SimpleMC
Cosmological parameter estimation code developed by Dr. A. Slosar and Dr. JA Vazquez. From 2019 to 2023, I helped in maintaining the code, and incorporating nested sampling, convergence criteria for Metropolis-Hastings, post-processing options and other new features.
Collaborating with Dr. JA Vazquez.
Links:
- Repository in GitHub.
- Documentation.
- Mini-course
nnogada
Neural Networks Optimized by Genetic Algorithms in Data Analysis (nnogada).
Links:
- Repository in GitHub.
- Documentation.
- Related paper.
Other projects using nnogada
:
- Reconstruncting Rotation Curves with neural networks.
- Dark energy reconstruction with LSST SNIa simulations.
- neuralike.
neuralike
Deep Learning and genetic algorithms for cosmological Bayesian inference speed-up.
Links:
crann
CRANN (Cosmological Reconstructions with Artificial Neural Networks). Python notebooks with model-independent reconstructions for cosmological functions. We will soon clean up the code and shape it into a library for ease of use.
Links:
- Cosmological Reconstructions with Artificial Neural Networks (arXiv).
- Repository in GitHub.