Anomaly Detection in Cosmology

Flatiron Institute

Institute for Advanced Studies

Carol(ina) Cuesta-Lazaro

1-Dimensional

Machine Learning

Secondary anisotropies

Galaxy formation

Intrinsic alignments

DESI / SphereX / Hetdex

Euclid / LSST

SO / CMB-S4

Ligo / Einstein

The era of Big Data Cosmology

xAstrophysics

HERA / CHIME

SAGA / MANGA

Galaxy formation

Emitters Census

Reionization

Cosmic Microwave Background

Galaxies / Dwarfs

21 cm

Galaxy Surveys

Gravitational Lensing

Gravitational Waves

AGN Feedback/Supernovae

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

We have increasingly precise observations of the cosmos, but our biggest questions remain about what we can't directly observe

Dark Matter

Gas

Bullet Cluster
\Lambda \mathrm{CDM}
["DESI 2024 VI: Cosmological Constraints from the Measurements of Baryon Acoustic Oscillations" arXiv:2404.03002]

Dark Energy is constant over time

Inflation

x5 times more collisionless matter than we can see

Dark Matter

Exponential expansion in the very early universe

Expansion is accelerating,

dynamical?

Dark Energy

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

Dataset Size = 1 

Can't poke it in the lab 

Simulations

Bayesian statistics

But inference in Cosmology is hard

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

Late Universe

Early Universe

Tension

From Tensions to Discoveries:  Anomalies in Cosmology

Early vs Late

Parametric Extensions

[Image Credit: Prof. Wendy Freedman]

 

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

Looking for what we don't know to look for

The missing pieces: Beyond parametric searches

Axion Dark Matter

Dark Matter - Baryon Interactions

Primordial Non-Gaussianity

Early Dark Energy

Dark Radiation

[Credit: Sandbox Studio]

 

[Credit: Sandbox Studio]

 

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

Observation

Question

Hypothesis

Testable Predictions

Gather data

Alter, Expand, Reject Hypothesis

Develop General Theories

[Figure adapted from ArchonMagnus]

Simulators as theory models

The Scientific Method in 2025

High-dimensional data

Simulations: Testing Ground and Theoretical Models

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

smsharma/awesome-neural-sbi

Astrophysics proliferates Simulation-based Inference

on Simulations

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

[Video credit: Francisco Villaescusa-Navarro]

Gas density

Gas temperature

Subgrid model 1

Subgrid model 2

Subgrid model 3

Subgrid model 4

New physics or pesky baryons?

We need to understand the baryons!

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

Can we learn a general and continuous representation of Baryonic feedback?

 

Gas

Galaxies

p(
p(
, z_\mathrm{baryons})

Dark Matter

Baryonic fields

Marginalize over a broader set of subgrid physics

Interpolate between simulators

Mingshau Liu

(Ming)

Constrain z via multi-wavelength observations

Known Unknowns

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

Trained on:

TNG, SIMBA, Astrid, EAGLE

z = f(x)

Encoder

z_\mathrm{baryons}

1) Encoder

Gas

Galaxies

p(
p(
, z_\mathrm{baryons})

Dark Matter

Baryonic fields

2) Probabilistic Decoder

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

p(
, z_\mathrm{baryons})

Dark Matter

Baryonic fields

\mathcal{O}(10)

(Test suite)

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

Gas Density

Temperature

Astrid

EAGLE

\alpha = 0
\alpha = 0.25
\alpha = 0.5
\alpha = 0.75
\alpha = 1

Interpolating over Simulations

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

Generalizing to unseen simulations: Magneticum

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

Aizhan Akhmetzhanova (Harvard)

["Detecting Model Misspecification in Cosmology with Scale-Dependent Normalizing Flows" Akhmetzhanova, Cuesta-Lazaro, Mishra-Sharma]

Unkown Unknowns

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

["Detecting Model Misspecification in Cosmology with Scale-Dependent Normalizing Flows" Akhmetzhanova, Cuesta-Lazaro, Mishra-Sharma]

Base

OOD Mock 1

OOD Mock 2

Large Scales

Small Scales

Small Scales

OOD Mock 1

OOD Mock 2

Parameter Inference Bias (Supervised)

OOD Metric (Unsupervised)

Large Scales

Small Scales

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

Anomaly Detection in Astrophysics

arXiv:2503.15312

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

Can we separate Systematics from Physics?

Pablo Mercader

Daniel Muthukrishna

Jeroen Audenaert

Legacy Survey

HSC

DESI

SDSS

Same Object / Different Instrument

Different Object / Same Instrument

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

Object 1

Object 2

Object 1

z_\mathrm{instrument}

Back to the Playground!

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 - IAS / Flatiron Institute

Ground Truth

Instrument Pair

Object Pair

Recon

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

Observation

Question

Hypothesis

Testable Predictions

Gather data

Alter, Expand, Reject Hypothesis

Develop General Theories

[Figure adapted from ArchonMagnus]

The Scientific Method in > 2025

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

Simulations in Foundation Models for Science

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)
E^\mathcal{O}
E^\mathcal{S}

Reconstruction

Alignment

50\%

(OT / Adversarial)

\hat{x}^\mathcal{O}
\hat{x}^\mathcal{S}
D

Shared Decoder

D

Observed Reconstructed

Simulated Reconstructed

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

A Toy Model Example

Idealized Simulations

Observations

+ Scale Dependent Noise

+ Bump

x^\mathcal{O}
x^\mathcal{S}

Carolina Cuesta-Lazaro - IAS / Flatiron Institute

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 - IAS / Flatiron Institute