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
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?
PrincetonCosmology2024
By carol cuesta
