations of the SMFs.

Calibrating Cosmological Simulations with Implicit Likelihood Inference Using Galaxy Growth Observables

In a recent study, we developed a method that models the impact of photometric redshift uncertainty on the two-point correlation function (2PCF). Using this method, we simultaneously obtained both the intrinsic clustering strength and the photometric redshift errors by fitting the projected 2PCF with two integration depths along the line-of-sight. Herein, we apply this method to the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys Data Release 8 (LS DR8), which is the largest galaxy sample currently available. We separate the galaxies into 20 samples in eight redshift bins, from z = 0.1 to z = 1.0, and several z-band absolute magnitude bins, with Mz ≤ −20. These galaxies are further divided into red and blue subsamples according to their M
 r
 0.5
 - M
 z
 0.5
 colors. We measure the projected 2PCFs for all these galaxy subsamples and fit them using our photometric redshift 2PCF model. We find that the photometric redshift errors are smaller in the red subsamples than in the overall population. In contrast, some systematic photometric redshift errors exist in the blue subsamples, such that some of the subsamples show a significantly enhanced 2PCF on large scales. Therefore, separately focusing only on the red subsamples and on all the subsamples, we find that the biases of the galaxies in these subsamples exhibit clear color, redshift, and luminosity dependencies; the brighter red galaxies at higher redshift are more biased than their bluer and low-redshift counterparts. Apart from the best-fit set of parameters, σz and b, from this state-of-the-art photometric redshift survey, we obtain high-precision intrinsic clustering measurements for these 40 red- and all-galaxy subsamples. These measurements, on large and small scales, hold important information regarding cosmology and galaxy formation that will be used in our subsequent probes in this series.

The clustering of galaxies in the DESI imaging legacy surveys DR8: I. The luminosity and color dependent intrinsic clustering

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Kai Wang

Author Tools

Chat about Author

Papers

The clustering of galaxies in the DESI imaging legacy surveys DR8: I. The luminosity and color dependent intrinsic clustering