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Abstract The rapid advancement of large-scale cosmological simulations has opened new avenues for cosmological and astrophysical research. However, the increasing diversity among cosmological simulation models presents a challenge to therobustness. In this work, we develop the Model-Insensitive ESTimator (Miest), a machine that canrobustlyestimate the cosmological parameters, Ωmandσ8, from neural hydrogen maps of simulation models in the Cosmology and Astrophysics with MachinE Learning Simulations project—IllustrisTNG,SIMBA, Astrid, and SWIFT-Eagle. An estimator is consideredrobustif it possesses a consistent predictive power across all simulations, including those used during the training phase. We train our machine using multiple simulation models and ensure that it only extracts common features between the models while disregarding the model-specific features. This allows us to develop a novel model that is capable of accurately estimating parameters across a range of simulation models, without being biased toward any particular model. Upon the investigation of the latent space—a set of summary statistics, we find that the implementation ofrobustnessleads to the blending of latent variables across different models, demonstrating the removal of model-specific features. In comparison to a standard machine lackingrobustness, the average performance of Mieston the unseen simulations during the training phase has been improved by ∼17% for Ωmand 38% forσ8. By using a machine learning approach that can extractrobust, yet physical features, we hope to improve our understanding of galaxy formation and evolution in a (subgrid) model-insensitive manner, and ultimately, gain insight into the underlying physical processes responsible forrobustness.
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This content will become publicly available on March 11, 2026
Indium( iii )–terpyridine complexes: synthesis, structures and remarkable antifungal and anticancer activities
Bioactive indium(iii)–terpyridine complexes are synthesized and structurally characterized. These compounds demostrated antifungal activity against variousCandidaspecies, as well as antiproliferative activity against human breast cancer cells.
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- PAR ID:
- 10592377
- Publisher / Repository:
- Royal Society of Chemistry
- Date Published:
- Journal Name:
- Dalton Transactions
- Volume:
- 54
- Issue:
- 11
- ISSN:
- 1477-9226
- Page Range / eLocation ID:
- 4656 to 4663
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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