# Data Compression and Inference in Cosmology with Self-Supervised Machine Learning

arXiv: https://arxiv.org/abs/2308.09751 · HTML: https://mlsanity.com/paper/2308.09751/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 20 references that are in the map.
- First posted on arXiv 18 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [Understanding and Improving the Role of Projection Head in Self-Supervised Learning](https://mlsanity.com/paper/2212.11491/) (2022)
- [For self-supervised learning, Rationality implies generalization, provably](https://mlsanity.com/paper/2010.08508/) (2020)
- [Towards Good Practices in Self-supervised Representation Learning](https://mlsanity.com/paper/2012.00868/) (2020)
- [Self-supervised similarity search for large scientific datasets](https://mlsanity.com/paper/2110.13151/) (2021)
- [Robust Field-level Likelihood-free Inference with Galaxies](https://mlsanity.com/paper/2302.14101/) (2023)
- [DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection](https://mlsanity.com/paper/2302.02005/) (2023)
- [A brief review of contrastive learning applied to astrophysics](https://mlsanity.com/paper/2306.05528/) (2023)

## Built on by (most cited first, 5 of 5)

- [Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models](https://mlsanity.com/paper/2403.07066/) (2024)
- [PAPERCLIP: Associating Astronomical Observations and Natural Language with Multi-Modal Models](https://mlsanity.com/paper/2403.08851/) (2024)
- [AstroPT: Scaling Large Observation Models for Astronomy](https://mlsanity.com/paper/2405.14930/) (2024)
- [Embed and Emulate: Contrastive representations for simulation-based inference](https://mlsanity.com/paper/2409.18402/) (2024)
- [Dark Energy Survey Year 3 results: Simulation-based wCDM inference from weak lensing and galaxy clustering maps with deep learning: Analysis design](https://mlsanity.com/paper/2511.04681/) (2025)

## Abstract (arXiv)

The influx of massive amounts of data from current and upcoming cosmological surveys necessitates compression schemes that can efficiently summarize the data with minimal loss of information. We introduce a method that leverages the paradigm of self-supervised machine learning in a novel manner to construct representative summaries of massive datasets using simulation-based augmentations. Deploying the method on hydrodynamical cosmological simulations, we show that it can deliver highly informative summaries, which can be used for a variety of downstream tasks, including precise and accurate parameter inference. We demonstrate how this paradigm can be used to construct summary representations that are insensitive to prescribed systematic effects, such as the influence of baryonic physics. Our results indicate that self-supervised machine learning techniques offer a promising new approach for compression of cosmological data as well its analysis.