MLizing cosmology
florpi
The What? How? And Why?
And really, Why?
![](https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png)
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https://florpi.github.io/
IAIFI Fellow
Carol Cuesta-Lazaro
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![](https://iaifi.org/images/iaifi-logo-black-noborder-hires.jpg)
The Why: The limits of analytical models
But... Access to tones of data!
simulated data
Complex non-Gaussian distributions
Non-linear forward models
![](https://newscenter.lbl.gov/wp-content/uploads/2022/01/allframe-1000mpc-960x540-1.gif)
(Image Credit: D. Schlegel/Berkeley Lab using data from DESI)
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A 2D animation of a folk music band composed of anthropomorphic autumn leaves, each playing traditional bluegrass instruments, amidst a rustic forest setting dappled with the soft light of a harvest moon
ML has solved high-dimensional inference
#1 Field-level likelihoods for galaxy surveys
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Siddharth Mishra-Sharma
#2 Probabilistic reconstruction of the cosmic web
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![](https://samuel.physics.harvard.edu/sites/scholar.harvard.edu/files/aravisamuel/files/corepark-cropped.jpeg?m=1582231368)
Core Park
![](https://pweb.cfa.harvard.edu/sites/default/files/2022-10/Nayantara-Mudur.jpg)
Nayantara Mudur
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Victoria Ono
Yueying Ni
#3 Faster, invertible and more accurate simulators
![](https://sjeffreson.github.io/images/galaxy-fig-insets-smaller.png)
Sarah Jeffreson
Chirag Modi
Generative Models 101
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Samples
Parametric PDF
Maximize the likelihood of the training samples
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Reverse diffusion: Denoise previous step
Forward diffusion: Add Gaussian noise (fixed)
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A person half Yoda half Gandalf
Diffusion generative models
![](https://blog.kakaocdn.net/dn/cH1S7j/btqYbH2lmp9/UsC9RqbM9PasAj82mAN6kK/img.png)
Score
#1 Modelling galaxy surveys
"A point cloud approach to generative modeling for galaxy surveys at the field level"
arXiv:2311.17141
Carolina Cuesta-Lazaro and Siddharth Mishra-Sharma
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2PCF
Mean pairwise
velocity
kNN
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Emulating cosmic variance
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2PCF
kNN
![](https://miro.medium.com/v2/resize:fit:1400/1*zJ6egQdZt_QSqcGcItn67Q.png)
Diffusion models approximate the likelihood
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arxiv:2107.00630
arxiv:2208.11970
Maximum Likelihood = Denoising
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Tight constraints with only 5000 positions!
"Probabilistic Reconstruction of Dark Matter fields from galaxies using diffusion models"
arXiv:2311.08558
Core Francisco Park, Victoria Ono, Nayantara Mudur, Yueying Ni, Carolina Cuesta-Lazaro
![](https://s3.amazonaws.com/media-p.slid.es/uploads/993552/images/10824461/fiducial_imshows-1.png)
#2 Solving inverse problems:
Probabilistic reconstruction of the cosmic web
1 to Many:
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#3 AI Powered simulators
![](https://sjeffreson.github.io/images/galaxy-fig-insets-smaller.png)
Fast and differentiable N-body sims
Inverting dynamics
More accurate hydro sims: Resolving star formation rates
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![](https://www.universetoday.com/wp-content/uploads/2011/06/molecular_cloud.jpg)
~10-100 pc
Molecular clouds
where stars form
Hydro sims:
A matrioska of scales
![](https://smd-cms.nasa.gov/wp-content/uploads/2023/06/spiral-galaxy-jpg.webp)
~10-50 kpc
Galaxies
where clouds form
![](https://s3.amazonaws.com/media-p.slid.es/uploads/993552/images/11040353/Comparison-of-the-three-TNG-simulations-TNG50-TNG100-and-TNG300-For-each-projected.png)
TNG50 ~50 Mpc
Cosmic web
(where galaxies form)
Adapted from: "Learning the learning the Universe" by Jake Bennet
![](https://wwwmpa.mpa-garching.mpg.de/galform/virgo/millennium/seqB_037a.jpg)
MXXL ~ 4 Gpc
Current subgrid models of the ISM
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High Res Sim
Springel and Hernquist 03
Learning subgrid models from high resolution isolated galaxies
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Gas Surface Density
SFR Surface Density
Hybrid simulators
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Nbody
Slow
Non-differentiable
Particle mesh
Accurate
Fast
Differentiable
Missing small scales
Nbodyify
Fast
Differentiable
Accurate
![](https://i.pinimg.com/474x/32/09/a7/3209a7c6b90087e89e6aaf9cd7ae325c.jpg)
![](https://cdn.memes.com/up/98946491600539583/i/1605563047040.jpg)
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"Nbodyify: adaptive mesh corrections for PM simulations"
Carolina Cuesta-Lazaro and Chirag Modi (in prep)
Gravitational evolution ODE
Particle-mesh
Hybrid Simulator
![](https://s3.amazonaws.com/media-p.slid.es/uploads/993552/images/10812245/densities-1.png)
#2 Probabilistic reconstruction of the cosmic web
#1 Field-level likelihoods for galaxy surveys
#3 Faster, invertible and more accurate simulators
![](https://s3.amazonaws.com/media-p.slid.es/uploads/993552/images/10824461/fiducial_imshows-1.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/993552/images/11040096/diffusion_fig-1.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/993552/images/10824461/fiducial_imshows-1.png)
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cuestalz@mit.edu
San Sebastian - MLizing cosmology
By carol cuesta
San Sebastian - MLizing cosmology
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