Emulating the CFHTLenS weak lensing data: Cosmological constraints from moments and Minkowski functionals

Petri, Andrea; Liu, Jia; Haiman, Zoltán; May, M.; Hui, Lam; Kratochvíl, Jan

doi:10.1103/physrevd.91.103511

Cited by 101 publications

(99 citation statements)

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“…For each such bin we calculate the peak counts, as well as the first three Minkowski functionals (e.g. Kratochvil et al 2012;Petri et al 2015, and references therein), which concludes our collection of 99 features.The right panel of figure 1 shows examples for the variation between models for such classical features. Presented there is the average power spectrum and peak count for all maps in the test set and for the four instructive models we chose in section 3.1 for data visualisation purposes.…”

Section: Standard Mass Map Descriptorsmentioning

confidence: 99%

“…Recent analyses of the weak lensing (WL) (Bartelmann & Schneider 2001) data from CFHTLens (Heymans et al 2012) used either higher-order (>2) moments of the convergence field (Van Waerbeke et al 2013), or Minkowski functionals (Petri et al 2015) to draw cosmological inference from a data description that goes beyond two-point statistics. Martinet et al (2018) and Shan et al (2018) applied peak count statistics (Dietrich & Hartlap 2010;Kratochvil et al 2010) to shear and convergence fields from KiDS (Hildebrandt et al 2017) and Gruen et al (2018) used countsin-cells (Friedrich et al 2018) to extract information from the DES (Abbott et al 2018) catalogues.…”

Section: Introductionmentioning

confidence: 99%

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On the dissection of degenerate cosmologies with machine learning

Merten¹,

Giocoli

Baldi

et al. 2019

Monthly Notices of the Royal Astronomical Society

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Based on the DUSTGRAIN-pathfinder suite of simulations, we investigate observational degeneracies between nine models of modified gravity and massive neutrinos. Three types of machine learning techniques are tested for their ability to discriminate lensing convergence maps by extracting dimensional reduced representations of the data. Classical map descriptors such as the power spectrum, peak counts and Minkowski functionals are combined into a joint feature vector and compared to the descriptors and statistics that are common to the field of digital image processing. To learn new features directly from the data we use a Convolutional Neural Network (CNN). For the mapping between feature vectors and the predictions of their underlying model, we implement two different classifiers; one based on a nearest-neighbour search and one that is based on a fully connected neural network. We find that the neural network provides a much more robust classification than the nearest-neighbour approach and that the CNN provides the most discriminating representation of the data. It achieves the cleanest separation between the different models and the highest classification success rate of 59% for a single source redshift. Once we perform a tomographic CNN analysis, the total classification accuracy increases significantly to 76% with no observational degeneracies remaining. Visualising the filter responses of the CNN at different network depths provides us with the unique opportunity to learn from very complex models and to understand better why they perform so well.

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Section: Standard Mass Map Descriptorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the dissection of degenerate cosmologies with machine learning

Merten¹,

Giocoli

Baldi

et al. 2019

Monthly Notices of the Royal Astronomical Society

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show abstract

“…The auto-correlation function of galaxies' positions in the large-scale structure is another powerful probe of cosmology (e.g. Peebles 1980). It can be measured from a wide-area spectroscopic sample, and is relatively easy to measure, i.e., with high signal-to-noise ratios.…”

Section: Introductionmentioning

confidence: 99%

Dark Quest. I. Fast and Accurate Emulation of Halo Clustering Statistics and Its Application to Galaxy Clustering

Nishimichi

Takada

Takahashi

et al. 2019

ApJ

208

202

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We perform an ensemble of N-body simulations with 2048 3 particles for 101 flat wCDM cosmological models sampled based on a maximin-distance Sliced Latin Hypercube Design. By using the halo catalogs extracted at multiple redshifts in the range of z = [0, 1.48], we develop Dark Emulator, which enables fast and accurate computations of the halo mass function, halo-matter cross-correlation, and halo auto-correlation as a function of halo masses, redshift, separations and cosmological models, based on the Principal Component Analysis and the Gaussian Process Regression for the large-dimensional input and output data vector. We assess the performance of the emulator using a validation set of N-body simulations that are not used in training the emulator. We show that, for typical halos hosting CMASS galaxies in the Sloan Digital Sky Survey, the emulator predicts the halo-matter cross correlation, relevant for galaxy-galaxy weak lensing, with an accuracy better than 2% and the halo auto-correlation, relevant for galaxy clustering correlation, with an accuracy better than 4%. We give several demonstrations of the emulator. It can be used to study properties of halo mass density profiles such as the mass-concentration relation and splashback radius for different cosmologies. The emulator outputs can be combined with an analytical prescription of halo-galaxy connection such as the halo occupation distribution at the equation level, instead of using the mock catalogs, to make accurate predictions of galaxy clustering statistics such as the galaxy-galaxy weak lensing and the projected correlation function for any model within the wCDM cosmologies, in a few CPU seconds. Subject headings: large-scale structure of the universe -numerical simulations -machine learning

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“…We set α = 0.59 because it yields the most tightly constrained combination (for b ph = 0) using the procedure outlined in Petri et al (2015). The right panel in Figure 4 shows how n pk is modified in a cosmology with a higher Σ 8 .…”

Section: The Lower Left Panel Ofmentioning

confidence: 99%

“…Petri et al 2015), higher-order correlation functions (e.g. Fu et al 2014), and Minkowski functionals (e.g Petri et al 2015). These summary statistics provide the opportunity to increase the amount of cosmological information extracted from cosmic shear.…”

Section: Introductionmentioning

confidence: 99%

The impact of photometric redshift errors on lensing statistics in ray-tracing simulations

Abruzzo

Haiman

2019

Monthly Notices of the Royal Astronomical Society

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Weak lensing surveys are reaching sensitivities at which uncertainties in the galaxy redshift distributions n(z) from photo-z errors degrade cosmological constraints. We use ray-tracing simulations and a simple treatment of photo-z errors to assess cosmological parameter biases from uncertainties in n(z) in an LSST-like survey. We use the power spectrum and the abundance of lensing peaks to infer cosmological parameters, and find that the former is somewhat more resilient to photo-z errors. We place conservative lower limits on the survey size at which different types of photo-z errors degrade ΛCDM (wCDM) parameter constraints by 50%. A residual constant photo-z bias of |δz| < 0.003(1 + z), satisfying the current LSST requirement, does not significantly degrade constraints for surveys smaller than ≈1300 (≈490) deg 2 using lensing peaks and ≈6500 (≈4900) deg 2 using the power spectrum. Adopting a recent prediction for LSST's full photo-z probability distribution function (PDF), we find that simply approximating n(z) with the photo-z galaxy distribution directly computed from this PDF would degrade surveys as small as ≈60 (≈65) deg 2 using lensing peaks or the power spectrum. Assuming that the centroid bias in each tomographic redshift bin can be removed from the photo-z galaxy distribution, using lensing peaks or the power spectrum still degrades surveys larger than ≈200 (≈255) or ≈248 (≈315) deg 2 . These results imply that the expected broad photo-z PDF significantly biases parameters, which needs to be further mitigated using more sophisticated photo-z treatments.

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Emulating the CFHTLenS weak lensing data: Cosmological constraints from moments and Minkowski functionals

Cited by 101 publications

References 50 publications

On the dissection of degenerate cosmologies with machine learning

On the dissection of degenerate cosmologies with machine learning

Dark Quest. I. Fast and Accurate Emulation of Halo Clustering Statistics and Its Application to Galaxy Clustering

The impact of photometric redshift errors on lensing statistics in ray-tracing simulations

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