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
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
Phystat-AnomalyDetection-2025
By carol cuesta
