neuralike

Deep Learning and Genetic Algorithms for Cosmological Bayesian Inference Speed-up

Code of our paper Deep Learning and genetic algorithms for cosmological Bayesian inference speed-up, accepted in Physical Review D, available at https://arxiv.org/abs/2405.03293.

Repository Structure

• neuralike/
  • NeuraLike.py.- Main class, gathers all other classes.
  • NeuralManager.py.- API class, Manager for neural networks to learn likelihood function over a grid.
  • NeuralNet.py.- Class with neural net architecture in PyTorch.
  • RandomSampling.py.- Creates random samples in the parameter space and evaluates the likelihood in them. This is used to generate the training set for a neural network.
  • pytorchtools.py.- Methods and utilities for PyTorch.

Usage

In the branch neuralike of the repository https://github.com/igomezv/simplemc_tests it is available neuralike integrated within the dynesty library for nested sampling within the SimpleMC cosmological parameter estimation code (https://igomezv.github.io/SimpleMC/).

Acknowledgments

We based our work on the following external codes:

• Philosophy of the method
  • https://arxiv.org/abs/1110.2997
  • https://github.com/DarkMachines/pyBAMBI
• Nested sampling library
  • https://dynesty.readthedocs.io/en/stable
• Cosmological parameter estimation
  • https://igomezv.github.io/SimpleMC
• Genetic algorithms library
  • https://deap.readthedocs.io/en/master
• Deep learning library
  • https://pytorch.org

Citation

If you use this work in your research, please cite:

@article{neuralike,
  title={Deep Learning and genetic algorithms for cosmological Bayesian inference speed-up},
  author={G{\'o}mez-Vargas, Isidro and V{\'a}zquez, J Alberto},
  journal={arXiv preprint arXiv:2405.03293},
  year={2024}
}

If you find useful our nnogada framework for hyperparameter tuning of neural networks with genetic algorithms:

@article{nnogada,
  title={Neural networks optimized by genetic algorithms in cosmology},
  author={Gómez-Vargas, I. and Andrade, J. B. and Vázquez, J. A.},
  journal={Physical Review D},
  volume={107},
  number={4},
  pages={043509},
  year={2023},
  publisher={American Physical Society},
  doi={https://doi.org/10.1103/PhysRevD.107.043509},
  url={https://doi.org/10.48550/arXiv.2209.02685}
}