Mining for cosmological information: Simulation-based methods for Redshift Space Distortions and Galaxy Clustering

The standard model of cosmology describes the complex large scale structure of the Universe through less than 10 free parameters. However, concordance with observations requires that about 95\% of the energy content of the universe is invisible to us. Most of this energy is postulated to be in the form of a cosmological constant, $\Lambda$, which drives the observed accelerated expansion of the Universe. Its nature is, however, unknown. This mystery forces cosmologists to look for inconsistencies between theory and data, searching for clues. But finding statistically significant contradictions requires extremely accurate measurements of the composition of the Universe, which are at present limited by our inability to extract all the information contained in the data, rather than being limited by the data itself. In this Thesis, we study how we can overcome these limitations by i) modelling how galaxies cluster on small scales with simulation-based methods, where perturbation theory fails to provide accurate predictions, and ii) developing summary statistics of the density field that are capable of extracting more information than the commonly used two-point functions. In the first half, we show how the real to redshift space mapping can be modelled accurately by going beyond the Gaussian approximation for the pairwise velocity distribution. We then show that simulation-based models can accurately predict the full shape of galaxy clustering in real space, increasing the constraining power on some of the cosmological parameters by a factor of 2 compared to perturbation theory methods. In the second half, we measure the information content of density dependent clustering. We show that it can improve the constraints on all cosmological parameters by factors between 3 and 8 over the two-point function. In particular, exploiting the environment dependence can constrain the mass of neutrinos by a factor of 8$ better than the two-point correlation function alone. We hope that the techniques described in this thesis will contribute to extracting all the cosmological information contained in ongoing and upcoming galaxy surveys, and provide insight into the nature of the accelerated expansion of the universe

A point cloud approach to generative modeling for galaxy surveys at the field level

We introduce a diffusion-based generative model to describe the distribution of galaxies in our Universe directly as a collection of points in 3-D space (coordinates) optionally with associated attributes (e.g., velocities and masses), without resorting to binning or voxelization. The custom diffusion model can be used both for emulation, reproducing essential summary statistics of the galaxy distribution, as well as inference, by computing the conditional likelihood of a galaxy field. We demonstrate a first application to massive dark matter haloes in the Quijote simulation suite. This approach can be extended to enable a comprehensive analysis of cosmological data, circumventing limitations inherent to summary statistic -- as well as neural simulation-based inference methods.Comment: 15+3 pages, 7+4 figure

Probabilistic reconstruction of Dark Matter fields from biased tracers using diffusion models

Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter components that cannot be directly observed. The relationship between dark matter density fields and galaxy distributions can be sensitive to assumptions in cosmology and astrophysical processes embedded in the galaxy formation models, that remain uncertain in many aspects. Based on state-of-the-art galaxy formation simulation suites with varied cosmological parameters and sub-grid astrophysics, we develop a diffusion generative model to predict the unbiased posterior distribution of the underlying dark matter fields from the given stellar mass fields, while being able to marginalize over the uncertainties in cosmology and galaxy formation

Learning an Effective Evolution Equation for Particle-Mesh Simulations Across Cosmologies

Particle-mesh simulations trade small-scale accuracy for speed compared to traditional, computationally expensive N-body codes in cosmological simulations. In this work, we show how a data-driven model could be used to learn an effective evolution equation for the particles, by correcting the errors of the particle-mesh potential incurred on small scales during simulations. We find that our learnt correction yields evolution equations that generalize well to new, unseen initial conditions and cosmologies. We further demonstrate that the resulting corrected maps can be used in a simulation-based inference framework to yield an unbiased inference of cosmological parameters. The model, a network implemented in Fourier space, is exclusively trained on the particle positions and velocities.Comment: 7 pages, 4 figures, Machine Learning and the Physical Sciences Workshop, NeurIPS 202

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

Constraining νΛ\nu \LambdaCDM with density-split clustering

The dependence of galaxy clustering on local density provides an effective method for extracting non-Gaussian information from galaxy surveys. The two-point correlation function (2PCF) provides a complete statistical description of a Gaussian density field. However, the late-time density field becomes non-Gaussian due to non-linear gravitational evolution and higher-order summary statistics are required to capture all of its cosmological information. Using a Fisher formalism based on halo catalogues from the Quijote simulations, we explore the possibility of retrieving this information using the density-split clustering (DS) method, which combines clustering statistics from regions of different environmental density. We show that DS provides more precise constraints on the parameters of the $\nu \Lambda$CDM model compared to the 2PCF, and we provide suggestions for where the extra information may come from. DS improves the constraints on the sum of neutrino masses by a factor of $8$ and by factors of 5, 3, 4, 6, and 6 for $\Omega_m$, $\Omega_b$, $h$, $n_s$, and $\sigma_8$, respectively. We compare DS statistics when the local density environment is estimated from the real or redshift-space positions of haloes. The inclusion of DS autocorrelation functions, in addition to the cross-correlation functions between DS environments and haloes, recovers most of the information that is lost when using the redshift-space halo positions to estimate the environment. We discuss the possibility of constructing simulation-based methods to model DS clustering statistics in different scenarios.Comment: Submitted to MNRAS. Source code for all figures in the paper is provided in the caption

Simulation-based Inference for Exoplanet Atmospheric Retrieval: Insights from winning the Ariel Data Challenge 2023 using Normalizing Flows

Advancements in space telescopes have opened new avenues for gathering vast amounts of data on exoplanet atmosphere spectra. However, accurately extracting chemical and physical properties from these spectra poses significant challenges due to the non-linear nature of the underlying physics. This paper presents novel machine learning models developed by the AstroAI team for the Ariel Data Challenge 2023, where one of the models secured the top position among 293 competitors. Leveraging Normalizing Flows, our models predict the posterior probability distribution of atmospheric parameters under different atmospheric assumptions. Moreover, we introduce an alternative model that exhibits higher performance potential than the winning model, despite scoring lower in the challenge. These findings highlight the need to reevaluate the evaluation metric and prompt further exploration of more efficient and accurate approaches for exoplanet atmosphere spectra analysis. Finally, we present recommendations to enhance the challenge and models, providing valuable insights for future applications on real observational data. These advancements pave the way for more effective and timely analysis of exoplanet atmospheric properties, advancing our understanding of these distant worlds.Comment: Conference proceeding for the ECML PKDD 202

Gravitational corrections to light propagation in a perturbed FLRW-universe and corresponding weak lensing spectra

When the gravitational lensing of the large-scale structure is calculated from a cosmological model a few assumptions enter: (i) one assumes that the photons follow unperturbed background geodesics, which is usually referred to as the Born-approximation, (ii) the lenses move slowly, (iii) the source-redshift distribution is evaluated relative to the background quantities and (iv) the lensing effect is linear in the gravitational potential. Even though these approximations are small individually they could sum up, especially since they include local effects such as the Sachs-Wolfe and peculiar motion, but also non-local ones like the Born-approximation and the integrated Sachs-Wolfe effect. In this work we will address all points mentioned and perturbatively calculate the effect on a tomographic cosmic shear power spectrum of each effect individually as well as all cross-correlations. Our findings show that each effect is at least 4 to 5 orders of magnitude below the leading order lensing signal. Finally we sum up all effects to estimate the overall impact on parameter estimation by a future cosmological weak lensing survey such as Euclid in a wCDM cosmology with parametrisation Ωm, σ8, ns, h, w0 and wa, using 5 tomographic bins. We consistently find a parameter bias of 10−5, which is therefore completely negligible for all practical purposes, confirming that other effects such as intrinsic alignments, magnification bias and uncertainties in the redshift distribution will be the dominant systematic source in future surveys

Towards a non-Gaussian model of redshift space distortions

To understand the nature of the accelerated expansion of the Universe, we need to combine constraints on the expansion rate and growth of structure. The growth rate is usually extracted from 3D galaxy maps by exploiting the effects of peculiar motions on galaxy clustering. However, theoretical models of the probability distribution function (PDF) of galaxy pairwise peculiar velocities are not accurate enough on small scales to reduce the error on theoretical predictions to the level required to match the precision expected for measurements from future surveys. Here, we improve the modelling of the pairwise velocity distribution by using the Skew-T PDF, which has non-zero skewness and kurtosis. Our model accurately reproduces the redshift space multipoles (monopole, quadrupole, and hexadecapole) predicted by N-body simulations, above scales of about 10h−1Mpc⁠. We illustrate how a Taylor expansion of the streaming model can reveal the contributions of the different moments to the clustering multipoles, which are independent of the shape of the velocity PDF. The Taylor expansion explains why the Gaussian streaming model works well in predicting the first two redshift space multipoles, although the velocity PDF is non-Gaussian even on large scales. Indeed, any PDF with the correct first two moments would produce precise results for the monopole down to scales of about 10h−1Mpc⁠, and for the quadrupole down to about 30h−1Mpc⁠. An accurate model for the hexadecapole needs to include higher order moments