ABSTRACT
Using synthetic Lyman-α forests from the Dark Energy Spectroscopic Instrument (DESI) survey, we present a study of the impact of errors in the estimation of quasar redshift on the Lyman-α ...correlation functions. Estimates of quasar redshift have large uncertainties of a few hundred km s−1 due to the broadness of the emission lines and the intrinsic shifts from other emission lines. We inject Gaussian random redshift errors into the mock quasar catalogues, and measure the auto-correlation and the Lyman-α-quasar cross-correlation functions. We find a smearing of the BAO feature in the radial direction, but changes in the peak position are negligible. However, we see a significant unphysical correlation for small separations transverse to the line of sight which increases with the amplitude of the redshift errors. We interpret this contamination as a result of the broadening of emission lines in the measured mean continuum, caused by quasar redshift errors, combined with the unrealistically strong clustering of the simulated quasars on small scales.
Quasar – CIV forest cross-correlation with SDSS DR12 Gontcho A Gontcho, Satya; Miralda-Escudé, Jordi; Font-Ribera, Andreu ...
Monthly notices of the Royal Astronomical Society,
10/2018, Volume:
480, Issue:
1
Journal Article
Peer reviewed
Open access
We present a new determination of the large-scale clustering of the CIV forest (i.e. the absorption due to all CIV absorbers) using its cross-correlation with quasars in the Sloan Digital Sky Survey ...Data Release 12. We fit a linear bias model to the measured cross-correlation. We find that the transmission bias of the CIV forest, , at a mean redshift of |$z\, =\, 2.3$|, obeys |$(1\, +\, \beta _c)b_{F c}=-0.024\, \pm \, 0.003$|. Here, β_c is the linear redshift space distortion parameter of the CIV absorption, which can only be poorly determined at β_c = 1.1 ± 0.6 from our data. The most accurately determined combination marginalized over β_c is |$(1\, +\, 0.44\, \beta _c)b_{F c}=-0.0170\, \pm \, 0.0014$|. The transmission bias is related to the bias of CIV absorbers and their host haloes, bτc, through the effective mean optical depth of the CIV forest, which we estimate at |$\bar{\tau }_c(z) \simeq 0.01$| from previous studies of the CIV equivalent width distribution. We then find 1 < bτc < 1.7, with the large error arising from uncertainties in βc and |$\bar{\tau }_c$|. This CIV bias is lower than the DLA bias bDLA ≃ 2 measured previously from the cross-correlation of DLAs and the Ly α forest, indicating that most CIV absorbers are hosted by haloes of lower mass than DLAs. More accurate determinations of |$\bar{\tau }_c(z)$| and βc are necessary to check this conclusion.
ABSTRACT
We present an extended validation of semi-analytical, semi-empirical covariance matrices for the two-point correlation function (2PCF) on simulated catalogs representative of luminous red ...galaxies (LRGs) data collected during the initial 2 months of operations of the Stage-IV ground-based Dark Energy Spectroscopic Instrument (DESI). We run the pipeline on multiple effective Zel’dovich (EZ) mock galaxy catalogs with the corresponding cuts applied and compare the results with the mock sample covariance to assess the accuracy and its fluctuations. We propose an extension of the previously developed formalism for catalogs processed with standard reconstruction algorithms. We consider methods for comparing covariance matrices in detail, highlighting their interpretation and statistical properties caused by sample variance, in particular, non-trivial expectation values of certain metrics even when the external covariance estimate is perfect. With improved mocks and validation techniques, we confirm a good agreement between our predictions and sample covariance. This allows one to generate covariance matrices for comparable data sets without the need to create numerous mock galaxy catalogs with matching clustering, only requiring 2PCF measurements from the data itself. The code used in this paper is publicly available at https://github.com/oliverphilcox/RascalC.
ABSTRACT We present a simple, differentiable method for predicting emission line strengths from rest-frame optical continua using an empirically determined mapping. Extensive work has been done to ...develop mock galaxy catalogues that include robust predictions for galaxy photometry, but reliably predicting the strengths of emission lines has remained challenging. Our new mapping is a simple neural network implemented using the JAX Python automatic differentiation library. It is trained on Dark Energy Spectroscopic Instrument Early Release data to predict the equivalent widths (EWs) of the eight brightest optical emission lines (including H α, H β, O ii, and O iii) from a galaxy’s rest-frame optical continuum. The predicted EW distributions are consistent with the observed ones when noise is accounted for, and we find Spearman’s rank correlation coefficient ρs > 0.87 between predictions and observations for most lines. Using a non-linear dimensionality reduction technique, we show that this is true for galaxies across the full range of observed spectral energy distributions. In addition, we find that adding measurement uncertainties to the predicted line strengths is essential for reproducing the distribution of observed line-ratios in the BPT diagram. Our trained network can easily be incorporated into a differentiable stellar population synthesis pipeline without hindering differentiability or scalability with GPUs. A synthetic catalogue generated with such a pipeline can be used to characterize and account for biases in the spectroscopic training sets used for training and calibration of photo-z’s, improving the modelling of systematic incompleteness for the Rubin Observatory LSST and other surveys.
ABSTRACT
We present the VST ATLAS Quasar Survey, consisting of ∼1229 000 quasar (QSO) candidates with 16 < g < 22.5 over ∼4700 deg2. The catalogue is based on VST ATLAS+NEOWISE imaging surveys and ...aims to reach a QSO sky density of 130 deg−2 for $z$ < 2.2 and ∼30 deg−2 for $z$ > 2.2. To guide our selection, we use X-ray/UV/optical/MIR data in the extended William Herschel Deep Field (WHDF) where we find a g < 22.5 broad-line QSO density of 269 ± 67 deg−2, roughly consistent with the expected ∼196 deg−2. We find that ∼25 per cent of our QSOs are morphologically classed as optically extended. Overall, we find that in these deep data, MIR, UV, and X-ray selections are ∼70–90 per cent complete while X-ray suffers less contamination than MIR and UV. MIR is however more sensitive than X-ray or UV to $z$ > 2.2 QSOs at g < 22.5 and the $S_X(0.5-10\, {\rm keV})\gt 1\times 10^{-14}$ ergs cm−2 s−1 limit of eROSITA. We adjust the selection criteria from our previous 2QDES pilot survey and prioritize VST ATLAS candidates that show both UV and MIR excess, also selecting candidates initially classified as extended. We test our selections using data from DESI (which will be released in DR1) and 2dF to estimate the efficiency and completeness, and we use ANNz2 to determine photometric redshifts. Applying over the ∼4700 deg2 ATLAS area gives us $\sim 917\,000\, z\lt 2.2$ QSO candidates of which 472 000 are likely to be $z$ < 2.2 QSOs, implying a sky density of ∼100 deg−2, which our WHDF analysis suggests will rise to at least 130 deg−2 when eROSITA X-ray candidates are included. At $z$ > 2.2, we find ∼310() 000 candidates, of which 169 000 are likely to be QSOs for a sky density of ∼36 deg−2.
ABSTRACT
The shear measurement from the Dark Energy Camera Legacy Survey (DECaLS) provides an excellent opportunity for galaxy–galaxy lensing study with the Dark Energy Spectroscopic Instrument ...(DESI) galaxies, given the large (∼9000 deg2) sky overlap. We explore this potential by combining the DESI 1 per cent survey and DECaLS Data Release 8 (DR8). With ∼106 deg2 sky overlap, we achieve significant detection of galaxy–galaxy lensing for Bright Galaxy Survey (BGS) and luminous red galaxy (LRG) as lenses. Scaled to the full BGS sample, we expect the statistical errors to improve from $18(12)\,{\rm per \ cent}$ to a promising level of $2(1.3)\,{\rm per \ cent}$ at $\theta \gt 8\,\mathrm{ arcmin} \, (\lt 8\,\mathrm{ arcmin})$. This brings stronger requirements for future systematics control. To fully realize such potential, we need to control the residual multiplicative shear bias |m| < 0.006 and the bias in the mean redshift |Δz| < 0.008, requiring the introduced bias in the measurement is <0.31σ. We also expect significant detection of galaxy–galaxy lensing with DESI LRG/emission line galaxy (ELG) full samples as lenses, and cosmic magnification of ELG through cross-correlation with low-redshift DECaLS shear. If such systematical error control can be achieved, we find the advantages of DECaLS, comparing with the Kilo Degree Survey (KiDS) and the Hyper Suprime-Cam (HSC), are at low redshift, large scale, and in measuring the shear ratio (to σR ∼ 0.04) and cosmic magnification.
Context.
We present a novel approach to the construction of mock galaxy catalogues for large-scale structure analysis based on the distribution of dark matter halos obtained with effective bias ...models at the field level.
Aims.
We aim to produce mock galaxy catalogues capable of generating accurate covariance matrices for a number of cosmological probes that are expected to be measured in current and forthcoming galaxy redshift surveys (e.g. two- and three-point statistics). The construction of the catalogues shown in this paper is part of a mock-comparison project within the Dark Energy Spectroscopic Instrument (DESI) collaboration.
Methods.
We use the bias assignment method (
BAM
) to model the statistics of halo distribution through a learning algorithm using a few detailed
N
-body simulations, and approximated gravity solvers based on Lagrangian perturbation theory. We introduce cosmic-web-dependent corrections to modelling redshift-space distortions at the
N
-body level – both in the halo and galaxy distributions –, as well as a multi-scale approach for accurate assignment of halo properties. Using specific models of halo occupation distributions to populate halos, we generate galaxy mocks with the expected number density and central-satellite fraction of emission-line galaxies, which are a key target of the DESI experiment.
Results.
BAM
generates mock catalogues with per cent accuracy in a number of summary statistics, such as the abundance, the two- and three-point statistics of halo distributions, both in real and redshift space. In particular, the mock galaxy catalogues display ∼3%−10% accuracy in the multipoles of the power spectrum up to scales of
k
∼ 0.4
h
−1
Mpc. We show that covariance matrices of two- and three-point statistics obtained with
BAM
display a similar structure to the reference simulation.
Conclusions.
BAM
offers an efficient way to produce mock halo catalogues with accurate two- and three-point statistics, and is able to generate a variety of multi-tracer catalogues with precise covariance matrices of several cosmological probes. We discuss future developments of the algorithm towards mock production in DESI and other galaxy-redshift surveys.
Abstract We use angular clustering of luminous red galaxies from the Dark Energy Spectroscopic Instrument (DESI) imaging surveys to constrain the local primordial non-Gaussianity parameter fNL. Our ...sample comprises over 12 million targets, covering 14,000 square degrees of the sky, with redshifts in the range 0.2 < z < 1.35. We identify Galactic extinction, survey depth, and astronomical seeing as the primary sources of systematic error, and employ linear regression and artificial neural networks to alleviate non-cosmological excess clustering on large scales. Our methods are tested against simulations with and without fNL and systematics, showing superior performance of the neural network treatment. The neural network with a set of nine imaging property maps passes our systematic null test criteria, and is chosen as the fiducial treatment. Assuming the universality relation, we find $f_{\rm NL} = 34^{+24(+50)}_{-44(-73)}$ at 68 per cent(95 per cent) confidence. We apply a series of robustness tests (e.g. cuts on imaging, declination, or scales used) that show consistency in the obtained constraints. We study how the regression method biases the measured angular power-spectrum and degrades the fNL constraining power. The use of the nine maps more than doubles the uncertainty compared to using only the three primary maps in the regression. Our results thus motivate the development of more efficient methods that avoid over-correction, protect large-scale clustering information, and preserve constraining power. Additionally, our results encourage further studies of fNL with DESI spectroscopic samples, where the inclusion of 3D clustering modes should help separate imaging systematics and lessen the degradation in the fNL uncertainty.
ABSTRACT
Together with larger spectroscopic surveys such as the Dark Energy Spectroscopic Instrument (DESI), the precision of large-scale structure studies and thus the constraints on the ...cosmological parameters are rapidly improving. Therefore, one must build realistic simulations and robust covariance matrices. We build galaxy catalogues by applying a halo occupation distribution (HOD) model upon the FastPM simulations, such that the resulting galaxy clustering reproduces high-resolution N-body simulations. While the resolution and halo finder are different from the reference simulations, we reproduce the reference galaxy two-point clustering measurements – monopole and quadrupole – to a precision required by the DESI Year 1 emission line galaxy sample down to non-linear scales, i.e. $k\lt 0.5\, h\, \mathrm{Mpc}^{-1}$ or $s\gt 10\, \mathrm{Mpc}\, h^{-1}$. Furthermore, we compute covariance matrices based on the resulting FastPM galaxy clustering – monopole and quadrupole. We study for the first time the effect of fitting on Fourier conjugate (e.g. power spectrum) on the covariance matrix of the Fourier counterpart (e.g. correlation function). We estimate the uncertainties of the two parameters of a simple clustering model and observe a maximum variation of 20 per cent for the different covariance matrices. Nevertheless, for most studied scales the scatter is between 2 and 10 per cent. Consequently, using the current pipeline we can precisely reproduce the clustering of N-body simulations and the resulting covariance matrices provide robust uncertainty estimations against HOD fitting scenarios. We expect our methodology will be useful for the coming DESI data analyses and their extension for other studies.
Abstract We explore the galaxy-halo connection information that is available in low-redshift samples from the early data release of the Dark Energy Spectroscopic Instrument (DESI). We model the halo ...occupation distribution (HOD) from z = 0.1 to 0.3 using Survey Validation 3 (SV3; a.k.a., the One-Percent Survey) data of the DESI Bright Galaxy Survey. In addition to more commonly used metrics, we incorporate counts-in-cylinders (CiC) measurements, which drastically tighten HOD constraints. Our analysis is aided by the Python package, galtab , which enables the rapid, precise prediction of CiC for any HOD model available in halotools . This methodology allows our Markov chains to converge with much fewer trial points, and enables even more drastic speedups due to its GPU portability. Our HOD fits constrain characteristic halo masses tightly and provide statistical evidence for assembly bias, especially at lower luminosity thresholds: the HOD of central galaxies in z ∼ 0.15 samples with limiting absolute magnitude M r < −20.0 and M r < −20.5 samples is positively correlated with halo concentration with a significance of 99.9% and 99.5%, respectively. Our models also favor positive central assembly bias for the brighter M r < −21.0 sample at z ∼ 0.25 (94.8% significance), but there is no significant evidence for assembly bias with the same luminosity threshold at z ∼ 0.15. We provide our constraints for each threshold sample’s characteristic halo masses, assembly bias, and other HOD parameters. These constraints are expected to be significantly tightened with future DESI data, which will span an area 100 times larger than that of SV3.
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