![]() This serves to provide the user with a rich interface, rapid analytics and interactive visuals.ĮxoPlanet is designed to have a minimal learning curve to allow researchers to focus more on the applicative aspect of machine learning algorithms rather than their implementation details and supports both methods of learning, providing algorithms for unsupervised and supervised training, which may be done with continuous or discrete labels. With the back-end built using the numpy and scikit-learn libraries, ExoPlanet couples fast and well tested algorithms, a UI designed over the PyQt framework, and graphs rendered using Matplotlib. Effects like redshift space distortions, doppler distortions, magnification biases, evolution and intrinsic aligments can be introduced in the simulations via the input power spectra which must be supplied by the user.ĮxoPlanet provides a graphical interface for the construction, evaluation and application of a machine learning model in predictive analysis. The mutiple fields can be generated tomographically in an arbitrary number of redshift slices and all their statistical properties (including cross-correlations) are determined by the angular power spectra supplied as input and the multivariate lognormal (or Gaussian) distribution assumed for the fields. It is C++ code parallelized with OpenMP FLASK generates fast full-sky simulations of cosmological large-scale structure observables such as multiple matter density tracers (galaxies, quasars, dark matter haloes), CMB temperature anisotropies and weak lensing convergence and shear fields. FLASK (Full-sky Lognormal Astro-fields Simulation Kit) makes tomographic realizations on the sphere of an arbitrary number of correlated lognormal or Gaussian random fields it can create joint simulations of clustering and lensing with sub-per-cent accuracy over relevant angular scales and redshift ranges. ![]()
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