Carol Cuesta-Lazaro (IAIFI Fellow)

in collaboration with Siddarth Mishra-Sharma

 Diffusion Models for Cosmology

 

Initial Conditions of the Universe

Laws of gravity

3-D distribution of galaxies

Which are the ICs of OUR Universe?

Primordial non-Gaussianity?

Probe Inflation

Galaxy formation

3-D distribution of dark matter

Is GR modified on large scales?

How do galaxies form?

Neutrino mass hierarchy?

ML for Large Scale Structure:

A wish list

Generative models

Learn p(x)

Evaluate the likelihood of a 3D map, as a function of the parameters of interest

1

Combine different galaxy properties (such as velocities and positions)

2

Sample 3D maps from the posterior distribution 

3

p(
)
|
\mathrm{Cosmology}

Explicit Density

Implicit Density

Tractable Density

Approximate Density

Normalising flows

Variational Autoencoders 

Diffusion models

Generative Adversarial Networks

The zoo of generative models

z_T
z_{0}
z_{1}
z_{2}
p_\theta(z_{t-1}|z_t)
q(z_t|z_{t-1})

Reverse diffusion: Denoise previous step

Forward diffusion: Add Gaussian noise (fixed)

Diffusion models

A person half Yoda half Gandalf

z_T
z_{0}
z_{1}

Diffusion on 3D coordinates

z_{2}
p_\theta(z_{t-1}|z_t)
q(z_t|z_{t-1})

Reverse diffusion: Denoise previous step

Forward diffusion: Add Gaussian noise (fixed)

Cosmology

Setting tight constraints with only 5000 halos

 

h_0
h_1
h_5
h_4
h_2
h_3
h_6
e_{01}
e_{12}

Node features

Edge features

\mathcal{G} = h^{L}_i, e^{L}_{ij} \rightarrow h^{L+1}_i, e^{L+1}_{ij}
e^{L+1}_{ij} = \phi_e(e^L_{ij}, h^L_i, h^L_j)

edge embedding

h^{L+1}_{i} = \phi_h( h^L_i, \mathcal{A}_j e^{L+1}_{ij})

node embedding

Input

noisy halo properties

Output

noise prediction

A variational Lower Bound

p(Z|X) = \frac{p(X|Z)p(Z)}{p(X)}
q(Z) \approx p(Z|X)

Inference is hard....

known PDF with some free parameters

Kullback-Leibler divergence

distance between two distributions

D_\mathrm{KL}(p(x)||q(x)) = \int p(x) \ln \frac{p(x)}{q(x)} dx

Not symmetric!

D_\mathrm{KL}(p(x)||q(x)) \geq 0
D_\mathrm{KL}(p(x)||q(x)) = 0 \Leftrightarrow p = q

A variational Lower Bound

 

\begin{aligned} & KL\left[q(Z) \| p(Z|X)\right] = \\ &= \int_Z q(Z) \log \frac{q(Z)}{p(Z|X)} \\ \end{aligned}
\begin{aligned} & KL\left[q(Z) \| p(Z|X)\right] = \\ &= \int_Z q(Z) \log \frac{q(Z)}{p(Z|X)} \\ &= -\int_Z q(Z) \log \frac{p(Z|X)}{q(Z)} \\ &= - \left(\int_Z q(Z) \log \frac{p(X,Z)}{q(Z)} - \int_Z q(Z) \log p(X)\right) \\ &= -\int_Z q(Z) \log \frac{p(X,Z)}{q(Z)} + \log p(X) \int_Z q(Z) \\ &= -\mathrm{VLB} + \log p(X) \end{aligned}
\begin{aligned} & KL\left[q(Z) \| p(Z|X)\right] = \\ &= \int_Z q(Z) \log \frac{q(Z)}{p(Z|X)} \\ &= -\int_Z q(Z) \log \frac{p(Z|X)}{q(Z)} \\ &= - \left(\int_Z q(Z) \log \frac{p(X,Z)}{q(Z)} - \int_Z q(Z) \log p(X)\right) \\ \end{aligned}
p(Z|X) = \frac{p(X|Z)p(Z)}{p(X)}

Inference = Optimisation

q(Z) \approx p(Z|X)
\mathrm{VLB} = \log p(X) - KL\left[q(Z) \| p(Z|X)\right]
\mathrm{VLB} \leq \log p(X)
\mathrm{VLB} = \log p(X)

only if q perfectly describes p!

\log p(X)
\mathrm{VLB} = \log E_{q(z)} \frac{p(X,Z)}{q(Z)}
KL\left[q(Z) \| p(Z|X)\right]
- \log p(x) \leq -\mathrm{VLB}(x) =
\gray{\mathrm{Prior Loss}} + \blue{\mathrm{Diffusion Loss}}
\gray{\mathrm{Prior Loss} = \mathrm{KL}(q(z_T|x)|p(z_T))}
\blue{\mathrm{Diffusion Loss} = }
\blue{\sum_{t} D_\mathrm{KL} \left[ q(z_{t-1}|z_{t},x) || p_\theta (z_{t-1}|z_{t}) \right]}

Ensure consistency forward/reverse

 

Challenge Learning a well calibrated likelihood from only 2000 N-body simulations

All learnable functions

Equivariant functions

Data constraints

Credit: Adapted from Tess Smidt

Equivariant diffusion

Implications for robustness and interpretability?

+ Galaxy formation

+ Observational systematics (Cut-sky, Fiber collisions)

+ Lightcone, Redshift Space Distortions....

Forward Model

N-body simulations

Observations

 SIMBIG arXiv:2211.00723

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By carol cuesta

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