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https://github.com/WDoyle123/GravAD
Gravitational Wave Analysis using Auto Differentiation with JAX
https://github.com/WDoyle123/GravAD
astrophysics cosmology gravitational-waves jax python
Last synced: 3 months ago
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Gravitational Wave Analysis using Auto Differentiation with JAX
- Host: GitHub
- URL: https://github.com/WDoyle123/GravAD
- Owner: WDoyle123
- License: gpl-3.0
- Created: 2023-03-21T14:55:27.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-08T15:29:01.000Z (about 1 year ago)
- Last Synced: 2023-12-08T16:33:42.056Z (about 1 year ago)
- Topics: astrophysics, cosmology, gravitational-waves, jax, python
- Language: Python
- Homepage:
- Size: 668 MB
- Stars: 8
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
GravAD
Gravitational Wave Analysis using Auto Differentiation with JAX
[](https://forthebadge.com)
---
## Table of Contents
- [Info](#info)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Screenshots](#screenshots)
- [Authors](#authors)
- [Citing](#citing)
- [Acknowledgements](#acknowledgements)
- [License](#license)
---
## Info
GravAD offers a cutting-edge approach to gravitational wave detection, leveraging Automatic Differentiation (AD), [JAX](https://github.com/google/jax), and [Ripple](https://github.com/tedwards2412/ripple) for template generation via IMRPhenomD. GravAD quickly pinpoints potential gravitational wave events in detector data by optimising the matched filtering process. Despite using fewer templates and significantly reducing computational time, it achieves nearly equivalent results to the established LIGO pipelines. This efficiency positions GravAD as an ideal preliminary tool in gravitational wave research, quickly sifting through data to highlight promising candidates for detailed analysis. GravAD also serves as an additional validator to existing search pipelines, fortifying their credibility.
## Features
- Dynamic Template Generation
- Optimised for GPUs
- Graphs and Results
- Works using real LIGO data
- Works with simulated signals
## Installation
### Prerequisites
This software requires Python 3.11.3 or newer, and has dependencies that can be installed via pip. You can view the full list of dependencies in the `requirements.txt` file. Note, this requirements.txt is for the CPU version of JAX. For GPU version of JAX please install separately following their guides.
### Installation via GitHub
1. Clone the GravAD repository to your local machine:
```
git clone https://github.com/WDoyle123/GravAD.git
```
2. Navigate to the cloned repository:
```
cd GravAD
```
3, Install the required packages:
```
pip install -r requirements.txt
```
## Usage
Once you have installed all the prerequisites and set up GravAD, you can start using it for gravitational wave analysis.
Simply run ```python main.py```.
Here is a simple example of how you might use GravAD:
### Changing the constants
Navigate to the ```constants.py``` file.
You can alter the detection frequencies, number of iteration GravAD runs for (how many templates), aswell as adjusting the simulated annealing optimisation.
```
# Constants
SAMPLING_RATE = 2048
LOW_FREQ_CUTOFF = 20.
HIGH_FREQ_CUTOFF = 1000.
MAX_ITERS = 100
TEMPERATURE = 1 # Initial Temperature
ADV_TEMPERATURE = 2 # Advanced Temperature
ANNEALING_RATE = 0.99
LRU = 1.5 # Learning Rate Upper
LRL = 5.5 # Learning Rate Lower
SEED = 1
```
You can alter which signals you want to detect and from which detector.
```
STRAINS = ['H1', 'L1']
EVENTS = ["GW150914", "GW151012", "GW151226", "GW170104", "GW170608", "GW170729", "GW170809",
"GW170814", "GW170817", "GW170818", "GW170823"]
```
### Adjusting the Optimisers
in ```GravAD.py```, navigate to:
```
def get_optimal_mass(init_mass1, init_mass2, freqs, params, data, psd, delta_f, signal_type, optimiser='sgd_adam'):
```
From here you can select the optimiser from.
Currently available optimisers:
sgd - Stochastic Gradient Descent
sgd_sa - Stochastic Gradient Descent + Simulated Annealing
sgd_p - Stochastic Gradient Descent + Momentum
sgd_sa_p - Stochastic Gradient Descent + Simulated Annealing + Momentum
sgd_sa_adam - Stochastic Gradient Descent + Simulated Annealing + Adam
sgd_adam - Stochastic Gradient Descent + Adam
### Real and Simulated Signals
In the ```main``` function in ```main.py``` you will find:
```
# Analyse real signals
if True:
real_signals()
plotter(pattern_real, folder_results_real, folder_max_snr_real)
# Analyse simulated signals (WIP)
if False:
simulated_signals()
plotter(pattern_simu, folder_results_sim, folder_max_snr_sim)
```
Simply change the ```True``` or ```False``` to enable real or simulated signals.
## Screenshots
Check out [graphs](./figures) for more!
## Authors
- [@WDoyle123](https://github.com/WDoyle123)
- Supervisor [@andrew-lundgren](https://github.com/andrew-lundgren)
## Citing
If you use `GravAD` in your work, please cite [our paper](https://doi.org/10.59973/emjsr.10):
```
@article{Doyle_2023,
author={Doyle, William},
title={Searching For Black Holes Using Auto Differentiation},
url={https://ipipublishing.org/index.php/emjsr/article/view/10},
DOI={10.59973/emjsr.10},
journal={Emerging Minds Journal for Student Research},
volume={1},
year={2023},
month={Jul.},
pages={17–38}
}
```
## Acknowledgements
We would like to acknowledge the use of [ripple](https://github.com/tedwards2412/ripple) for template generation in this project.