florpi
https://florpi.github.io/
IAIFI Fellow
Carol(ina) Cuesta-Lazaro
1. Probabilistic debiasing of the cosmic web
2. Physical priors on EFT bias parameters
3. How would a Machine learn a "bias expansion"?
Base Distribution
Target Distribution
Bridging two distributions
Make the data as likely as possible
Prompt
A person half Yoda, half Gandalf
1 to Many:
["Debiasing with Diffusion: Probabilistic reconstruction of Dark Matter fields from galaxies" Ono et al arXiv:2403.10648]
Victoria Ono
Core Park
Truth
Sampled
Observed
Small
Large
Scale (k)
Power Spectrum
Cross correlation
TNG-300
True DM
Inferred DM
Size of training simulation
Galaxy Cluster
Void
[arXiv:2403.10648]
Model trained on Astrid subgrid model
["3D Reconstruction of Dark Matter Fields with Diffusion Models: Towards Application to Galaxy Surveys" Park, Mudur, Cuesta-Lazaro et al (in-prep)]
Posterior Sample
Posterior Mean
[Video credit: Francisco Villaescusa-Navarro]
Gas density
Gas temperature
Subgrid model 1
Subgrid model 2
Subgrid model 3
Subgrid model 4
Mikhail Ivanov
Robust galaxy bias model: Effective field Theories
+ Simulation as priors
Field-level EFT
["Full-shape analysis with simulation-based priors: constraints on single field inflation from BOSS" Ivanov, Cuesta-Lazaro et al arXiv:2402.13310]
Andrej Obuljen
Michael Toomey
["Full-shape analysis with simulation-based priors: cosmological parameters and the structure growth anomaly" Ivanov, Obuljen, Cuesta-Lazaro, Toomey arXiv:2409.10609]
Simulator 1
Simulator 2
Dark Matter
Feedback
i) Contrastive
Baryonic fields
ii) Generative
Generative model
Total matter, gas temperature,
gas metalicity
Encoder
By carol cuesta
