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[ascl:2312.015] SUNBIRD: Neural-network-based models for galaxy clustering

SUNBIRD trains neural-network-based models for galaxy clustering. It also incorporates pre-trained emulators for different summary statistics, including galaxy two-point correlation function, density-split clustering statistics, and old-galaxy cross-correlation function. These models have been trained on mock galaxy catalogs, and were calibrated to work for specific samples of galaxies. SUNBIRD implements routines with PyTorch to train new neural-network emulators.

Code site:
https://github.com/florpi/sunbird
Described in:
https://ui.adsabs.harvard.edu/abs/2023arXiv230916539C
Bibcode:
2023ascl.soft12015C

Views: 269

ascl:2312.015
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