**Cosmological parameter inference using gravitational waves and machine learning.**
COSMOFlow uses **normalising flows** to compute complex probability distributions for cosmological and population parameters inferred from gravitational wave events resulting from compact binary coalescences in the universe.
Cosmological parameter inference using Gravitational waves and machine learning. It harnesses the power of Normalising Flows to compute the complex probability distributions of cosmological and population paramters inferred using gravitational wave evetns from compact binary coalescences in the unvierse. The code right now allwos for the computation of posterior distribution of the rate of expansion of the unvierse (H0) using psoterior samples from gravitational wave events from binary black holes.
### What does COSMOFlow do?
COSMOFlow is designed to compute posterior distributions of the rate of expansion of the universe, specifically the **Hubble constant (H₀)**, using posterior samples from gravitational wave events such as binary black holes. This project leverages Bayesian inference and machine learning to model these distributions efficiently.
## Getting started
### Key Features:
-**Efficient Inference**: Use trained normalising flow models to obtain Bayesian posteriors on cosmological parameters such as H₀ in seconds.
-**Scalability**: Applicable to different compact binary coalescence events (e.g., binary black holes, neutron stars) and cosmological models.
-**Use of Prior Information**: Incorporates prior knowledge from galaxy catalogues to improve parameter estimation.
To make it easy for you to get started with GitLab, here's a list of recommended next steps.
## Paper
For a detailed explanation of the methodology, please refer to the paper associated with this project:
[**Inferring Cosmological Parameters using Normalizing Flows**](https://journals.aps.org/prd/abstract/10.1103/PhysRevD.109.123547), published in Physical Review D.
Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
## Add your files
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-[ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
-[ ] [Set up project integrations](https://git.ligo.org/federico.stachurski/cosmoflow/-/settings/integrations)
## Collaborate with your team
-[ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-[ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-[ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-[ ] [Automatically merge when pipeline succeeds](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
## Test and Deploy
Use the built-in continuous integration in GitLab.
-[ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-[ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-[ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-[ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-[ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
***
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### Abstract:
We present a machine learning approach using normalizing flows for inferring cosmological parameters from gravitational wave events. Our methodology is general to any type of compact binary coalescence event and cosmological model and relies on the generation of training data representing distributions of gravitational wave event parameters. These parameters are conditional on the underlying cosmology and incorporate prior information from galaxy catalogues.
We demonstrate this approach by inferring the **Hubble constant (H₀)** using binary black hole events detected during the O1, O2, and O3 observational runs of the advanced LIGO/VIRGO detectors. The resulting posterior estimate is 𝐻₀ = 74.51+14.80
−13.63 km s⁻¹ Mpc⁻¹. Our trained normalizing flow model can compute results in 𝒪(1) second.
---
## Installation
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To set up and run COSMOFlow, follow these steps:
## Contributing
**Clone the Repository**:
First, you need to download the project to your local machine. Run the following command in your terminal:
State if you are open to contributions and what your requirements are for accepting them.
For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
## Authors and acknowledgment
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## License
For open source projects, say how it is licensed.
I would like to thank Christopher Messenger, Martin Hendry, Jessica Irwin, Michael Williams.
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