Redshift surveys in a nutshell
Learning optimal summaries with machine learning
Carolina Cuesta-Lazaro
16th December 2021 - CCA
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Collaborators: Cheng-Zong Ruan, Yosuke Kobayashi, Alexander Eggemeier, Pauline Zarrouk, Sownak Bose, Takahiro Nishimichi, Baojiu Li, Carlton Baugh
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Medical Imaging
Epidemiology: Agent Based simulations
Natural Language Processing
OBSERVED
SIMULATED
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Cosmology
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Two representative MG models f(R) and nDGP:
- The background expansion is the same as LCDM
- One parameter to describe deviations from LCDM
(same large scale real space clustering)
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Cosmology =
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Neural Network Emulator
Galaxy =
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WORK IN PROGRESS
But... We know the late time galaxy field is non-Gaussian. How much information are we throwing away?
Voids
Clusters
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How much information is still missing??
Input
x
Neural network
f
Representation
(Summary statistic)
r = f(x)
Output
o = g(r)
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Invariance to known unknowns
Increased interpretability through structured inputs
Modelling cross-correlations
CCA
By carol cuesta
CCA
- 362