# Machine learning improved fits of the sound horizon at the baryon drag epoch

@article{Aizpuru2021MachineLI, title={Machine learning improved fits of the sound horizon at the baryon drag epoch}, author={Andoni Aizpuru and Rub{\'e}n Campos Arjona and Savvas Nesseris}, journal={Physical Review D}, year={2021} }

The baryon acoustic oscillations (BAO) have proven to be an invaluable tool in constraining the expansion history of the Universe at late times and are characterized by the comoving sound horizon at the baryon drag epoch rs(zd). The latter quantity can be calculated either numerically using recombination codes or via fitting functions, such as the one by Eisenstein and Hu (EH), made via grids of parameters of the recombination history. Here we quantify the accuracy of these expressions and show… Expand

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A Living Review of Machine Learning for Particle Physics

- Computer Science, Physics
- ArXiv
- 2021

This living review is a nearly comprehensive list of citations for those developing and applying deep learning approaches to experimental, phenomenological, or theoretical analyses, and will be updated as often as possible to incorporate the latest developments. Expand

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