References

Cite SimpleMC

  • Aubourg, É., et al (2015). Cosmological implications of baryon acoustic oscillation measurements. Physical Review D, 92(12), 123516.

  • Vazquez, JA., Gómez-Vargas, I., & Slosar, A. (2021). SimpleMC: A package for cosmological parameter estimation and model comparison. [arXiv.xxx.xxx]. In process (soon available).

Cite external codes

We have gathered great codes made by others and put them to work together in the cosmological context. In particular, we use the following, if you consider it pertinent, please also cite them.

  • Nested samplers (dynesty):

    • Speagle, J. S. (2020). dynesty: a dynamic nested sampling package for estimating Bayesian posteriors and evidences. Monthly Notices of the Royal Astronomical Society, 493(3), 3132-3158.

  • Single ellipsoidal nested sampling:

    • Mukherjee, P., Parkinson, D., & Liddle, A. R. (2006). A nested sampling algorithm for cosmological model selection. The Astrophysical Journal Letters, 638(2), L51.

  • Multi ellipsoidal nested sampling:

    • Feroz, F., Hobson, M. P., & Bridges, M. (2009). MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics. Monthly Notices of the Royal Astronomical Society, 398(4), 1601-1614.

  • Cube and ball nested sampling:

    • Buchner, J. (2016). A statistical test for nested sampling algorithms. Statistics and Computing, 26(1-2), 383-392.

  • Random walk nested sampling:

    • Skilling, J. (2006). Nested sampling for general Bayesian computation. Bayesian analysis, 1(4), 833-859.

  • Slice nested sampling:

    • Handley, W. J., Hobson, M. P., & Lasenby, A. N. (2015). POLYCHORD: next-generation nested sampling. Monthly Notices of the Royal Astronomical Society, 453(4), 4384-4398.

  • Dynamic nested sampling:

    • Handley, W. J., Hobson, M. P., & Lasenby, A. N. (2015). POLYCHORD: next-generation nested sampling. Monthly Notices of the Royal Astronomical Society, 453(4), 4384-4398.

  • Artificial Neural Networks in nested sampling:

    • Graff, P., Feroz, F., Hobson, M. P., & Lasenby, A. (2012). BAMBI: blind accelerated multimodal Bayesian inference. Monthly Notices of the Royal Astronomical Society, 421(1), 169-180.

    • Will Handley, & mjw63. (2018, December 22). DarkMachines/pyBAMBI: Continuous integration set up (Version 0.1.1). Zenodo. http://doi.org/10.5281/zenodo.2500878.

  • EMCEE:

    • Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. (2013). emcee: the MCMC hammer. Publications of the Astronomical Society of the Pacific, 125(925), 306.

  • MCEvidence:

    • Heavens, A., Fantaye, Y., Mootoovaloo, A., Eggers, H., Hosenie, Z., Kroon, S., & Sellentin, E. (2017). Marginal Likelihoods from Monte Carlo Markov Chains. arXiv preprint arXiv:1704.03472.

  • Corner plots:

    • Foreman-Mackey, D. (2016). corner: Scatterplot matrices in Python. The Journal of Open Source Software, 1.

  • Getdist plots:

    • Lewis, A. (2019). GetDist: a Python package for analysing Monte Carlo samples. arXiv preprint arXiv:1910.13970.

  • Genetic algoritms from DEAP (ga_deap):

    • Fortin, F. A., De Rainville, F. M., Gardner, M. A. G., Parizeau, M., & Gagné, C. (2012). DEAP: Evolutionary algorithms made easy. The Journal of Machine Learning Research, 13(1), 2171-2175.

  • Neural nets (tensorflow):

    • Abadi, M., et al (2016). Tensorflow: A system for large-scale machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16) (pp. 265-283).

Some research using SimpleMC

  • Padilla, L. E., Tellez, L. O., Escamilla, L. A., & Vazquez, J. A. (2021). Cosmological parameter inference with Bayesian statistics. Universe, 7(7), 213.

  • Akarsu, Ö., Barrow, J. D., Escamilla, L. A., & Vazquez, J. A. (2020). Graduated dark energy: Observational hints of a spontaneous sign switch in the cosmological constant. Physical Review D, 101(6), 063528.

  • Gómez-Vargas, I., Vázquez, J. A., Esquivel, R. M., & García-Salcedo, R. (2021). Cosmological Reconstructions with Artificial Neural Networks. arXiv preprint arXiv:2104.00595.

  • Vázquez, J. A., Tamayo, D., Sen, A. A., & Quiros, I. (2021). Bayesian model selection on scalar ε-field dark energy. Physical Review D, 103(4), 043506.

  • Tamayo, D., & Vazquez, J. A. (2019). Fourier-series expansion of the dark-energy equation of state. Monthly Notices of the Royal Astronomical Society, 487(1), 729-736.s