Redshift surveys in a nutshell
Learning summary statistics with ML
Carolina Cuesta-Lazaro
Newcastle Astro Journal Club
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Collaborators: Cheng-Zong Ruan, Yosuke Kobayashi, Enrique Paillas, Alexander Eggemeier, Pauline Zarrouk, Sownak Bose, Takahiro Nishimichi, Baojiu Li, Carlton Baugh
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Medical Imaging
Epidemiology: Agent Based simulations
OBSERVED
SIMULATED
Cosmology
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Simulations
HPC
Science question
Statistics ML
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Fifth forces modify structure growth
GROWTH
- GRAVITY
- FIFTH FORCE
+ EXPANSION
Credit: Cartoon depicting Willem de Sitter as Lambda from Algemeen Handelsblad (1930).
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Credit: https://arxiv.org/abs/1912.09383
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Resolving tensions
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Early Universe
~linear
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Gravity
Late Universe
Non-linear
Credit: S. Codis+16
Non-linearity = PT predictions inaccurate
Credit: S. Codis+16
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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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Machine Learning as a solution to
- Non-linearities Produce accurate predictions based on N-body simulations
- Non-Gaussianity Extract cosmological information at the field level
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Cosmology =
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Main Assumptions
- Galaxies don't impact dark matter clustering
- Number of galaxies depends on halo mass only
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- We don't know the Initial Conditions
- Data is very high dimensional
- Large number of parameters to constrain
- N-body sims extremely slow to run! (Sampling parameter space > O(10^6) calls)
Cosmology =
Galaxy =
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?
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Summarise the data
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N-body simulations
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Likelihood evaluations
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Credit: https://cs231n.github.io/convolutional-networks/
What to emulate?
- Flexibility: Vary galaxy tracers, and their cross-correlations. Marginalising over g requires flexible g!
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1% accuracy1-sigma accuracy:- Emulator only as good as data used for training
- Model clustering and mapping between real and redshift space separately
Neural Net
Analytical
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Cosmology =
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Neural Network Emulator
1) Very fast -> MCMC
2) Halo-Galaxy mapping modelled very accurately
3) Allows for flexible implementations of Halo-Galaxy connection
4) Modelling RSD through the Streaming Model simplifies the functions the emulator needs to learn
Galaxy =
Cosmology
Centrals
Satellites
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How much information are we throwing away by summarising in two piont functions?
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How much information are we throwing away by summarising the data?
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Density-dependent clustering
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Clusters
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Voids
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0.08
0.05
0.02
0.7
0.4
PRELIMINARY
0.85
0.80
1.1
1.0
0.9
3.5
0.9
3.0
0.33
0.08
0.28
0.03
0.07
0.4
0.7
0.8
0.86
0.87
1.06
0.87
3.0
3.5
Input
x
Neural network
f
Representation
(Summary statistic)
r = f(x)
Output
o = g(r)
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Increased interpretability through structured inputs
Modelling cross-correlations
ML and cosmology
- ML to accelerate non-linear predictions: allow MCMC sampling of non-linear scales
- Precision of future surveys: what and how we emulate will have an impact on cosmological constraints
- 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?
Copy of deck
By carol cuesta
Copy of deck
- 326