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Recently, a new wave of full modeling analyses have emerged within theLarge-Scale Structure community, leading mostly to tighter constraints on theestimation of cosmological parameters, when compared with standard approachesused over the last decade by collaboration analyses of stage III experiments.However, the majority of these full-shape analyses have primarily beenconducted in Fourier space, with limited emphasis on exploring theconfiguration space. Investigating n-point correlations in configuration spacedemands a higher computational cost compared to Fourier space because ittypically requires an additional integration step. This can pose a limitationwhen using these approaches, especially when considering higher-orderstatistics. One avenue to mitigate the high computation time is to takeadvantage of neural network acceleration techniques. In this work, we present afull shape analysis of Sloan Digital Sky Survey III/BOSS in configuration spaceusing a neural network accelerator. We show that the efficacy of the pipelineis enhanced by a time factor $10^{3}$ without sacrificing precision, making itpossible to reduce the error associated with the surrogate modeling to below$10^{-2}$ percent which is compatible with the precision required for currentstage IV experiments such as DESI. We find $\Omega_m=0.286\pm 0.009$,$H_0=68.8\pm 1.2$ $\mathrm{km} \mathrm{s^{-1}}\mathrm{Mpc^{-1}}$ and $A_s\times 10^9 =2.09 ^{+0.25}_{-0.29}$. Our results on public BOSS data are ingood agreement with BOSS official results and compatible with other independentfull modeling analyses. We explore relaxing the prior on $\omega_b$ and varying$n_s$, without significant changes in the mean values of the cosmologicalparameters posterior distributions, but enlarging their widths. Finally, weexplore the information content of the multipoles when constrainingcosmological parameters.

Full Shape Cosmology Analysis from BOSS in configuration space using Neural Network Acceleration