Reconstructing Functions and Estimating Parameters with Artificial Neural Network: a test with Hubble parameter and SNe Ia. (arXiv:1910.03636v1 [astro-ph.CO])

<a href="http://arxiv.org/find/astro-ph/1/au:+Wang_G/0/1/0/all/0/1">Guo-Jian Wang</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ma_X/0/1/0/all/0/1">Xiao-Jiao Ma</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Li_S/0/1/0/all/0/1">Si-Yao Li</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Xia_J/0/1/0/all/0/1">Jun-Qing Xia</a>

In this work, we propose a new non-parametric approach for reconstructing a

function from observational data using Artificial Neural Network (ANN), which

has no assumptions to the data and is a completely data-driven approach. We

test the ANN method by reconstructing functions of the Hubble parameter

measurements $H(z)$ and the distance redshift relation $D_L(z)$ of type Ia

supernova. We find that both $H(z)$ and $D_L(z)$ can be reconstructed with high

accuracy. Furthermore, we estimate cosmological parameters using the

reconstructed functions of $H(z)$ and $D_L(z)$ and find the results are

consistent with those obtained using the observational data directly.

Therefore, we propose that the function reconstructed by ANN can represent the

actual distribution of observational data and can be used for parameter

estimation in further cosmological research. In addition, we present a new

strategy to train and evaluate the neural network, and a code for

reconstructing functions using ANN has been developed and will be available

soon.

In this work, we propose a new non-parametric approach for reconstructing a

function from observational data using Artificial Neural Network (ANN), which

has no assumptions to the data and is a completely data-driven approach. We

test the ANN method by reconstructing functions of the Hubble parameter

measurements $H(z)$ and the distance redshift relation $D_L(z)$ of type Ia

supernova. We find that both $H(z)$ and $D_L(z)$ can be reconstructed with high

accuracy. Furthermore, we estimate cosmological parameters using the

reconstructed functions of $H(z)$ and $D_L(z)$ and find the results are

consistent with those obtained using the observational data directly.

Therefore, we propose that the function reconstructed by ANN can represent the

actual distribution of observational data and can be used for parameter

estimation in further cosmological research. In addition, we present a new

strategy to train and evaluate the neural network, and a code for

reconstructing functions using ANN has been developed and will be available

soon.

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