scholarly journals Mitigating contamination in LSS surveys: a comparison of methods

2021 ◽  
Vol 503 (4) ◽  
pp. 5061-5084 ◽  
Author(s):  
Noah Weaverdyck ◽  
Dragan Huterer
Keyword(s):  
Power Spectrum ◽  
Large Scale ◽  
Power Spectra ◽  
Dark Energy Survey ◽  
Current State ◽  
Galaxy Distribution ◽  
The Galaxy ◽  
Improved Estimators ◽  
Mitigation Methods ◽  
Energy Survey

ABSTRACT Future large-scale structure surveys will measure the locations and shapes of billions of galaxies. The precision of such catalogues will require meticulous treatment of systematic contamination of the observed fields. We compare several existing methods for removing such systematics from galaxy clustering measurements. We show how all the methods, including the popular pseudo-Cℓ Mode Projection and Template Subtraction methods, can be interpreted under a common regression framework and use this to suggest improved estimators. We show how methods designed to mitigate systematics in the power spectrum can be used to produce clean maps, which are necessary for cosmological analyses beyond the power spectrum, and we extend current methods to treat the next-order multiplicative contamination in observed maps and power spectra, which reduced power spectrum errors from $\Delta \chi ^2_{\rm C_\ell }\simeq 10$ to ≃ 1 in simulated analyses. Two new mitigation methods are proposed, which incorporate desirable features of current state-of-the-art methods while being simpler to implement. Investigating the performance of all the methods on a common set of simulated measurements from Year 5 of the Dark Energy Survey, we test their robustness to various analysis cases. Our proposed methods produce improved maps and power spectra when compared to current methods, while requiring almost no user tuning. We end with recommendations for systematics mitigation in future surveys, and note that the methods presented are generally applicable beyond the galaxy distribution to any field with spatial systematics.

scholarly journals Halo exclusion criteria impacts halo statistics

2019 ◽  
Vol 489 (3) ◽  
pp. 4170-4175 ◽  
Author(s):  
Rafael García ◽  
Eduardo Rozo
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Mass Function ◽  
Exclusion Criteria ◽  
Simulation Data ◽  
Dark Energy Survey ◽  
Current State ◽  
Statistical Precision ◽  
Energy Survey ◽  
Cluster Abundance

ABSTRACT Every halo-finding algorithm must make a critical yet relatively arbitrary choice: it must decide which structures are parent haloes, and which structures are subhaloes of larger haloes. We refer to this choice as percolation. We demonstrate that the choice of percolation impacts the statistical properties of the resulting halo catalogue. Specifically, we modify the halo-finding algorithm rockstar to construct three different halo catalogues from the same simulation data, each with identical mass definitions, but different choice of percolation. The resulting haloes exhibit significant differences in both halo abundance and clustering properties. Differences in the halo mass function reach 6 per cent for haloes of mass $10^{13}\ h^{-1}\ {\rm {\rm M}_{\odot }}$, larger than the few per cent precision necessary for current cluster abundance experiments such as the Dark Energy Survey. Comparable differences are observed in the large-scale clustering bias, while differences in the halo–matter correlation function reach 30 per cent on translinear scales. These effects can bias weak-lensing estimates of cluster masses at a level comparable to the statistical precision of current state-of-the-art experiments.

scholarly journals Likelihood-free inference with neural compression of DES SV weak lensing map statistics

2020 ◽  
Vol 501 (1) ◽  
pp. 954-969
Author(s):  
Niall Jeffrey ◽  
Justin Alsing ◽  
François Lanusse
Keyword(s):  
Dark Energy ◽  
Large Scale ◽  
Cosmological Parameters ◽  
Power Spectra ◽  
Weak Lensing ◽  
Probability Density Estimation ◽  
Unknown Parameters ◽  
Dark Energy Survey ◽  
Inference Problems ◽  
Energy Survey

ABSTRACT In many cosmological inference problems, the likelihood (the probability of the observed data as a function of the unknown parameters) is unknown or intractable. This necessitates approximations and assumptions, which can lead to incorrect inference of cosmological parameters, including the nature of dark matter and dark energy, or create artificial model tensions. Likelihood-free inference covers a novel family of methods to rigorously estimate posterior distributions of parameters using forward modelling of mock data. We present likelihood-free cosmological parameter inference using weak lensing maps from the Dark Energy Survey (DES) Science Verification data, using neural data compression of weak lensing map summary statistics. We explore combinations of the power spectra, peak counts, and neural compressed summaries of the lensing mass map using deep convolution neural networks. We demonstrate methods to validate the inference process, for both the data modelling and the probability density estimation steps. Likelihood-free inference provides a robust and scalable alternative for rigorous large-scale cosmological inference with galaxy survey data (for DES, Euclid, and LSST). We have made our simulated lensing maps publicly available.

scholarly journals The anisotropy of the power spectrum in periodic cosmological simulations

2021 ◽  
Vol 503 (4) ◽  
pp. 5638-5645
Author(s):  
Gábor Rácz ◽  
István Szapudi ◽  
István Csabai ◽  
László Dobos
Keyword(s):  
Dark Matter ◽  
Power Spectrum ◽  
Large Scale ◽  
Cold Dark Matter ◽  
Initial Conditions ◽  
Power Spectra ◽  
Cosmological Simulations ◽  
Spherical Collapse ◽  
Lower Amplitude ◽  
Hydrodynamical Simulations

ABSTRACT The classical gravitational force on a torus is anisotropic and always lower than Newton’s 1/r2 law. We demonstrate the effects of periodicity in dark matter only N-body simulations of spherical collapse and standard Lambda cold dark matter (ΛCDM) initial conditions. Periodic boundary conditions cause an overall negative and anisotropic bias in cosmological simulations of cosmic structure formation. The lower amplitude of power spectra of small periodic simulations is a consequence of the missing large-scale modes and the equally important smaller periodic forces. The effect is most significant when the largest mildly non-linear scales are comparable to the linear size of the simulation box, as often is the case for high-resolution hydrodynamical simulations. Spherical collapse morphs into a shape similar to an octahedron. The anisotropic growth distorts the large-scale ΛCDM dark matter structures. We introduce the direction-dependent power spectrum invariant under the octahedral group of the simulation volume and show that the results break spherical symmetry.

scholarly journals Anti-symmetric clustering signals in the observed power spectrum

2021 ◽  
Vol 2021 (12) ◽  
pp. 003
Author(s):  
José Fonseca ◽  
Chris Clarkson
Keyword(s):  
Dark Matter ◽  
Power Spectrum ◽  
Euler Equation ◽  
Large Scale ◽  
Signal To Noise Ratio ◽  
Power Spectra ◽  
Large Scale Structure ◽  
Scale Structure ◽  
Signal To Noise ◽  
Peculiar Velocities

Abstract In this paper, we study how to directly measure the effect of peculiar velocities in the observed angular power spectra. We do this by constructing a new anti-symmetric estimator of Large Scale Structure using different dark matter tracers. We show that the Doppler term is the major component of our estimator and we show that we can measure it with a signal-to-noise ratio up to ∼ 50 using a futuristic SKAO HI galaxy survey. We demonstrate the utility of this estimator by using it to provide constraints on the Euler equation.

scholarly journals Super-sample covariance approximations and partial sky coverage

2018 ◽  
Vol 611 ◽  
pp. A83 ◽  
Author(s):  
Fabien Lacasa ◽  
Marcos Lima ◽  
Michel Aguena
Keyword(s):  
Large Scale ◽  
Statistical Error ◽  
The Self ◽  
Mass Relation ◽  
Numerical Implementation ◽  
Galaxy Surveys ◽  
Dark Energy Survey ◽  
Analytical Approximations ◽  
Sample Covariance ◽  
Energy Survey

Super-sample covariance (SSC) is the dominant source of statistical error on large scale structure (LSS) observables for both current and future galaxy surveys. In this work, we concentrate on the SSC of cluster counts, also known as sample variance, which is particularly useful for the self-calibration of the cluster observable-mass relation; our approach can similarly be applied to other observables, such as galaxy clustering and lensing shear. We first examined the accuracy of two analytical approximations proposed in the literature for the flat sky limit, finding that they are accurate at the 15% and 30–35% level, respectively, for covariances of counts in the same redshift bin. We then developed a harmonic expansion formalism that allows for the prediction of SSC in an arbitrary survey mask geometry, such as large sky areas of current and future surveys. We show analytically and numerically that this formalism recovers the full sky and flat sky limits present in the literature. We then present an efficient numerical implementation of the formalism, which allows fast and easy runs of covariance predictions when the survey mask is modified. We applied our method to a mask that is broadly similar to the Dark Energy Survey footprint, finding a non-negligible negative cross-z covariance, i.e. redshift bins are anti-correlated. We also examined the case of data removal from holes due to, for example bright stars, quality cuts, or systematic removals, and find that this does not have noticeable effects on the structure of the SSC matrix, only rescaling its amplitude by the effective survey area. These advances enable analytical covariances of LSS observables to be computed for current and future galaxy surveys, which cover large areas of the sky where the flat sky approximation fails.

scholarly journals A study on the statistical significance of mutual information between morphology of a galaxy and its large-scale environment

2020 ◽  
Vol 497 (4) ◽  
pp. 4077-4090 ◽  
Author(s):  
Suman Sarkar ◽  
Biswajit Pandey
Keyword(s):  
Mutual Information ◽  
Large Scale ◽  
Statistical Significance ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Information Theoretic ◽  
Galaxy Distribution ◽  
Sky Survey ◽  
The Galaxy ◽  
Physical Correlations

ABSTRACT A non-zero mutual information between morphology of a galaxy and its large-scale environment is known to exist in Sloan Digital Sky Survey (SDSS) upto a few tens of Mpc. It is important to test the statistical significance of these mutual information if any. We propose three different methods to test the statistical significance of these non-zero mutual information and apply them to SDSS and Millennium run simulation. We randomize the morphological information of SDSS galaxies without affecting their spatial distribution and compare the mutual information in the original and randomized data sets. We also divide the galaxy distribution into smaller subcubes and randomly shuffle them many times keeping the morphological information of galaxies intact. We compare the mutual information in the original SDSS data and its shuffled realizations for different shuffling lengths. Using a t-test, we find that a small but statistically significant (at $99.9{{\ \rm per\ cent}}$ confidence level) mutual information between morphology and environment exists upto the entire length-scale probed. We also conduct another experiment using mock data sets from a semi-analytic galaxy catalogue where we assign morphology to galaxies in a controlled manner based on the density at their locations. The experiment clearly demonstrates that mutual information can effectively capture the physical correlations between morphology and environment. Our analysis suggests that physical association between morphology and environment may extend to much larger length-scales than currently believed, and the information theoretic framework presented here can serve as a sensitive and useful probe of the assembly bias and large-scale environmental dependence of galaxy properties.

scholarly journals Impact of relativistic effects on the primordial non-Gaussianity signature in the large-scale clustering of quasars

2020 ◽  
Vol 499 (2) ◽  
pp. 2598-2607
Author(s):  
Mike (Shengbo) Wang ◽  
Florian Beutler ◽  
David Bacon
Keyword(s):  
Dark Energy ◽  
Power Spectrum ◽  
Luminosity Function ◽  
Large Scale ◽  
Relativistic Effects ◽  
Redshift Range ◽  
Cosmological Background ◽  
Relativistic Corrections ◽  
The Galaxy ◽  
The Impact

ABSTRACT Relativistic effects in clustering observations have been shown to introduce scale-dependent corrections to the galaxy overdensity field on large scales, which may hamper the detection of primordial non-Gaussianity fNL through the scale-dependent halo bias. The amplitude of relativistic corrections depends not only on the cosmological background expansion, but also on the redshift evolution and sensitivity to the luminosity threshold of the tracer population being examined, as parametrized by the evolution bias be and magnification bias s. In this work, we propagate luminosity function measurements from the extended Baryon Oscillation Spectroscopic Survey (eBOSS) to be and s for the quasar (QSO) sample, and thereby derive constraints on relativistic corrections to its power spectrum multipoles. Although one could mitigate the impact on the fNL signature by adjusting the redshift range or the luminosity threshold of the tracer sample being considered, we suggest that, for future surveys probing large cosmic volumes, relativistic corrections should be forward modelled from the tracer luminosity function including its uncertainties. This will be important to quasar clustering measurements on scales $k \sim 10^{-3}\, h\, {\rm Mpc}^{-1}$ in upcoming surveys such as the Dark Energy Spectroscopic Instrument (DESI), where relativistic corrections can overwhelm the expected fNL signature at low redshifts z ≲ 1 and become comparable to fNL ≃ 1 in the power spectrum quadrupole at redshifts z ≳ 2.5.

The Dark Energy Survey: perspectives for resolved stellar population studies

2009 ◽  
Vol 5 (S262) ◽  
pp. 265-269
Author(s):  
Basílio Santiago ◽  
Brian Yanny
Keyword(s):  
Dark Energy ◽  
Stellar Population ◽  
Galactic Halo ◽  
Population Studies ◽  
Large Magellanic Cloud ◽  
Dark Energy Survey ◽  
The Galaxy ◽  
Photometric Measurements ◽  
Tidal Tails ◽  
Energy Survey

AbstractThe Dark Energy Survey (DES) will cover 5000 sq. deg. in grizY filters. Although its main goals are related to cosmology, it will yield photometric measurements of over 108 stars, most of them belonging to the Galaxy. DES will increase the sampling depth of very low-luminosity stellar and sub-stellar species, such as white, red, and brown dwarfs, by a factor of several as compared to SDSS. The structure of the Galactic halo, including its complex sub-structures caused by accretion remnants and globular cluster tidal tails, will also be probed and analyzed. DES will also allow comparison of star counts between Northern and Southern Galactic hemispheres to unprecedented detail. Finally, a significant sample of stars in the outskirts of the Large Magellanic Cloud (LMC) will be studied, providing new light into the debate about the existence of an LMC spheroidal component. These, among other important research goals attainable with the DES stellar data, are discussed in this contribution.

scholarly journals Faint Structures in Low Density Regions of the Nearby Universe

1999 ◽  
Vol 183 ◽  
pp. 256-256
Author(s):  
U. Lindner ◽  
K.J. Fricke ◽  
J. Einasto ◽  
M. Einasto
Keyword(s):  
Large Scale ◽  
Large Scale Structure ◽  
Morphological Type ◽  
High Density ◽  
Low Density ◽  
Clusters Of Galaxies ◽  
Galaxy Distribution ◽  
The Galaxy ◽  
Empty Sphere ◽  
First Time

We present an investigation of the galaxy distribution in the huge underdense region between the Hercules, Coma and Local Superclusters, the so-called Northern Local Void (NLV), using void statistics (for details refer to Lindner et al. this Volume). Reshift data for galaxies and poor clusters of galaxies are available in low and high density regions as well. Samples of galaxies with different morphological type and various luminosity limits have been studied separately and void catalogues have been compiled from three different luminosity limited galaxy samples for the first time. Voids have been found using the empty sphere method which has the potential to detect and describe subtle structures in the galaxy distribution. Our approach is complementary to most other methods usually used in Large–Scale Structure studies.

scholarly journals Exact joint likelihood of pseudo-Cℓ estimates from correlated Gaussian cosmological fields

2019 ◽  
Vol 491 (3) ◽  
pp. 3165-3181 ◽  
Author(s):  
Robin E Upham ◽  
Lee Whittaker ◽  
Michael L Brown
Keyword(s):  
Quadratic Form ◽  
Power Spectrum ◽  
Quadratic Forms ◽  
Joint Distribution ◽  
Large Scale ◽  
Power Spectra ◽  
Microwave Background ◽  
Cosmological Constraints ◽  
Statistical Precision ◽  
Joint Likelihood

ABSTRACT We present the exact joint likelihood of pseudo-Cℓ power spectrum estimates measured from an arbitrary number of Gaussian cosmological fields. Our method is applicable to both spin-0 fields and spin-2 fields, including a mixture of the two, and is relevant to cosmic microwave background (CMB), weak lensing, and galaxy clustering analyses. We show that Gaussian cosmological fields are mixed by a mask in such a way that retains their Gaussianity and derive exact expressions for the covariance of the cut-sky spherical harmonic coefficients, the pseudo-aℓms, without making any assumptions about the mask geometry. We then show that each auto or cross-pseudo-Cℓ estimator can be written as a quadratic form, and apply the known joint distribution of quadratic forms to obtain the exact joint likelihood of a set of pseudo-Cℓ estimates in the presence of an arbitrary mask. We show that the same formalism can be applied to obtain the exact joint likelihood of quadratic maximum likelihood power spectrum estimates. Considering the polarization of the CMB as an example, we show using simulations that our likelihood recovers the full, exact multivariate distribution of EE, BB, and EB pseudo-Cℓ power spectra. Our method provides a route to robust cosmological constraints from future CMB and large-scale structure surveys in an era of ever-increasing statistical precision.

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