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Abstract Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter (DM) components that cannot be directly observed. Galaxy formation simulations can be used to study the relationship between DM density fields and galaxy distributions. However, this relationship can be sensitive to assumptions in cosmology and astrophysical processes embedded in galaxy formation models, which remain uncertain in many aspects. In this work, we develop a diffusion generative model to reconstruct DM fields from galaxies. The diffusion model is trained on the CAMELS simulation suite that contains thousands of state-of-the-art galaxy formation simulations with varying cosmological parameters and subgrid astrophysics. We demonstrate that the diffusion model can predict the unbiased posterior distribution of the underlying DM fields from the given stellar density fields while being able to marginalize over uncertainties in cosmological and astrophysical models. Interestingly, the model generalizes to simulation volumes ≈500 times larger than those it was trained on and across different galaxy formation models. The code for reproducing these results can be found athttps://github.com/victoriaono/variational-diffusion-cdm✎.
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This content will become publicly available on March 1, 2026
Aemulus ν : precision halo mass functions in w ν CDM cosmologies
Abstract Precise and accurate predictions of the halo mass function for cluster mass scales inwνCDM cosmologies are crucial for extracting robust and unbiased cosmological information from upcoming galaxy cluster surveys.Here, we present a halo mass function emulator for cluster mass scales (≳ 1013M⊙/h) up to redshiftz= 2 with comprehensive support for the parameter space ofwνCDM cosmologies allowed by current data.Based on theAemulusνsuite of simulations, the emulator marks a significant improvement in the precision of halo mass function predictions by incorporating both massive neutrinos and non-standard dark energy equation of state models.This allows for accurate modeling of the cosmology dependence in large-scale structure and galaxy cluster studies.We show that the emulator, designed using Gaussian Process Regression, has negligible theoretical uncertainties compared to dominant sources of error in future cluster abundance studies.Our emulator is publicly available (https://github.com/DelonShen/aemulusnu_hmf), providing the community with a crucial tool for upcoming cosmological surveys such as LSST and Euclid.
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- Award ID(s):
- 2009291
- PAR ID:
- 10638792
- Publisher / Repository:
- IOP
- Date Published:
- Journal Name:
- Journal of Cosmology and Astroparticle Physics
- Volume:
- 2025
- Issue:
- 03
- ISSN:
- 1475-7516
- Page Range / eLocation ID:
- 056
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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