You can visit my GitHub profile to explore additional projects, as well as materials from tutorials, courses, and workshops.
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.
