Bridging Baryons and Dark Matter

IAIFI Fellow, MIT / Center for Astrophysics

Carolina Cuesta-Lazaro

A Machine Learning perspective

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

5-Dimensional

w_0, w_a, f\sigma_8, \Omega_m, \sum m_\nu

Carolina Cuesta-Lazaro IAIFI/MIT @ Princeton 2024

smsharma/awesome-neural-sbi

Astrophysics dominates Simulation-based Inference

Carolina Cuesta-Lazaro IAIFI/MIT @ Princeton 2024

 ~ Gpc

pc

kpc

Mpc

Gpc

[Video credit: Francisco Villaescusa-Navarro]

Gas density

Gas temperature

Carolina Cuesta-Lazaro IAIFI/MIT @ Princeton 2024

Carolina Cuesta-Lazaro IAIFI/MIT @ Princeton 2024

1

Effective Field Theories

Probabilistic Debiasing

2

Learning to represent feedback

3

Robust

Conservative assumptions galaxy formation

Large Scales

Robust?

Hydro sims assumptions

All Scales

Robust?

Generalizable

All Scales

Mikhail Ivanov

Robust galaxy bias model: Effective field Theories 

+ Simulation as priors

p(b)
p(\mathrm{Galaxies}|b)

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

Carolina Cuesta-Lazaro IAIFI/MIT @ Princeton 2024

["The Millennium and Astrid galaxies in effective field theory: comparison with galaxy-halo connection models at the field level"

Ivanov, Cuesta-Lazaro et al arXiv:2412.01888]

 

["Full-shape analysis with simulation-based priors: cosmological parameters and the structure growth anomaly" 
Ivanov, Obuljen, Cuesta-Lazaro, Toomey arXiv:2409.10609]

 

p_\phi(\rho_\mathrm{DM}|\rho_\mathrm{Galaxies})

1 to Many:

Galaxies

Dark Matter 

["Debiasing with Diffusion: Probabilistic reconstruction of Dark Matter fields from galaxies" 
Ono et al (including Cuesta-Lazaro) 
NeurIPs 2024 ML for the physical Sciences arXiv:2403.10648]

 

Victoria Ono

Core F. Park

Probabilistic Debiasing

Carolina Cuesta-Lazaro IAIFI/MIT @ Princeton 2024

Truth

Sampled

Observed

Small

Large

Scale (k)

Power Spectrum

Small

Large

Scale (k)

Cross correlation

Carolina Cuesta-Lazaro IAIFI/MIT @ Princeton 2024

Small

Large

\langle\mathrm{True}\,\,\mathrm{Pred}\rangle

In-Distribution

In-Distribution

In-Distribution

Out-of-Distribution

Out-of-Distribution

Out-of-Distribution

Out-of-Distribution

Out-of-Distribution

Out-of-Distribution

Carolina Cuesta-Lazaro IAIFI/MIT @ Princeton 2024

TNG-300

True DM

Sample DM

Size of training simulation

2) Generalising to larger volumes

Model trained on Astrid subgrid model

1) Generalising across subgrid models

Carolina Cuesta-Lazaro IAIFI/MIT @ Princeton 2024

["3D Reconstruction of Dark Matter Fields with Diffusion Models: Towards Application to Galaxy Surveys" 
Park, Mudur, Cuesta-Lazaro et al ICML 2024 AI for Science]

 

Posterior Sample

Posterior Mean

Debiasing Cosmic Flows

Carolina Cuesta-Lazaro IAIFI/MIT @ Princeton 2024

Representation Learning

Informative abstractions of the data

Transfer learning beyond LCDM

Cosmic web Anomaly Detection

Representing baryonic feedback

Carolina Cuesta-Lazaro IAIFI/MIT @ Princeton 2024

Representation Learning a la gradient descent

Contrastive

Generative

inductive biases

from scratch or from partial observations

Students at MIT are

OVER-CAFFEINATED

NERDS

SMART

ATHLETIC

Carolina Cuesta-Lazaro IAIFI/MIT @ Princeton 2024

\Omega_m, \sigma_8

Simulator 1

Simulator 2

z
p(
, z)

Dark Matter

Feedback

\Omega_m, \sigma_8

i) Contrastive

Learning the feedback manifold

Baryonic fields

ii) Generative

Carolina Cuesta-Lazaro IAIFI/MIT @ Princeton 2024

Baryonic fields

Dark Matter

Generative model

Total matter, gas temperature,

gas metalicity

p(
)
, z)
p(
z = f_\theta (
)

Encoder

+

Carolina Cuesta-Lazaro IAIFI/MIT @ Princeton 2024

p(\mathcal{C},z|
)
z

X-Ray

 

 

Gas mass fractions

Gas density profiles

Sunyaev-Zeldovich

Galaxy Properties

Thermal Integrated electron pressure (hot electrons)

Star formation + histories

Stellar mass / halo mass relation

Carolina Cuesta-Lazaro IAIFI/MIT @ Princeton 2024

Multi-wavelength observations

FRBs

Integrated electron density

Kinetic Integrated electron density x peculiar velocity

1. Effective Field Theories of galaxy clustering benefit from simulation-based priors without compromising robustness

2. Probabilistic debiasing can robustly map the dark matter distribution

3. A general representation for baryonic feedback may inform galaxy formation modelling

Conclusions

Carolina Cuesta-Lazaro IAIFI/MIT @ IPMU 2024

Can we use hydro sims directly?

Requires forward modelling survey systematics at the field level

Can we constrain it through multi-wavelength observations?