arXiv subjects

Daniel J. Eisenstein

Publications and source records attributed to Daniel J. Eisenstein.

At least 163 records · Page 9Linked to original sources

arXiv2021

Evidence for galaxy assembly bias in BOSS CMASS redshift-space galaxy correlation function

Building accurate and flexible galaxy-halo connection models is crucial in modeling galaxy clustering on non-linear scales. Recent studies have found that halo concentration by itself cannot capture the full galaxy assembly bias effect and that the local environment of the halo can be an excellent indicator of galaxy assembly bias. In this paper, we propose an extended halo occupation distribution model (HOD) that includes both a concentration-based assembly bias term and an environment-based assembly bias term. We use this model to achieve a good fit (chi^2/DoF = 1.35) on the 2D redshift-space 2-point correlation function (2PCF) of the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxy sample. We find that the inclusion of both assembly bias terms is strongly favored by the data and the standard 5-parameter HOD is strongly rejected. More interestingly, the redshift-space 2PCF drives the assembly bias parameters in a way that preferentially assigns galaxies to lower mass halos. This results in galaxy-galaxy lensing predictions that are within 1sigma agreement with the observation, alleviating the perceived tension between galaxy clustering and lensing. We also showcase a consistent 3-5sigma preference for a positive environment-based assembly bias that persists over variations in the fit. We speculate that the environmental dependence might be driven by underlying processes such as mergers and feedback, but might also be indicative of a larger halo boundaries such as the splashback radius. Regardless, this work highlights the importance of building flexible galaxy-halo connection models and demonstrates the extra constraining power of the redshift-space 2PCF.

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arXiv2021

The galaxy-halo connection of emission-line galaxies in IllustrisTNG

We employ the hydrodynamical simulation IllustrisTNG-300-1 to explore the halo occupation distribution (HOD) and environmental dependence of luminous star-forming emission-line galaxies (ELGs) at $z \sim 1$. Such galaxies are key targets for current and upcoming cosmological surveys. We select model galaxies through cuts in colour-colour space allowing for a direct comparison with the Extended Baryon Oscillation Spectroscopic Survey and the Dark Energy Spectroscopic Instrument (DESI) surveys and then compare them with galaxies selected based on specific star-formation rate (sSFR) and stellar mass. We demonstrate that the ELG populations are twice more likely to reside in lower-density regions (sheets) compared with the mass-selected populations and twice less likely to occupy the densest regions of the cosmic web (knots). We also show that the colour-selected and sSFR-selected ELGs exhibit very similar occupation and clustering statistics, finding that the agreement is best for lower redshifts. In contrast with the mass-selected sample, the occupation of haloes by a central ELG peaks at $\sim$20\%. We furthermore explore the dependence of the HOD and the auto-correlation on environment, noticing that at fixed halo mass, galaxies in high-density regions cluster about 10 times more strongly than low-density ones. This result suggests that we should model carefully the galaxy-halo relation and implement assembly bias effects into our models (estimated at $\sim$4\% of the clustering of the DESI colour-selected sample at $z = 0.8$). Finally, we apply a simple mock recipe to recover the clustering on large scales ($r \gtrsim 1 \ {\rm Mpc}/h$) to within 1\% by augmenting the HOD model with an environment dependence, demonstrating the power of adopting flexible population models.

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arXiv2020

The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: BAO and RSD measurements from anisotropic clustering analysis of the Quasar Sample in configuration space between redshift 0.8 and 2.2

We measure the anisotropic clustering of the quasar sample from Data Release 16 (DR16) of the Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey (eBOSS). A sample of $343,708$ spectroscopically confirmed quasars between redshift $0.8<z<2.2$ are used as tracers of the underlying dark matter field. In comparison with DR14 sample, the final sample doubles the number of objects as well as the survey area. In this paper, we present the analysis in configuration space by measuring the two-point correlation function and decompose using the Legendre polynomials. For the full-shape analysis of the Legendre multipole moments, we measure the BAO distance and the growth rate of the cosmic structure. At an effective redshift of $z_{\rm eff}=1.48$, we measure the comoving angular diameter distance $D_{\rm M}(z_{\rm eff})/r_{\rm drag} = 30.66\pm0.88$, the Hubble distance $D_{\rm H}(z_{\rm eff})/r_{\rm drag} = 13.11\pm0.52$, and the growth rate $fσ_8(z_{\rm eff}) = 0.439\pm0.048$. The accuracy of these measurements is confirmed using an extensive set of mock simulations developed for the quasar sample. The uncertainties on the distance and growth rate measurements have been reduced substantially ($\sim 45\%$ and $\sim30\%$) with respect to the DR14 results. We also perform a BAO-only analysis to cross check the robustness of the methodology of the full-shape analysis. Combining our analysis with the Fourier space analysis, we arrive at $D^{\bf{c}}_{\rm M}(z_{\rm eff})/r_{\rm drag} = 30.22 \pm 0.79$, $D^{\bf{c}}_{\rm H}(z_{\rm eff})/r_{\rm drag} = 13.26 \pm 0.47$, and $fσ_8^{\bf{c}}(z_{\rm eff}) = 0.464 \pm 0.045$.

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arXiv2020

Preliminary Target Selection for the DESI Quasar (QSO) Sample

The DESI survey will measure large-scale structure using quasars as direct tracers of dark matter in the redshift range $0.9 2.1$. We present two methods to select candidate quasars for DESI based on imaging in three optical ($g, r, z$) and two infrared ($W1, W2$) bands. The first method uses traditional color cuts and the second utilizes a machine-learning algorithm.

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arXiv2020

Preliminary Target Selection for the DESI Emission Line Galaxy (ELG) Sample

DESI will precisely constrain cosmic expansion and the growth of structure by collecting $\sim$35 million redshifts across $\sim$80% of cosmic history and one third of the sky to study Baryon Acoustic Oscillations (BAO) and Redshift Space Distortions (RSD). We present a preliminary target selection for an Emission Line Galaxy (ELG) sample, which will comprise about half of all DESI tracers. The selection consists of a $g$-band magnitude cut and a $(g-r)$ vs. $(r-z)$ color box, which we validate using HSC/PDR2 photometric redshifts and DEEP2 spectroscopy. The ELG target density should be $\sim$2400 deg$^{-2}$, with $\sim$65% of ELG redshifts reliably within a redshift range of $0.6<z<1.6$. ELG targeting for DESI will be finalized during a `Survey Validation' phase.

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arXiv2020

Preliminary Target Selection for the DESI Luminous Red Galaxy (LRG) Sample

The DESI survey will observe more than 8 million candidate luminous red galaxies (LRGs) in the redshift range $0.3<z<1.0$. Here we present a preliminary version of the DESI LRG target selection developed using Legacy Surveys Data Release 8 $g$, $r$, $z$ and $W1$ photometry. This selection yields a sample with a uniform surface density of ${\sim}\,600$ deg$^{-2}$and very low predicted stellar contamination and redshift failure rates. During DESI Survey Validation, updated versions of this selection will be tested and optimized.

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arXiv2020

Preliminary Target Selection for the DESI Milky Way Survey (MWS)

The DESI Milky Way Survey (MWS) will observe $\ge$8 million stars between $16 < r < 19$ mag, supplemented by observations of brighter targets under poor observing conditions. The survey will permit an accurate determination of stellar kinematics and population gradients; characterize diffuse substructure in the thick disk and stellar halo; enable the discovery of extremely metal-poor stars and other rare stellar types; and improve constraints on the Galaxy's 3D dark matter distribution from halo star kinematics. MWS will also enable a detailed characterization of the stellar populations within 100 pc of the Sun, including a complete census of white dwarfs. The target catalog from the preliminary selection described here is public.

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arXiv2020

Preliminary Target Selection for the DESI Bright Galaxy Survey (BGS)

The Dark Energy Spectroscopic Instrument (DESI) will execute a nearly magnitude-limited survey of low redshift galaxies ($0.05 \leq z \leq 0.4$, median $z \approx 0.2$). Clustering analyses of this Bright Galaxy Survey (BGS) will yield the most precise measurements to date of baryon acoustic oscillations and redshift-space distortions at low redshift. DESI BGS will comprise two target classes: (i) BRIGHT ($r<19.5$~mag), and (ii) FAINT ($19.5<r<20$~mag). Here we present a summary of the star-galaxy separation, and different photometric and geometrical masks, used in BGS to reduce the number of spurious targets. The selection results in a total density of $\sim 800$ objects/deg$^2$ for the BRIGHT and $\sim 600$ objects/deg$^2$ for the FAINT selections.A full characterization of the BGS selection can be found in Ruiz-Macias et al. (2020).

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arXiv2020

Baryon Acoustic Oscillations in the projected cross-correlation function between the eBOSS DR16 quasars and photometric galaxies from the DESI Legacy Imaging Surveys

We search for the Baryon Acoustic Oscillations in the projected cross-correlation function binned into transverse comoving radius between the SDSS-IV DR16 eBOSS quasars and a dense photometric sample of galaxies selected from the DESI Legacy Imaging Surveys. We estimate the density of the photometric sample of galaxies in this redshift range to be about 2900 deg$^{-2}$, which is deeper than the official DESI ELG selection, and the density of the spectroscopic sample is about 20 deg$^{-2}$. In order to mitigate the systematics related to the use of different imaging surveys close to the detection limit, we use a neural network approach that accounts for complex dependencies between the imaging attributes and the observed galaxy density. We find that we are limited by the depth of the imaging surveys which affects the density and purity of the photometric sample and its overlap in redshift with the quasar sample, which thus affects the performance of the method. When cross-correlating the photometric galaxies with quasars in $0.6 \leq z \leq 1.2$, the cross-correlation function can provide better constraints on the comoving angular distance, $D_{\rm M}$ (6\% precision) compared to the constraint on the spherically-averaged distance $D_{\rm V}$ (9\% precision) obtained from the auto-correlation. Although not yet competitive, this technique will benefit from the arrival of deeper photometric data from upcoming surveys which will enable it to go beyond the current limitations we have identified in this work.

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arXiv2020

Validation of Emission-Line Galaxies Target Selection Algorithms for the Dark Energy Spectroscopic Instrument Using the MMT Binospec

The forthcoming Dark Energy Spectroscopic Instrument (DESI) experiment plans to measure the effects of dark energy on the expansion of the Universe and create a $3$D map of the Universe using galaxies up to $z \sim 1.6$ and QSOs up to $z \sim 3.5$. In order to create this map, DESI will obtain spectroscopic redshifts of over $30$ million objects; among them, a majority are \oii emitting star-forming galaxies known as emission-line galaxies (ELGs). These ELG targets will be pre-selected by drawing a selection region on the $g - r$ vs. $r - z$ colour-colour plot, where high redshift ELGs form a separate locus from the lower redshift ELGs and interlopers. In this paper, we study the efficiency of three ELG target selection algorithms -- the final design report (FDR) cut based on the DEEP2 photometry, Number Density Modelling and Random Forest -- to determine how the combination of these three algorithms can be best used to yield a simple selection boundary that will be best suited to meet DESI's science goals. To do this, we selected $17$ small patches in the DESI footprint where we run the three target selection algorithms to pre-select ELGs based on their photometry. We observed the pre-selected ELGs using the MMT Binospec, which is similar in functionality to the DESI instrument, to obtain their spectroscopic redshifts and fluxes of $1054$ ELGs. By analysing the redshift and fluxing distribution of these galaxies, we find that although NDM performed the best, simple changes in the FDR definition would also yield sufficient performance.

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arXiv2020

HST Grism-derived Forecasts for Future Galaxy Redshift Surveys

The mutually complementary Euclid and Roman galaxy redshift surveys will use Halpha- and [OIII]-selected emission line galaxies as tracers of the large scale structure at $0.9 \lesssim z \lesssim 1.9$ (Halpha) and $1.5 \lesssim z \lesssim 2.7$ ([OIII]). It is essential to have a reliable and sufficiently precise knowledge of the expected numbers of Halpha-emitting galaxies in the survey volume in order to optimize these redshift surveys for the study of dark energy. Additionally, these future samples of emission-line galaxies will, like all slitless spectroscopy surveys, be affected by a complex selection function that depends on galaxy size and luminosity, line equivalent width, and redshift errors arising from the misidentification of single emission-line galaxies. Focusing on the specifics of the Euclid survey, we combine two slitless spectroscopic WFC3-IR datasets -- 3D-HST+AGHAST and the WISP survey -- to construct a Euclid-like sample that covers an area of 0.56 deg$^2$ and includes 1277 emission line galaxies. We detect 1091 ($\sim$3270 deg$^{-2}$) Halpha+[NII]-emitting galaxies in the range $0.9\leq z \leq 1.6$ and 162 ($\sim$440 deg$^{-2}$) [OIII]$λ$5007-emitters over $1.5\leq z \leq 2.3$ with line fluxes $\geq 2 \times 10^{-16}$ erg s$^{-1}$ cm$^{-2}$. The median of the Halpha+[NII] equivalent width distribution is $\sim$250Å, and the effective radii of the continuum and Halpha+[NII] emission are correlated with a median of $\sim$0.38" and significant scatter ($σ\sim $0.2"$-$0.35"). Finally, we explore the prevalence of redshift misidentification in future Euclid samples, finding potential contamination rates of $\sim$14-20% and $\sim$6% down to $2\times 10^{-16}$ and $6 \times 10^{-17}$ erg s$^{-1}$ cm$^{-2}$, respectively, though with increased wavelength coverage these percentages drop to nearly zero.

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arXiv2020

Can Assembly Bias Explain the Lensing Amplitude of the BOSS CMASS Sample in a Planck Cosmology?

In this paper, we investigate whether galaxy assembly bias can reconcile the 20-40% disagreement between the observed galaxy projected clustering signal and the galaxy-galaxy lensing signal in the BOSS CMASS galaxy sample reported in Leauthaud et al. (2017). We use the suite of AbacusCosmos Lamda-CDM simulations at Planck best-fit cosmology and two flexible implementations of extended halo occupation distribution (HOD) models that incorporate galaxy assembly bias to build forward models and produce joint fits of the observed galaxy clustering signal and the galaxy-galaxy lensing signal. We find that our models using the standard HODs without any assembly bias generalizations continue to show a 20-40% over-prediction of the observed galaxy-galaxy lensing signal. We find that our implementations of galaxy assembly bias do not reconcile the two measurements at Planck best-fit cosmology. In fact, despite incorporating galaxy assembly bias, the satellite distribution parameter, and the satellite velocity bias parameter into our extended HOD model, our fits still strongly suggest a 31-34% discrepancy between the observed projected clustering and galaxy-galaxy lensing measurements. It remains to be seen whether a combination of other galaxy assembly bias models, alternative cosmological parameters, or baryonic effects can explain the amplitude difference between the two signals.

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arXiv2019

Computing the Small-Scale Galaxy Power Spectrum and Bispectrum in Configuration-Space

We present a new class of estimators for computing small-scale power spectra and bispectra in configuration-space via weighted pair- and triple-counts, with no explicit use of Fourier transforms. Particle counts are truncated at $R_0\sim 100h^{-1}\,\mathrm{Mpc}$ via a continuous window function, which has negligible effect on the measured power spectrum multipoles at small scales. This gives a power spectrum algorithm with complexity $\mathcal{O}(NnR_0^3)$ (or $\mathcal{O}(Nn^2R_0^6)$ for the bispectrum), measuring $N$ galaxies with number density $n$. Our estimators are corrected for the survey geometry and have neither self-count contributions nor discretization artifacts, making them ideal for high-$k$ analysis. Unlike conventional Fourier transform based approaches, our algorithm becomes more efficient on small scales (since a smaller $R_0$ may be used), thus we may efficiently estimate spectra across $k$-space by coupling this method with standard techniques. We demonstrate the utility of the publicly available power spectrum algorithm by applying it to BOSS DR12 simulations to compute the high-$k$ power spectrum and its covariance. In addition, we derive a theoretical rescaled-Gaussian covariance matrix, which incorporates the survey geometry and is found to be in good agreement with that from mocks. Computing configuration- and Fourier-space statistics in the same manner allows us to consider joint analyses, which can place stronger bounds on cosmological parameters; to this end we also discuss the cross-covariance between the two-point correlation function and the small-scale power spectrum.

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arXiv2019

Cosmology with galaxy-galaxy lensing on non-perturbative scales: Emulation method and application to BOSS LOWZ

We describe our nonlinear emulation (i.e., interpolation) framework that combines the halo occupation distribution (HOD) galaxy bias model with $N$-body simulations of nonlinear structure formation, designed to accurately predict the projected clustering and galaxy-galaxy lensing signals from luminous red galaxies (LRGs) in the redshift range $0.16 < z < 0.36$ on comoving scales $0.6 < r_p < 30$ \hMpc. The interpolation accuracy is $\lesssim 1-2$ per cent across the entire physically plausible range of parameters for all scales considered. We correctly recover the true value of the cosmological parameter $S_8 = ({σ_8}/{0.8228}) ({Ω_{\text{m}}}/{0.3107})^{0.6}$ from mock measurements produced via subhalo abundance matching (SHAM)-based lightcones designed to approximately match the properties of the SDSS LOWZ galaxy sample. Applying our model to Baryon Oscillation Spectroscopic Survey (BOSS) Data Release 14 (DR14) LOWZ galaxy clustering and galaxy-shear cross-correlation measurements made with Sloan Digital Sky Survey (SDSS) Data Release 8 (DR8) imaging, we perform a prototype cosmological analysis marginalizing over $w$CDM cosmological parameters and galaxy HOD parameters. We obtain a 4.4 per cent measurement of $S_8 = 0.847 \pm 0.037$, in $3.5σ$ tension with the Planck cosmological results of $1.00 \pm 0.02$. We discuss the possibility of underestimated systematic uncertainties or astrophysical effects that could explain this discrepancy.

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arXiv2019

RascalC: A Jackknife Approach to Estimating Single and Multi-Tracer Galaxy Covariance Matrices

To make use of clustering statistics from large cosmological surveys, accurate and precise covariance matrices are needed. We present a new code to estimate large scale galaxy two-point correlation function (2PCF) covariances in arbitrary survey geometries that, due to new sampling techniques, runs $\sim 10^4$ times faster than previous codes, computing finely-binned covariance matrices with negligible noise in less than 100 CPU-hours. As in previous works, non-Gaussianity is approximated via a small rescaling of shot-noise in the theoretical model, calibrated by comparing jackknife survey covariances to an associated jackknife model. The flexible code, RascalC, has been publicly released, and automatically takes care of all necessary pre- and post-processing, requiring only a single input dataset (without a prior 2PCF model). Deviations between large scale model covariances from a mock survey and those from a large suite of mocks are found to be be indistinguishable from noise. In addition, the choice of input mock are shown to be irrelevant for desired noise levels below $\sim 10^5$ mocks. Coupled with its generalization to multi-tracer data-sets, this shows the algorithm to be an excellent tool for analysis, reducing the need for large numbers of mock simulations to be computed.

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arXiv2019

Estimating Covariance Matrices for Two- and Three-Point Correlation Function Moments in Arbitrary Survey Geometries

We present configuration-space estimators for the auto- and cross-covariance of two- and three-point correlation functions (2PCF and 3PCF) in general survey geometries. These are derived in the Gaussian limit (setting higher-order correlation functions to zero), but for arbitrary non-linear 2PCFs (which may be estimated from the survey itself), with a shot-noise rescaling parameter included to capture non-Gaussianity. We generalize previous approaches to include Legendre moments via a geometry-correction function calibrated from measured pair and triple counts. Making use of importance sampling and random particle catalogs, we can estimate model covariances in fractions of the time required to do so with mocks, obtaining estimates with negligible sampling noise in $\sim 10$ ($\sim 100$) CPU-hours for the 2PCF (3PCF) auto-covariance. We compare results to sample covariances from a suite of BOSS DR12 mocks and find the matrices to be in good agreement, assuming a shot-noise rescaling parameter of $1.03$ ($1.20$) for the 2PCF (3PCF). To obtain strongest constraints on cosmological parameters we must use multiple statistics in concert; having robust methods to measure their covariances at low computational cost is thus of great relevance to upcoming surveys.

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arXiv2019

A Hybrid Deep Learning Approach to Cosmological Constraints From Galaxy Redshift Surveys

We present a deep machine learning (ML)-based technique for accurately determining $σ_8$ and $Ω_m$ from mock 3D galaxy surveys. The mock surveys are built from the AbacusCosmos suite of $N$-body simulations, which comprises 40 cosmological volume simulations spanning a range of cosmological models, and we account for uncertainties in galaxy formation scenarios through the use of generalized halo occupation distributions (HODs). We explore a trio of ML models: a 3D convolutional neural network (CNN), a power-spectrum-based fully connected network, and a hybrid approach that merges the two to combine physically motivated summary statistics with flexible CNNs. We describe best practices for training a deep model on a suite of matched-phase simulations and we test our model on a completely independent sample that uses previously unseen initial conditions, cosmological parameters, and HOD parameters. Despite the fact that the mock observations are quite small ($\sim0.07h^{-3}\,\mathrm{Gpc}^3$) and the training data span a large parameter space (6 cosmological and 6 HOD parameters), the CNN and hybrid CNN can constrain $σ_8$ and $Ω_m$ to $\sim3\%$ and $\sim4\%$, respectively.

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arXiv2019

Application of the iterative reconstruction to simulated galaxy fields

We apply an iterative reconstruction method to galaxy mocks in redshift space obtained from $N$-body simulations. Comparing the two-point correlation functions for the reconstructed density field, we find that although the performance is limited by shot noise and galaxy bias compared to the matter field, the iterative method can still reconstruct the initial linear density field from the galaxy field better than the standard method both in real and in redshift space. Furthermore, the iterative method is able to reconstruct both the monopole and quadrupole more precisely, unlike the standard method. We see that as the number density of galaxies gets smaller, the performance of reconstruction gets worse due to the sparseness. However, the precision in the determination of bias ($\sim20\%$) hardly impacts on the reconstruction processes.

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