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pySpectrum calculates the power spectrum and bispectrum for galaxies, halos, and dark matter.
Spender establishes a restframe for galaxy spectra that has higher resolution and larger wavelength range than the spectra from which it is trained. The model can be trained from spectra at different redshifts or even from different instruments without the need to standardize the observations. Spender also has an explicit, differentiable redshift dependence, which can be coupled with a redshift estimator for a fully data-driven spectrum analysis pipeline. The code describes the restframe spectrum by an autoencoder and transforms the restframe model to the observed redshift; it also matches the spectral resolution and line spread function of the instrument.
desitarget selects targets for spectroscopic follow-up by Dark Energy Spectroscopic Instrument (DESI). The pipeline uses bitmasks to record that a specific source has been selected by a particular targeting algorithm, setting bit-values in output data files in a number of different columns that indicate whether a particular target meets specific selection criteria. desitarget also outputs a unique TARGETID that allows each target to be tracked throughout the DESI survey. This TARGETID encodes information about each DESI target, such as the catalog the target was selected from, whether a target is a sky location or part of a random catalog, and whether a target is part of a secondary program.
HaloFlow uses a machine learning approach to infer Mh and stellar mass, M∗, using grizy band magnitudes, morphological properties quantifying characteristic size, concentration, and asymmetry, total measured satellite luminosity, and number of satellites.
provabgs infers full posterior distributions of galaxy properties for galaxies in the DESI Bright Galaxy Survey using state-of-the-art Bayesian spectral energy distribution (SED) modeling of DESI spectroscopy and photometry. provabgs includes a state-of-the-art stellar population synthesis (SPS) model based on non-parametric prescription for star formation history, a metallicity history that varies over the age of the galaxy, and a flexible dust prescription. It has a neural network emulator for the SPS model that enables accelerated inference. Full posteriors of the 12 SPS parameters can be derived in ~10 minutes. The emulator is currently designed for galaxies from 0 < z < 0.6. provabgs also includes a Bayesian inference pipeline that is based on zeus (ascl:2008.010).