%0 Generic %1 cheng2022datadriven %A Cheng, Yun-Ting %A Wandelt, Benjamin D. %A Chang, Tzu-Ching %A Dore, Olivier %D 2022 %K tifr %T Data-driven Cosmology from Three-dimensional Light Cones %U http://arxiv.org/abs/2210.10052 %X We present a data-driven technique to analyze multi-frequency images from upcoming cosmological surveys mapping large sky area. Using full information from the data at the two-point level, our method can simultaneously constrain the large-scale structure (LSS), the spectra and redshift distribution of emitting sources, and the noise in observed data without any prior assumptions beyond the homogeneity and isotropy of cosmological perturbations. In particular, the method does not rely on source detection or photometric or spectroscopic redshift estimates. Here we present the formalism and demonstrate our technique with a mock observation from nine optical and near-infrared photometric bands. Our method can recover the input signal and noise without bias, and quantify the uncertainty on the constraints. Our technique provides a flexible framework to analyze the large-scale structure observation traced by different types of sources, which has potential for wide application to the current or future cosmological datasets such as SPHEREx, Rubin Observatory, Euclid, or the Nancy Grace Roman Space Telescope.

@misc{cheng2022datadriven, abstract = {We present a data-driven technique to analyze multi-frequency images from upcoming cosmological surveys mapping large sky area. Using full information from the data at the two-point level, our method can simultaneously constrain the large-scale structure (LSS), the spectra and redshift distribution of emitting sources, and the noise in observed data without any prior assumptions beyond the homogeneity and isotropy of cosmological perturbations. In particular, the method does not rely on source detection or photometric or spectroscopic redshift estimates. Here we present the formalism and demonstrate our technique with a mock observation from nine optical and near-infrared photometric bands. Our method can recover the input signal and noise without bias, and quantify the uncertainty on the constraints. Our technique provides a flexible framework to analyze the large-scale structure observation traced by different types of sources, which has potential for wide application to the current or future cosmological datasets such as SPHEREx, Rubin Observatory, Euclid, or the Nancy Grace Roman Space Telescope.}, added-at = {2022-10-20T08:07:34.000+0200}, author = {Cheng, Yun-Ting and Wandelt, Benjamin D. and Chang, Tzu-Ching and Dore, Olivier}, biburl = {https://www.bibsonomy.org/bibtex/2abb8e38200fb29a6d805c7c74fce431b/citekhatri}, description = {Data-driven Cosmology from Three-dimensional Light Cones}, interhash = {8537eaa12748cc3ca954fc464db802a8}, intrahash = {abb8e38200fb29a6d805c7c74fce431b}, keywords = {tifr}, note = {cite arxiv:2210.10052Comment: 22 pages, 20 figures, submitted to ApJ}, timestamp = {2022-10-20T08:07:34.000+0200}, title = {Data-driven Cosmology from Three-dimensional Light Cones}, url = {http://arxiv.org/abs/2210.10052}, year = 2022 }