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