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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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Post-synthetic modification of bis-iron( iii )-μ-oxo-porphyrin prisms to enhance oxygen reduction electrocatalysis
Bis-iron(iii)-μ-oxo-porphyrin prisms may be tunedviapost-synthetic tethering. Tethered prisms show enhanced selectivity for the Oxygen Reduction Reaction (ORR) and maintain a cofacial geometry under homogeneous ORR conditions.
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- Award ID(s):
- 1847950
- PAR ID:
- 10537036
- Publisher / Repository:
- RSC
- Date Published:
- Journal Name:
- Inorganic Chemistry Frontiers
- Volume:
- 11
- Issue:
- 17
- ISSN:
- 2052-1553
- Page Range / eLocation ID:
- 5557 to 5565
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/d4qi01219d
