https://github.com/jaimerzp/limberjack.jl
Auto-differentiable methods for Cosmology
https://github.com/jaimerzp/limberjack.jl
auto-differentiation cosmology
Last synced: 3 months ago
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Auto-differentiable methods for Cosmology
- Host: GitHub
- URL: https://github.com/jaimerzp/limberjack.jl
- Owner: JaimeRZP
- License: mit
- Created: 2023-09-29T08:38:38.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-26T12:53:12.000Z (5 months ago)
- Last Synced: 2025-02-20T21:23:01.330Z (3 months ago)
- Topics: auto-differentiation, cosmology
- Language: Julia
- Homepage:
- Size: 1.04 MB
- Stars: 8
- Watchers: 2
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: ReadMe.md
- License: LICENSE
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README
# LimberJack.jl
[](https://github.com/JaimeRZP/LimberJack.jl/actions?query=workflow%3ALimberJack-CI+branch%3Amain)
[](https://jaimeruizzapatero.net/LimberJack.jl/dev/)
A differentiable cosmological code in Julia.
## Design Philosophy
+ **Modularity**: each main function within ```LimberJack.jl``` has its own module. New functions can be added by including extra modules. ```LimberJack.jl``` has the following modules:
| Module | function |
| ----------- | :----------- |
| ```boltzmann.jl``` | Performs the computation of primordial power spectrum |
| ```core.jl``` | Defines the structures where the theoretical predictions are stored and computes the background quantities |
| ```data_utils.jl``` | Manages ```sacc``` files for large data vectors |
| ```growth.jl``` | Computes the growth factor |
| ```halofit.jl``` | Computes the non-linear matter power spectrum as given by the Halofit fitting formula |
| ```spectra.jl``` | Computes the power spectra of any two tracers |
| ```theory.jl``` | Computes large data vectors that combine many spectra |
| ```tracers.jl``` | Computes the kernels associated with each type of kernel |
+ **Object-oriented**: ```LimberJack.jl``` mimics ```CCL.py``` class structure by using ```Julia```'s ```structures```.
+ **Transparency**: ```LimberJack.jl``` is fully written in ```Julia``` without needing to inerface to any other programming language (```C```, ```Python```...) to compute thoretical predictions. This allows the user full access to the code from input to output.
## Goals
+ **Gradients**: one order of magnitude faster gradients than finite differences.
+ **Precision**: sub-percentage error with respect to ```CCL```.
+ **Speed**: ```C```-like performance.
## Installation
In order to run ```LimberJack.jl``` you will need ```Julia-1.7.0``` or newer installed in your system.
Older versions of ```Julia``` might be compatible but haven't been tested.
You can find instructions on how to install ```Julia``` here: https://julialang.org/downloads/.
Once you have installed ```Julia``` you can install ```LimberJack.jl``` following these steps:
``` julia
using Pkg
Pkg.add("LimberJack")
```
### Installing Sacc.py in Julia
``` julia
using Pkg
Pkg.add("CondaPkg")
CondaPkg.add("sacc")
```
## Use
``` julia
# Import
using LimberJack
# create LimberJack.jl Cosmology instance
cosmology = Cosmology(Ωm=0.30, Ωb=0.05, h=0.70, ns=0.96, s8=0.81;
tk_mode="EisHu",
Pk_mode="Halofit")
z = Vector(range(0., stop=2., length=256))
nz = @. exp(-0.5*((z-0.5)/0.05)^2)
tracer = NumberCountsTracer(cosmology, zs, nz; b=1.0)
ls = [10.0, 30.0, 100.0, 300.0]
cls = angularCℓs(cosmology, tracer, tracer, ls)
```
## Challenges
1. **Parallelization**: the current threading parallelization of ```LimberJack.jl``` is far away from the optimal one over number of threads scaling. Future works could study alternative parallalization schemes or possible inneficiencies in the code.
2. **GPU's**: ```LimberJack.jl``` currently cannot run on GPU's which are known to significantly speed-up cosmological inference. Future works could study implementing ```Julia``` GPU libraries such as ```CUDA.jl```.
3. **Backwards-AD**: currently ```LimberJack.jl```'s preferred AD mode is forward-AD. However, the key computation of cosmological inference, obtaining the $\chi^2$, is a map from N parameters to a scalar. For a large number of parameters, backwards-AD is in theory the preferred AD mode and should significantly speed up the computation of the gradient. Future works could look into making ```LimberJack.jl``` compatible with the latest ```Julia``` AD libraries such as ```Zygote.jl``` to implement efficient backwards-AD.
## Contributors
| | | | | | | |
|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
| 
| 
| 
|
| 
| 
| 
|
| Jaime Ruiz-Zapatero | Andrina Nicola | Carlos Garcia-garcia| David Alonso | Arrykrishna Mootoovaloo | Jamie Sullivan | Marco Bonici |
| Lead | Halofit | Validation | Tracers | EmuPk | Bolt.jl | Benchmarks |
## Citing LimberJack
```
@ARTICLE{2023arXiv231008306R,
author = {{Ruiz-Zapatero}, J. and {Alonso}, D. and {Garc{\'\i}a-Garc{\'\i}a}, C. and {Nicola}, A. and {Mootoovaloo}, A. and {Sullivan}, J.~M. and {Bonici}, M. and {Ferreira}, P.~G.},
title = "{LimberJack.jl: auto-differentiable methods for angular power spectra analyses}",
journal = {arXiv e-prints},
keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
year = 2023,
month = oct,
eid = {arXiv:2310.08306},
pages = {arXiv:2310.08306},
doi = {10.48550/arXiv.2310.08306},
archivePrefix = {arXiv},
eprint = {2310.08306},
primaryClass = {astro-ph.CO},
adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv231008306R},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
```