DESI EDR: Calibrating the Tully-Fisher Relationship with the DESI Peculiar Velocity Survey
Douglass, K., BenZvi, S., Uberoi, N., Cullan Howlett, Christoph Saulder, Said, K., R. Demina, Aguilar, J., Steven P. Ahlen, Greg Aldering, D. Bianchi, Donal Brooks, T. Claybaugh, A. Cuceu, T. M. Davis, Kyle Dawson, Axel de la Macorra, Andreu Font-Ribera, J. E. Forero-Romero, ENRIQUE GAZTAÑAGA · HAL (Le Centre pour la Communication Scientifique Directe) · 2025
The HyperFit Python package is designed to fit N-dimensional data with an N-1 dimensional plane. It can account for uncertainties in both the x and y data, with the ability for these uncertainties to be covariant. The model assumes that the data is Gaussian-distributed about this plane. Based on the R HyperFit package by Aaron Robotham and Danail Obreschkow (https://ui.adsabs.harvard.edu/abs/2015PASA...32...33R/abstract), the Python version available at https://github.com/cullanhowlett/HyperFit was written by Cullan Howlett. Here, we share a modified version of HyperFit, with the addition of the MultiLinFit class. MultiLinFit is limited to only fitting 2-dimensional data with a line, but it has the ability to fit multiple data sets simultaneously and assumes that all data sets share a common slope but have different intercepts and (potentially) different measures of intrinsic Gaussian scatter around their lines. This package is used in the Tully-Fisher Relation analysis of the DESI Peculiar Velocity Survey: Early Data Release: "DESI EDR: Calibrating the Tully-Fisher Relationship with the DESI Peculiar Velocity Survey" by Kelly Douglass et al. Year 1: "The DESI DR1 Peculiar Velocity Survey: The Tully-Fisher Distance Catalog" by Kelly Douglass et al.