%0 Generic %1 han2021millimeterdl %A Han, Dongwon %A Sehgal, Neelima %A Villaescusa-Navarro, Francisco %D 2021 %K tifr %T MillimeterDL: Deep Learning Simulations of the Microwave Sky %U http://arxiv.org/abs/2105.11444 %X We present 500 high-resolution, full-sky millimeter-wave Deep Learning (DL) simulations that include lensed CMB maps and correlated foreground components. We find that these MillimeterDL simulations can reproduce a wide range of non-Gaussian summary statistics matching the input training simulations, while only being optimized to match the power spectra. The procedure we develop in this work enables the capability to mass produce independent full-sky realizations from a single expensive full-sky simulation, when ordinarily the latter would not provide enough training data. We also circumvent a common limitation of high-resolution DL simulations that they be confined to small sky areas, often due to memory or GPU issues; we do this by developing a "stitching" procedure that can faithfully recover the high-order statistics of a full-sky map without discontinuities or repeated features. In addition, since our network takes as input a full-sky lensing convergence map, it can in principle take a full-sky lensing convergence map from any large-scale structure (LSS) simulation and generate the corresponding lensed CMB and correlated foreground components at millimeter wavelengths; this is especially useful in the current era of combining results from both CMB and LSS surveys, which require a common set of simulations.

@misc{han2021millimeterdl, abstract = {We present 500 high-resolution, full-sky millimeter-wave Deep Learning (DL) simulations that include lensed CMB maps and correlated foreground components. We find that these MillimeterDL simulations can reproduce a wide range of non-Gaussian summary statistics matching the input training simulations, while only being optimized to match the power spectra. The procedure we develop in this work enables the capability to mass produce independent full-sky realizations from a single expensive full-sky simulation, when ordinarily the latter would not provide enough training data. We also circumvent a common limitation of high-resolution DL simulations that they be confined to small sky areas, often due to memory or GPU issues; we do this by developing a "stitching" procedure that can faithfully recover the high-order statistics of a full-sky map without discontinuities or repeated features. In addition, since our network takes as input a full-sky lensing convergence map, it can in principle take a full-sky lensing convergence map from any large-scale structure (LSS) simulation and generate the corresponding lensed CMB and correlated foreground components at millimeter wavelengths; this is especially useful in the current era of combining results from both CMB and LSS surveys, which require a common set of simulations.}, added-at = {2021-05-25T07:36:42.000+0200}, author = {Han, Dongwon and Sehgal, Neelima and Villaescusa-Navarro, Francisco}, biburl = {https://www.bibsonomy.org/bibtex/29db314de1958739a670b1d3121466651/citekhatri}, description = {MillimeterDL: Deep Learning Simulations of the Microwave Sky}, interhash = {c4e682f176cf71b3bc269f07354ec9b8}, intrahash = {9db314de1958739a670b1d3121466651}, keywords = {tifr}, note = {cite arxiv:2105.11444Comment: Sims are now public on NERSC at /global/cfs/cdirs/cmb/data/generic/mmDL; see also https://lambda.gsfc.nasa.gov/simulation/tb_sim_ov.cfm on LAMBDA}, timestamp = {2021-05-25T07:36:42.000+0200}, title = {MillimeterDL: Deep Learning Simulations of the Microwave Sky}, url = {http://arxiv.org/abs/2105.11444}, year = 2021 }