59 research outputs found
Persistent homology in cosmic shear: constraining parameters with topological data analysis
In recent years, cosmic shear has emerged as a powerful tool to study the
statistical distribution of matter in our Universe. Apart from the standard
two-point correlation functions, several alternative methods like peak count
statistics offer competitive results. Here we show that persistent homology, a
tool from topological data analysis, can extract more cosmological information
than previous methods from the same dataset. For this, we use persistent Betti
numbers to efficiently summarise the full topological structure of weak lensing
aperture mass maps. This method can be seen as an extension of the peak count
statistics, in which we additionally capture information about the environment
surrounding the maxima. We first demonstrate the performance in a mock analysis
of the KiDS+VIKING-450 data: we extract the Betti functions from a suite of
CDM -body simulations and use these to train a Gaussian process emulator
that provides rapid model predictions; we next run a Markov-Chain Monte Carlo
analysis on independent mock data to infer the cosmological parameters and
their uncertainty. When comparing our results, we recover the input cosmology
and achieve a constraining power on that is 5% tighter than that of peak
count statistics. Performing the same analysis on 100 deg of Euclid-like
simulations, we are able to improve the constraints on and
by 18% and 10%, respectively, while breaking some of the
degeneracy between and the dark energy equation of state. To our
knowledge, the methods presented here are the most powerful topological tools
to constrain cosmological parameters with lensing data
A revised density split statistic model for general filters
Studying the statistical properties of the large-scale structure in the
Universe with weak gravitational lensing is a prime goal of several current and
forthcoming galaxy surveys. The power that weak lensing has to constrain
cosmological parameters can be enhanced by considering statistics beyond
second-order shear correlation functions or power spectra. One such
higher-order probe that has proven successful in observational data is the
density split statistics (DSS), in which one analyses the mean shear profiles
around points that are classified according to their foreground galaxy density.
In this paper, we generalise the most accurate DSS model to allow for a broad
class of angular filter functions used for the classification of the different
local density regions. This approach is motivated by earlier findings showing
that an optimised filter can provide tighter constraints on model parameters
compared to the standard top-hat case. We build on large deviation theory
approaches and approximations thereof to model the matter density PDF, and on
perturbative calculations of higher-order moments of the density field. The
novel addition relies on the generalisation of these previously employed
calculations to allow for general filter functions and is validated on several
sets of numerical simulations. The revised model fits well the simulation
measurements, with a residual systematic offset that is small compared to the
statistical accuracy of current weak lensing surveys. The accuracy of the model
is slightly lower for a compensated filter than for a non-negative filter
function, and that it increases with the filter size. Using a Fisher matrix
approach, we find constraints comparable to the commonly used two-point cosmic
shear measures. Hence, our DSS model can be used in competitive analyses of
current cosmic shear data, while it may need refinements for forthcoming
lensing surveys.Comment: 21 pages, 13 figure
On cosmological bias due to the magnification of shear and position samples in modern weak lensing analyses
The magnification of galaxies in modern galaxy surveys induces additional
correlations in the cosmic shear, galaxy-galaxy lensing and clustering
observables used in modern lensing "3x2pt" analyses, due to sample selection.
In this paper, we emulate the magnification contribution to all three
observables utilising the SLICS simulations suite, and test the sensitivity of
the cosmological model, galaxy bias and redshift distribution calibration to
un-modelled magnification in a Stage-IV-like survey using Monte-Carlo sampling.
We find that magnification cannot be ignored in any single or combined
observable, with magnification inducing biases in the
plane, including for cosmic shear and 3x2pt analyses. Significant cosmological
biases exist in the 3x2pt and cosmic shear from magnification of the shear
sample alone. We show that magnification induces significant biases in the mean
of the redshift distribution where a position sample is analysed, which may
potentially be used to identify contamination by magnification.Comment: 17 pages, 7 figures, 3 tables. Submitted to MNRAS. Comments welcom
Starlet higher order statistics for galaxy clustering and weak lensing
We present a first application to photometric galaxy clustering and weak
lensing of wavelet based multi-scale higher order summary statistics: starlet
peak counts and starlet -norm. Peak counts are the local maxima in the
map and the -norm is computed via the sum of the absolute values of the
starlet (wavelet) decomposition coefficients of a map, providing a fast
multi-scale calculation of the pixel distribution, encoding the information of
all pixels in the map. We employ the cosmo-SLICS simulations sources and lenses
catalogues and we compute wavelet based higher order statistics in the context
of combined probes and their potential when applied to the weak lensing
convergence maps and galaxy maps. We get forecasts on the matter density
parameter , the reduced Hubble constant , the matter
fluctuation amplitude , and the dark energy equation of state
parameter . We find that, in our setting for this first application,
considering the two probes as independent, starlet peaks and the -norm
represent interesting summary statistics that can improve the constraints with
respect to the power spectrum also in the case of photometric galaxy clustering
and when the two probes are combined.Comment: A&A Letters to the Editor, Forthcoming article, accepte
Non-Gaussianity in the Weak Lensing Correlation Function Likelihood -- Implications for Cosmological Parameter Biases
We study the significance of non-Gaussianity in the likelihood of weak
lensing shear two-point correlation functions, detecting significantly non-zero
skewness and kurtosis in one-dimensional marginal distributions of shear
two-point correlation functions in simulated weak lensing data. We examine the
implications in the context of future surveys, in particular LSST, with
derivations of how the non-Gaussianity scales with survey area. We show that
there is no significant bias in one-dimensional posteriors of
and due to the non-Gaussian likelihood distributions of shear
correlations functions using the mock data ( deg). We also present a
systematic approach to constructing approximate multivariate likelihoods with
one-dimensional parametric functions by assuming independence or more flexible
non-parametric multivariate methods after decorrelating the data points using
principal component analysis (PCA). While the use of PCA does not modify the
non-Gaussianity of the multivariate likelihood, we find empirically that the
one-dimensional marginal sampling distributions of the PCA components exhibit
less skewness and kurtosis than the original shear correlation
functions.Modeling the likelihood with marginal parametric functions based on
the assumption of independence between PCA components thus gives a lower limit
for the biases. We further demonstrate that the difference in cosmological
parameter constraints between the multivariate Gaussian likelihood model and
more complex non-Gaussian likelihood models would be even smaller for an
LSST-like survey. In addition, the PCA approach automatically serves as a data
compression method, enabling the retention of the majority of the cosmological
information while reducing the dimensionality of the data vector by a factor of
5.Comment: 16 pages, 10 figures, published MNRA
MGLENS: Modified gravity weak lensing simulations for emulation-based cosmological inference
We present MGLENS, a large series of modified gravity lensing simulations tailored for cosmic shear data analyses and forecasts in which cosmological and modified gravity parameters are varied simultaneously. Based on the FORGE and BRIDGE N-body simulation suites presented in companion papers, we construct 100 × 5000 deg2 of mock Stage-IV lensing data from two 4D Latin hypercubes that sample cosmological and gravitational parameters in f(R) and nDGP gravity, respectively. These are then used to validate our inference analysis pipeline based on the lensing power spectrum, exploiting our implementation of these modified gravity models within the COSMOSIS cosmological inference package. Sampling this new likelihood, we find that cosmic shear can achieve 95 per cent CL constraints on the modified gravity parameters of log10[fR0 ] 0.09, after marginalizing over intrinsic alignments of galaxies and including scales up to = 5000. We also investigate the impact of photometric uncertainty, scale cuts, and covariance matrices. We finally explore the consequences of analysing MGLENS data with the wrong gravity model, and report catastrophic biases for a number of possible scenarios. The Stage-IV MGLENS simulations,the FORGE and BRIDGE emulators and the COSMOSIS interface modules will be made publicly available upon journal acceptance
Increasing the Fisher Information Content in the Matter Power Spectrum by Non-linear Wavelet Weiner Filtering
We develop a purely mathematical tool to recover some of the information lost
in the non-linear collapse of large-scale structure. From a set of 141
simulations of dark matter density fields, we construct a non-linear Weiner
filter in order to separate Gaussian and non-Gaussian structure in wavelet
space. We find that the non-Gaussian power is dominant at smaller scales, as
expected from the theory of structure formation, while the Gaussian counterpart
is damped by an order of magnitude on small scales. We find that it is possible
to increase the Fisher information by a factor of three before reaching the
translinear plateau, an effect comparable to other techniques like the linear
reconstruction of the density field.Comment: 7 pages, 6 figures. Accepted for publication in The Astrophysical
Journa
Cosmology from weak lensing peaks and minima with Subaru Hyper Suprime-Cam survey first-year data
We present cosmological constraints derived from peak counts, minimum counts,
and the angular power spectrum of the Subaru Hyper Suprime-Cam first-year (HSC
Y1) weak lensing shear catalog. Weak lensing peak and minimum counts contain
non-Gaussian information and hence are complementary to the conventional
two-point statistics in constraining cosmology. In this work, we forward-model
the three summary statistics and their dependence on cosmology, using a suite
of -body simulations tailored to the HSC Y1 data. We investigate systematic
and astrophysical effects including intrinsic alignments, baryon feedback,
multiplicative bias, and photometric redshift uncertainties. We mitigate the
impact of these systematics by applying cuts on angular scales, smoothing
scales, statistic bins, and tomographic redshift bins. By combining peaks,
minima, and the power spectrum, assuming a flat-CDM model, we obtain
, a 35\%
tighter constraint than that obtained from the angular power spectrum alone.
Our results are in agreement with other studies using HSC weak lensing shear
data, as well as with Planck 2018 cosmology and recent CMB lensing constraints
from the Atacama Cosmology Telescope and the South Pole Telescope
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