Beyond the Observable Universe
florpi
https://florpi.github.io/
IAIFI Fellow
Carol(ina) Cuesta-Lazaro
["DESI 2024 VI: Cosmological Constraints from the Measurements of Baryon Acoustic Oscillations" arXiv:2404.03002]
What role did Machine Learning play?
Dark Energy is constant over time
DESI's Dark Energy constraints
Carolina Cuesta-Lazaro IAIFI/MIT @ Rising Stars in Physics 2024
1-Dimensional
Machine Learning
Secondary anisotropies
Galaxy formation
Intrinsic alignments
DESI, DESI-II, Spec-S5
Euclid / LSST
Simons Observatory
CMB-S4
Ligo
Einstein
The era of Big Data Cosmology
xAstrophysics
Carolina Cuesta-Lazaro IAIFI/MIT @ Rising Stars in Physics 2024
+ Simulations
[Image Credit: Claire Lamman (CfA/Harvard) / DESI Collaboration]
Carolina Cuesta-Lazaro IAIFI/MIT @ Rising Stars in Physics 2024
Inference a la gradient descent
Carolina Cuesta-Lazaro IAIFI/MIT @ Rising Stars in Physics 2024
Base Distribution
Target Distribution
1-to-Many mapping between distributions
Make the data as likely as possible
Prompt
A person half Yoda, half Gandalf
[arXiv:2311.17141]
Base Distribution
Target Distribution
Generative models for Galaxy Surveys
Carolina Cuesta-Lazaro IAIFI/MIT @ Rising Stars in Physics 2024
Prompt
Cosmological model
TNG-300
True DM
Inferred DM
Size of training simulation
Galaxy Cluster
Void
Carolina Cuesta-Lazaro IAIFI/MIT @ Rising Stars in Physics 2024
1 to Many:
[arXiv:2403.10648]
Unwinding gravitational evolution
Carolina Cuesta-Lazaro IAIFI/MIT @ Rising Stars in Physics 2024
Prompt
~ Gpc
pc
kpc
Mpc
Gpc
Subgrid Model
How well can we simulate the Universe?
[Video credit: Francisco Villaescusa-Navarro]
Gas density
Gas temperature
Carolina Cuesta-Lazaro IAIFI/MIT @ Rising Stars in Physics 2024
Subgrid model 1
Subgrid model 2
Subgrid model 3
Subgrid model 4
1. There is a lot of information in large scale structure surveys that ML methods can access
2. We can tackle high dimensional inference problems so far unatainable
3. Theoretical uncertainties are limiting the amount of information we can robustly extract
Data-driven theory
Hybrid simulators, robustness, representation learning
Mapping dark matter, constrained simulations... Let's get creative!
Field level inference
