Janis Fluri

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Fluri

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Janis

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0000-0002-4813-4935

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Publications 1 - 10 of 13

Cosmological constraints from noisy convergence maps through deep learning
Fluri, Janis; Kacprzak, Tomasz; Refregier, Alexandre; et al. (2018)
Physical Review D
Deep learning is a powerful analysis technique that has recently been proposed as a method to constrain cosmological parameters from weak lensing mass maps. Because of its ability to learn relevant features from the data, it is able to extract more information from the mass maps than the commonly used power spectrum, and thus achieve better precision for cosmological parameter measurement. We explore the advantage of convolutional neural networks over the power spectrum for varying levels of shape noise and different smoothing scales applied to the maps. We compare the cosmological constraints from the two methods in the ΩM−σ8 plane for sets of 400 deg2 convergence maps. We find that, for a shape noise level corresponding to 8.53 galaxies/arcmin2 and the smoothing scale of σs=2.34 arcmin, the network is able to generate 45% tighter constraints. For a smaller smoothing scale of σs=1.17 the improvement can reach ∼50%, while for a larger smoothing scale of σs=5.85, the improvement decreases to 19%. The advantage generally decreases when the noise level and smoothing scales increase. We present a new training strategy to train the neural network with noisy data, as well as considerations for practical applications of the deep learning approach.
A tomographic spherical mass map emulator of the KiDS-1000 survey using conditional generative adversarial networks
Yiu, Timothy Wing Hei; Fluri, Janis; Kacprzak, Tomasz (2022)
Journal of Cosmology and Astroparticle Physics
Large sets of matter density simulations are becoming increasingly important in large-scale structure cosmology. Matter power spectra emulators, such as the Euclid Emulator and CosmicEmu, are trained on simulations to correct the non-linear part of the power spectrum. Map-based analyses retrieve additional non-Gaussian information from the density field, whether through human-designed statistics such as peak counts, or via machine learning methods such as convolutional neural networks. The simulations required for these methods are very resource-intensive, both in terms of computing time and storage. This creates a computational bottleneck for future cosmological analyses, as well as an entry barrier for testing new, innovative ideas in the area of cosmological information retrieval. Map-level density field emulators, based on deep generative models, have recently been proposed to address these challenges. In this work, we present a novel mass map emulator of the KiDS-1000 survey footprint, which generates noise-free spherical maps in a fraction of a second. It takes a set of cosmological parameters (ΩM, σ8) as input and produces a consistent set of 5 maps, corresponding to the KiDS-1000 tomographic redshift bins. To construct the emulator, we use a conditional generative adversarial network architecture and the spherical convolutional neural network DeepSphere, and train it on N-body-simulated mass maps. We compare its performance using an array of quantitative comparison metrics: angular power spectra Cℓ, pixel/peaks distributions, Cℓ correlation matrices, and Structural Similarity Index. Overall, the average agreement on these summary statistics is <10% for the cosmologies at the centre of the simulation grid, and degrades slightly on grid edges. However, the quality of the generated maps is worse at high negative κ values or large scale, which can significantly affect summaries sensitive to such observables. Finally, we perform a mock cosmological parameter estimation using the emulator and the original simulation set. We find good agreement in these constraints, for both likelihood and likelihood-free approaches. The emulator is available.
Predicting cosmological observables with PyCosmo
Tarsitano, Federica; Schmitt, U.; Refregier, Alexandre; et al. (2021)
Astronomy and Computing
Current and upcoming cosmological experiments open a new era of precision cosmology, thus demanding accurate theoretical predictions for cosmological observables. Because of the complexity of the codes delivering such predictions, reaching a high level of numerical accuracy is challenging. Among the codes already fulfilling this task, PyCosmo is a Python-based framework providing solutions to the Einstein–Boltzmann equations and accurate predictions for cosmological observables. We present the first public release of the code, which is valid in ΛCDM cosmology. The novel aspect of this version is that the user can work within a Python framework, either locally or through an online platform, called PyCosmo Hub. In this work we first describe how the observables are implemented. Then, we check the accuracy of the theoretical predictions for background quantities, power spectra and Limber and beyond-Limber angular power spectra by comparison with other codes: the Core Cosmology Library (CCL), CLASS, HMCode and iCosmo. In our analysis we quantify the agreement of PyCosmo with the other codes, for a range of cosmological models, monitored through a series of unit tests. PyCosmo, conceived as a multi-purpose cosmology calculation tool in Python, is designed to be interactive and user-friendly. The PyCosmo Hub is accessible from this link: https://cosmology.ethz.ch/research/software-lab/PyCosmo.html. On this platform the users can perform their own computations using Jupyter Notebooks without the need of installing any software, access to the results presented in this work and benefit from tutorial notebooks illustrating the usage of the code. The link above also redirects to the code release and documentation.
Symbolic implementation of extensions of the PyCosmo Boltzmann solver
Moser, Beatrice; Lorenz, Cristiane S.; Schmitt, Uwe; et al. (2022)
Astronomy and Computing
PyCosmo is a Python-based framework for the fast computation of cosmological model predictions. One of its core features is the symbolic representation of the Einstein-Boltzmann system of equations. Efficient C/C++ code is generated from the SymPy symbolic expressions making use of the sympy2c package. This enables easy extensions of the equation system for the implementation of new cosmological models. We illustrate this with three extensions of the PyCosmo Boltzmann solver to include a dark energy component with a constant equation of state, massive neutrinos and a radiation streaming approximation. We describe the PyCosmo framework, highlighting new features, and the symbolic implementation of the new models. We compare the PyCosmo predictions for the ?CDM model extensions with CLASS, both in terms of accuracy and computational speed. We find a good agreement, to better than 0.1% when using high-precision settings and a comparable computational speed. Links to the Python Package Index (PyPI) page of the code release and to the PyCosmo Hub, an online platform where the package is installed, are available at: https://cosmology.ethz.ch/research/softwarelab/PyCosmo.html. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
CosmoGridV1: a simulated wCDM theory prediction for map-level cosmological inference
Kacprzak, Tomasz; Fluri, Janis; Schneider, Aurel; et al. (2023)
Journal of Cosmology and Astroparticle Physics
We present CosmoGridV1: a large set of lightcone simulations for map-level cosmological inference with probes of large scale structure. It is designed for cosmological parameter measurement based on Stage-III photometric surveys with non-Gaussian statistics and machine learning. CosmoGridV1 spans the wCDM model by varying Ωm, σ 8, w 0, H 0, n s, Ω b , and assumes three degenerate neutrinos with fixed ∑ mν = 0.06 eV. This space is covered by 2500 grid points on a Sobol sequence. At each grid point, we run 7 simulations with PkdGrav3 and store 69 particle maps at nside = 2048 up to z = 3.5, as well as halo catalog snapshots. The fiducial cosmology has 200 independent simulations, along with their stencil derivatives. An important part of CosmoGridV1 is the benchmark set of 28 simulations, which include larger boxes, higher particle counts, and higher redshift resolution of shells. They allow for testing if new types of analyses are sensitive to choices made in CosmoGridV1. We add baryon feedback effects on the map level, using shell-based baryon correction model. The shells are used to create maps of weak gravitational lensing, intrinsic alignment, and galaxy clustering, using the UFalcon code. The main part of CosmoGridV1 are the raw particle count shells that can be used to create full-sky maps for a given n(z). We also release projected maps for a Stage-III forecast, as well as maps used previously in KiDS-1000 deep learning constraints with CosmoGridV1. The data is available at http://www.cosmogrid.ai/.
DeepLSS: Breaking Parameter Degeneracies in Large-Scale Structure with Deep-Learning Analysis of Combined Probes
Kacprzak, Tomasz; Fluri, Janis (2022)
Physical Review X
In classical cosmological analysis of large-scale structure surveys with two-point functions, the parameter measurement precision is limited by several key degeneracies within the cosmology and astrophysics sectors. For cosmic shear, clustering amplitude σ8 and matter density ωm roughly follow the S8=σ8(ωm/0.3)0.5 relation. In turn, S8 is highly correlated with the intrinsic galaxy alignment amplitude AIA. For galaxy clustering, the bias bg is degenerate with both σ8 and ωm, as well as the stochasticity rg. Moreover, the redshift evolution of intrinsic alignment (IA) and bias can cause further parameter confusion. A tomographic two-point probe combination can partially lift these degeneracies. In this work we demonstrate that a deep-learning analysis of combined probes of weak gravitational lensing and galaxy clustering, which we call DeepLSS, can effectively break these degeneracies and yield significantly more precise constraints on σ8, ωm, AIA, bg, rg, and IA redshift evolution parameter ηIA. In a simulated forecast for a stage-III survey, we find that the most significant gains are in the IA sector: the precision of AIA is increased by approximately 8 times and is almost perfectly decorrelated from S8. Galaxy bias bg is improved by 1.5 times, stochasticity rg by 3 times, and the redshift evolution ηIA and ηb by 1.6 times. Breaking these degeneracies leads to a significant gain in constraining power for σ8 and ωm, with the figure of merit improved by 15 times. We give an intuitive explanation for the origin of this information gain using sensitivity maps. These results indicate that the fully numerical, map-based forward-modeling approach to cosmological inference with machine learning may play an important role in upcoming large-scale structure surveys. We discuss perspectives and challenges in its practical deployment for a full survey analysis.
Towards a full wCDM map-based analysis for weak lensing surveys
Zürcher, Dominik; Fluri, Janis; Ajani, Virginia; et al. (2023)
Monthly Notices of the Royal Astronomical Society
The next generation of weak lensing surveys will measure the matter distribution of the local Universe with unprecedented precision, allowing the resolution of non-Gaussian features of the convergence field. This encourages the use of higher-order mass-map statistics for cosmological parameter inference. We extend the forward-modelling based methodology introduced in a previous forecast paper to match these new requirements. We provide multiple forecasts for the wCDM parameter constraints that can be expected from stage 3 and 4 weak lensing surveys. We consider different survey setups, summary statistics and mass map filters including wavelets. We take into account the shear bias, photometric redshift uncertainties and intrinsic alignment. The impact of baryons is investigated and the necessary scale cuts are applied. We compare the angular power spectrum analysis to peak and minima counts as well as Minkowski functionals of the mass maps. We find a preference for Starlet over Gaussian filters. Our results suggest that using a survey setup with 10 instead of 5 tomographic redshift bins is beneficial. Adding cross-tomographic information improves the constraints on cosmology and especially on galaxy intrinsic alignment for all statistics. In terms of constraining power, we find the angular power spectrum and the peak counts to be equally matched for stage 4 surveys, followed by minima counts and the Minkowski functionals. Combining different summary statistics significantly improves the constraints and compensates the stringent scale cuts. We identify the most ‘cost-effective’ combination to be the angular power spectrum, peak counts and Minkowski functionals following Starlet filtering.
Full wCDM analysis of KiDS-1000 weak lensing maps using deep learning
Fluri, Janis; Kacprzak, Tomasz; Lucchi, Aurelien; et al. (2022)
Physical Review D
We present a full forward-modeled wCDM analysis of the KiDS-1000 weak lensing maps using graph-convolutional neural networks (GCNN). Utilizing the cosmogrid, a novel massive simulation suite spanning six different cosmological parameters, we generate almost one million tomographic mock surveys on the sphere. Due to the large dataset size and survey area, we perform a spherical analysis while limiting our map resolution to HEALPix nside=512. We marginalize over systematics such as photometric redshift errors, multiplicative calibration and additive shear bias. Furthermore, we use a map-level implementation of the nonlinear intrinsic alignment model along with a novel treatment of baryonic feedback to incorporate additional astrophysical nuisance parameters. We also perform a spherical power spectrum analysis for comparison. The constraints of the cosmological parameters are generated using a likelihood-free inference method called Gaussian process approximate Bayesian computation (GPABC). Finally, we check that our pipeline is robust against choices of the simulation parameters. We find constraints on the degeneracy parameter of S8σ8ωM/0.3=0.78-0.06+0.06 for our power spectrum analysis and S8=0.79-0.05+0.05 for our GCNN analysis, improving the former by 16%. This is consistent with earlier analyses of the 2-point function, albeit slightly higher. Baryonic corrections generally broaden the constraints on the degeneracy parameter by about 10%. These results offer great prospects for full machine learning based analyses of ongoing and future weak lensing surveys.
Cosmological parameter estimation and inference using deep summaries
Fluri, Janis; Kacprzak, Tomasz; Refregier, Alexandre; et al. (2021)
Physical Review D
The ability to obtain reliable point estimates of model parameters is of crucial importance in many fields of physics. This is often a difficult task given that the observed data can have a very high number of dimensions. In order to address this problem, we propose a novel approach to construct parameter estimators with a quantifiable bias using an order expansion of highly compressed deep summary statistics of the observed data. These summary statistics are learned automatically using an information maximising loss. Given an observation, we further show how one can use the constructed estimators to obtain approximate Bayes computation (ABC) posterior estimates and their corresponding uncertainties that can be used for parameter inference using Gaussian process regression even if the likelihood is not tractable. We validate our method with an application to the problem of cosmological parameter inference of weak lensing mass maps. We show in that case that the constructed estimators are unbiased and have an almost optimal variance, while the posterior distribution obtained with the Gaussian process regression is close to the true posterior and performs better or equally well than comparable methods. © 2021 American Physical Society
Dark energy survey year 3 results: Cosmology with peaks using an emulator approach
Zürcher, Dominik; Fluri, Janis; Sgier, Raphaël; et al. (2022)
Monthly Notices of the Royal Astronomical Society
We constrain the matter density Omega(m) and the amplitude of density fluctuations sigma(s) within the Lambda CDM cosmological model with shear peak statistics and angular convergence power spectra using mass maps constructed from the first three years of data of the Dark Energy Survey (DES Y3). We use tomographic shear peak statistics, including cross-peaks: peak counts calculated on maps created by taking a harmonic space product of the convergence of two tomographic redshift bins. Our analysis follows a forward-modelling scheme to create a likelihood of these statistics using N-body simulations, using a Gaussian process emulator. We take into account the uncertainty from the remaining, largely unconstrained Lambda CDM parameters (Omega(b), n(s), and h). We include the following lensing systematics: multiplicative shear bias, photometric redshift uncertainty, and galaxy intrinsic alignment. Stringent scale cuts are applied to avoid biases from unmodelled baryonic physics. We find that the additional non-Gaussian information leads to a tightening of the constraints on the structure growth parameter yielding S-8 sigma(8)root Omega(m)/0.3 = 0.797(-0.013)(+0.015) (68 per cent confidence limits), with a precision of 1.8 per cent, an improvement of 38 per cent compared to the angular power spectra only case. The results obtained with the angular power spectra and peak counts are found to be in agreement with each other and no significant difference in S-8 is recorded. We find a mild tension of 1.5 a between our study and the results from Planck 2018, with our analysis yielding a lower S-8. Furthermore, we observe that the combination of angular power spectra and tomographic peak counts breaks the degeneracy between galaxy intrinsic alignment A(IA) and S-8, improving cosmological constraints. We run a suite of tests concluding that our results are robust and consistent with the results from other studies using DES Y3 data.

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