Carolina Cuesta-Lazaro
19th January 2022 - Waterloo Astronomy Seminar
Collaborators: Cheng-Zong Ruan, Yosuke Kobayashi, Alexander Eggemeier, Pauline Zarrouk, Sownak Bose, Takahiro Nishimichi, Baojiu Li, Carlton Baugh
Initial Conditions
Dynamics
Dark energy
Dark matter
Ordinary matter
Amplitude initial density field
Scale dependence
Linear
Credit: NASA / WMAP SCIENCE TEAM
GALAXY CLUSTERING
GRAVITATIONAL WAVES
GRAVITATIONAL LENSING
Early Universe
~linear
Gravity
Late Universe
Non-linear
Credit: S. Codis+16
Non-linearity = PT predictions inaccurate
Credit: S. Codis+16
Early Universe
~linear
Gravity
Late Universe
Non-linear
Credit: S. Codis+16
Non-Guassianity
Second moment not optimal
Space-time
geometetry
Energy content
Adding new degrees of freedom
GROWTH
- GRAVITY
- FIFTH FORCE
+ EXPANSION
Credit: Cartoon depicting Willem de Sitter as Lambda from Algemeen Handelsblad (1930).
Cosmology =
Main Assumptions
Cosmology =
Galaxy =
?
N-body simulations
Credit: James Hensman
Credit: James Hensman
Optimize the marginal likelihood: Analytical solution!
Pros
Cons
Credit: https://cs231n.github.io/convolutional-networks/
Loss Value
Weights
Weights
+
+
+
+
Network A
Network B
Pros
Cons
Neural Net
Analytical
PAIRWISE VELOCITY
DISTRIBUTION
Probability of finding a pair of galaxies at distance r
On large scales,
slowly varying function of
n = 4 reproduces clustering down to small scales
INFALL
OUTFLOW
Two representative extensions to General Relativity:
- The background expansion is the same as LCDM
- One parameter to describe deviations from LCDM
How do these vary with cosmological parameters on small scales?
Described by four parameters
Code available on github soon!
Likelihood evaluations
Credit: ChangHoon Hahn et al https://arxiv.org/abs/2012.02200
P
B
r
r1
r2
r3
Credit: Sihao Cheng et al https://arxiv.org/pdf/2006.08561.pdf
Input
x
Neural network
f
Representation
(Summary statistic)
r = f(x)
Output
o = g(r)
Increased interpretability through structured inputs
Modelling cross-correlations