Enrique Paillas, Pauline Zarrouk, Yan-Chuan Cai, Will Percival, Sesh Nadathur, Mathilde Pinon, Arnaud de Mattia, Florian Beuler
Carolina Cuesta-Lazaro
IAIFI fellow - MIT/CfA
arXiv:2209.04310
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Initial Conditions
Dynamics
Dark energy
Dark matter
Ordinary matter
Amplitude initial density field
Scale dependence
Neutrino mass first evidence beyond SM physics + LSS most accurate measurement
Primordial Non-gaussianity to probe the physics of inflation (single/multi field, particle content)
Galaxy formation not only a nuisance to margnilize over!
Large scale modifications of gravity using growth to detect the existence of fifth forces
Linear
Early Universe
~linear
Gravity
Late Universe
Non-linear
Credit: S. Codis+16
Non-Guassianity
Second moment not optimal
arxiv:1911.11158
Autocorrelation
Cross-correlation with haloes
Monopole
Quadrupole
Voids
Clusters
arXiv:1909.05273
Covariance
Derivatives
Finite differences
Where does the information come from?
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Density split in a Gaussian Random Field
But, can we estimate densities realistically?
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Real
Redshift
Cross-correlation between quintiles and haloes
Monopole
Quadrupole
Monopole
Quadrupole
Autocorrelation of quintiles
Comparison against other works that have used the same set of tracers
Simulated Data
Data
Prior
Posterior
Inverse problem
Forwards model
+ Galaxy-Halo connection
+ Cut-sky
+ Lightcone
+ Alcock-Paczinsky
+ Fiber collisions
Forward Model
N-body simulations
Observations
Density split in a SDSS BOSS
CMASS 0.46 < z < 0.6
Galaxy-Halo connection
Main Assumptions
Extensions: Get creative!
But these extensions might limit our constraining power
a) Use hydro simulations to limit options
b) Mask the data to optimise robustness?
Velocity bias
Assembly bias
Environment
Concentration
Formation time
...
Credit: https://cs231n.github.io/convolutional-networks/
Neural Network emulator
Implicit likelihood inference with normalising flows
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
Increased interpretability through structured inputs
Modelling cross-correlations
cuestalz@mit.edu