Generative Solutions for Cosmic Problems
Flatiron Institute
Institute for Advanced Studies
Carol(ina) Cuesta-Lazaro
Astrophysics proliferates in Simulation-based Inference
on Simulations
Carolina Cuesta-Lazaro Flatiron/IAS
Scaling Cosmological Simulations
In Volume
In Parameter Space
Carolina Cuesta-Lazaro Flatiron/IAS
Cheap Gravity Only sims
p(\mathrm{Baryons}|\mathrm{Cheap})
(Test suite)
Astrid
EAGLE
\alpha = 0
\alpha = 0.25
\alpha = 0.5
\alpha = 0.75
\alpha = 1
Disentangling Physics and Instrument via Counterfactuals
Carolina Cuesta-Lazaro Flatiron/IAS
Physics
Systematics
[arXiv:2503.15312]
Object 1
Object 2
Object 1
z_\mathrm{instrument}
Orientation + Scale
Number
p(
z_\mathrm{instrument},
z_\mathrm{object}
)
Instrument 1
Instrument 1
Instrument 2
Instrument Encoder
z_\mathrm{object}
Object Encoder
Instrument Pair
Object Pair
Instrument Pair
Object Pair
Carolina Cuesta-Lazaro Flatiron/IAS
Carolina Cuesta-Lazaro Flatiron/IAS
z_\mathrm{instrument}
Instrument Encoder
p(
z_\mathrm{instrument},
z_\mathrm{object}
)
z_\mathrm{object}
Object Encoder
Orientation + Scale
Number
Instrument Pair(s)
Object Pair
Selected by proximity
Nearby approx similar systematics
Target
x^\mathcal{O}
x^\mathcal{S}
Simulated Data
Observed Data
z^\mathcal{O}_p
z^\mathcal{O}_s
z^\mathcal{S}_s
z^\mathcal{S}_p
Alignment Loss
\mathcal{L} = \sum_{\mathcal{D} \in (\mathcal{S}, \mathcal{O})} p(x^\mathcal{D}|z^\mathcal{D}_s, z^\mathcal{D}_p) + \lambda d(z^\mathcal{O}_s,z^\mathcal{S}_s)
Reconstruction
Statistical Alignment
50\%
(OT / Adversarial)
Encoder
Obs
Encoder
Sims
Private Domain Information
Shared Information
\hat{x}^\mathcal{O}
\hat{x}^\mathcal{S}
Observed Reconstructed
Simulated Reconstructed
Shared Decoder
Shared Decoder
Carolina Cuesta-Lazaro Flatiron/IAS
A Toy Model Example
Idealized Simulations
Observations
+ Scale Dependent Noise
+ Bump
x^\mathcal{O}
x^\mathcal{S}
Carolina Cuesta-Lazaro Flatiron/IAS
Amplitude
Tilt
Tilt
p(\theta|z^\mathcal{O}_s)
p(\theta|z^\mathcal{O}_p)
p(\theta|z^\mathcal{O}_p,z^\mathcal{O}_s)
p(\theta|z^\mathcal{O}_p)
Robust SBI from Shared
p(x^\mathcal{O}|z^\mathcal{O}_p,z^\mathcal{O}_s)
p(x^\mathcal{O}|z^\mathcal{O}_s)
Visualizing Information Split
Carolina Cuesta-Lazaro Flatiron/IAS
Carolina Cuesta-Lazaro Flatiron/IAS
Trained Gaussian - Tested Zero Column
\mathcal{L}(q_\mathrm{obs},q_\mathrm{latent},p_\mathrm{obs},p_\mathrm{latent})
1. Design next Experiment
2. Hypothesize Equation of motion
3. Simulate and Compare
p(\mathrm{World})
p(\mathrm{Prompt}|\mathrm{World})
Carolina Cuesta-Lazaro Flatiron/IAS
Learning to Play Scientist
Pavel Izmailov
["An LLM-driven framework for cosmological
model-building and exploration" Mudur, Cuesta-Lazaro, Toomey
ML4PS NeurIPs Workshop 2025]
Pablo Mercader
Daniel Muthukrishna
Jeroen Audenaert
Legacy Survey
HSC
DESI
SDSS
Same Object / Different Instrument
Different Object / Same Instrument
Carolina Cuesta-Lazaro Flatiron/IAS
Ground Truth
Instrument Pair
Object Pair
Recon
Carolina Cuesta-Lazaro Flatiron/IAS
PolymathicIntro - 2026
By carol cuesta
