Multiple physics pretraining for physical surrogate models
M McCabe, BRS Blancard, LH Parker, R Ohana… - ar**
and validating cosmological inference pipelines. A major challenge in generating mock …
and validating cosmological inference pipelines. A major challenge in generating mock …
Explaining dark matter halo density profiles with neural networks
L Lucie-Smith, HV Peiris, A Pontzen - Physical Review Letters, 2024 - APS
We use explainable neural networks to connect the evolutionary history of dark matter halos
with their density profiles. The network captures independent factors of variation in the …
with their density profiles. The network captures independent factors of variation in the …
Hybrid bias and displacement emulators for field-level modelling of galaxy clustering in real and redshift space
M Pellejero Ibañez, RE Angulo… - Monthly Notices of the …, 2024 - academic.oup.com
Recently, hybrid bias expansions have emerged as a powerful approach to modelling the
way in which galaxies are distributed in the Universe. Similarly, field-level emulators have …
way in which galaxies are distributed in the Universe. Similarly, field-level emulators have …
AI-assisted super-resolution cosmological simulations III: time evolution
In this work, we extend our recently developed super-resolution (SR) model for cosmological
simulations to produce fully time-consistent evolving representations of the particle phase …
simulations to produce fully time-consistent evolving representations of the particle phase …
Can denoising diffusion probabilistic models generate realistic astrophysical fields?
Score-based generative models have emerged as alternatives to generative adversarial
networks (GANs) and normalizing flows for tasks involving learning and sampling from …
networks (GANs) and normalizing flows for tasks involving learning and sampling from …