# Estimating Cosmological Parameters from the Dark Matter Distribution

arXiv: https://arxiv.org/abs/1711.02033 · HTML: https://mlsanity.com/paper/1711.02033/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2017.

## Built on by (most cited first, 11 of 11)

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- [Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure](https://mlsanity.com/paper/2308.02636/) (2023)
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- [SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systems](https://mlsanity.com/paper/2212.03410/) (2022)
- [Learnable wavelet neural networks for cosmological inference](https://mlsanity.com/paper/2307.14362/) (2023)

## Abstract (arXiv)

A grand challenge of the 21st century cosmology is to accurately estimate the cosmological parameters of our Universe. A major approach to estimating the cosmological parameters is to use the large-scale matter distribution of the Universe. Galaxy surveys provide the means to map out cosmic large-scale structure in three dimensions. Information about galaxy locations is typically summarized in a "single" function of scale, such as the galaxy correlation function or power-spectrum. We show that it is possible to estimate these cosmological parameters directly from the distribution of matter. This paper presents the application of deep 3D convolutional networks to volumetric representation of dark-matter simulations as well as the results obtained using a recently proposed distribution regression framework, showing that machine learning techniques are comparable to, and can sometimes outperform, maximum-likelihood point estimates using "cosmological models". This opens the way to estimating the parameters of our Universe with higher accuracy.