Enrique Paillas, Pauline Zarrouk, Yan-Chuan Cai, Will Percival, Sesh Nadathur, Mathilde Pinon, Arnaud de Mattia, Florian Beuler
Constraining vΛCDM:
beyond two-point functions
Carolina Cuesta-Lazaro
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IAIFI fellow
arXiv:2209.04310
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?
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Early Universe
~linear
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Gravity
Late Universe
Non-linear
Credit: S. Codis+16
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Non-Guassianity
Second moment not optimal
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Autocorrelation
Cross-correlation with haloes
Monopole
Quadrupole
Voids
Clusters
Monopole
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Where does the information come from?
?
Show comparison Quijote and GRF
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# TODO: ADD GREEN AFTERWARDS
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But, can we estimate densities realistically?
?
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Autocorrelation of quintiles
Cross-correlation between quintiles and haloes
Monopole
Monopole
Quadrupole
Quadrupole
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Simulated Data
Data
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Prior
Posterior
Inverse problem
Forwards model
Density Estimation with normalising flows
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No assumptions on the likelihood (likelihoods rarely Gaussian!)
No expensive MCMC chains needed to estimate posterior
Input
x
Neural network
f
Representation
(Summary statistic)
r = f(x)
Output
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Increased interpretability through structured inputs
Modelling cross-correlations
What ML can do for cosmology
- ML to accelerate non-linear predictions and density estimation
- Can ML extract **all** the information that there is at the field-level in the non-linear regime?
- Compare data and simulations, point us to the missing pieces?
cuestalz@mit.edu
Contact me for anything related to Astro and ML!
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DensitySplit
By carol cuesta
DensitySplit
- 271