Baryonification
Inflation
Symmetry-preserving ML
Galaxy Formation
Simulation Based Inference
Epidemiological simulations
Medical Imaging
AI4Science
Exoplanets
LSS
Compute
Simulations
Data
ML
Statistics
Physics
What is dark matter made of?
What is driving the accelerated expansion?
How did the Universe begin?
Late Universe
Early Universe
Tension
Early vs Late
Parametric Extensions
[Image Credit: Prof. Wendy Freedman]
Is LCDM broken?
Systematics?
-> Shrink error bars
-> Build methods for attribution
Carolina Cuesta-Lazaro NYU/Flatiron
[Image Credit: Claire Lamman (CfA/Harvard) / DESI Collaboration]
\theta
Forward Model
Observable
x
Predict
Infer
Theory Parameters
Inverse mapping
p(\mathcal{\theta}|x)
+ MCMC hammer
\color{darkgray}{\Omega_m}, \color{darkgray}{w_0, w_a},\color{darkgray}{f_\mathrm{NL}}\, ...
Dark matter
Dark energy
Inflation
Initial conditions
+
\mathcal{O}(10)
\mathcal{O}(10M)
Carol's optimistic forecast
Carolina Cuesta-Lazaro NYU/Flatiron
+
Reconstructing ALL latent variables:
Dark Matter distribution
Entire formation history
Peculiar velocities
Predictive Cross Validation:
Cross-Correlation with other probes without Cosmic Variance
[Image Credit: Yuuki Omori]
Constraining Inflation:
Inferring primordial non-gaussianity
Why field-level inference?
Data-driven Subgrid models / Data-driven Systematics
Carolina Cuesta-Lazaro NYU/Flatiron
1) Likelihood not necessarily Gaussian
2) Forward model no need differentiable
3) Amortized
Marginalizing over ICs
Fixing ICs
HMC: Marginalizing over ICs
True
Reconstructed
\delta_\mathrm{Obs}
\delta_\mathrm{ICs}
p(\delta_\mathrm{ICs}, \theta|\delta_\mathrm{Obs})
Carolina Cuesta-Lazaro NYU/Flatiron
Learned Subgrid Models
Black Hole powered jets regulate star formation
But jets interact with the turbulent interstellar medium!
Carolina Cuesta-Lazaro NYU/Flatiron
A Matryoshka of Scales
Carolina Cuesta-Lazaro NYU/Flatiron
DiscoverPhysics: Benchmarking LLMs for
Out-of-the-Box Scientific Thinking
Hypothesis
Simulate World
Invisible particles
Extra dimensions
Multi Species ...
Simulate World
Invisible particles
Extra dimensions
Multi Species ...
Propose Experiment
Simulate
Text: Conceptual Understanding
Trajectories (.csv)
Science Agent (LLM)
Science Agent (LLM)
Outputs
Python Code: Trajectory MSE
["DiscoverPhysics: Benchmarking LLMs for Out-of-the-Box Scientific Thinking" Wiemann, Smith et al (including CCL)]
World Generator
World Solver
def simulate(
pos1,
pos2,
duration,
**params,
):
"Simulate Universe"
return trajectories
Convergence,
Re-implementation tests....
def discovered_law(pos1, pos2, p1, p2, velocity2, duration, **params):
"""Particle 2 is accelerated toward particle 1 by a radial force
per unit along r^ that combines a static source term G*p1,
a radial-velocity term (analogous to an advective/retardation
coupling), and a centripetal-like tangential kinetic-energy
term, all scaled by 1/r and independent of p2."""
...
return trajectories
def fit_parameters():
return {
"G": {"init": 1.0, "bounds": [0.1, 5.0]},
...
}
Running Experiment...
Reward
Predictiveness
Conceptual Understanding
(MSE)
(Evaluation Score)
"This world consists of ..."
World Definition
Simulation Code
Carolina Cuesta-Lazaro NYU/Flatiron
Easy
Medium
Hard
The challenge: Long roll outs / Attribution
Carolina Cuesta-Lazaro NYU/Flatiron
Brown Bag
By carol cuesta
