ProDyGI: Quantifying Galaxy Group Finding Uncertainty and Building Pedagogical Pathways

Abstract

This work presents the Probabilistic and Dynamical Group Identifier, a new algorithm for determining uncertainty in the galaxy group finding process by adding a probabilistic component to the process of grouping galaxies in redshift space. This algorithm adopts an assumed stellar-to-halo mass relation (Moster 2012) with injected scatter to probabilistically assign halo masses to galaxies. The algorithm then invokes a spherical overdensity approach (Lacey 1994) to define a boundary within which to search for potential satellites of a given central halo. Finally, the algorithm carries out satellite classification by incorporating position, velocity, and probability information in the form of a satellite fraction as a function of satellite candidate redshift relative to that of the proposed central galaxy. It is this probability information that allows for variance and therefore the novel approach of quantifying uncertainty from mass errors, redshift errors, and group membership errors via any chosen parameter. Notably, ProDyGI is primarily an uncertainty estimating tool to complement group finders, not explicitly a group finder itself, though it could be used in that capacity. This work demonstrates the capacity of this method to quantify group finding uncertainty by generating 200 realizations on mock data from the Vishnu simulation (Bernyk 2016), successfully calculating confidence intervals on the chosen parameter of group multiplicity. In addition, I present the Interdisciplinary and Socially Conscious Astronomy Pedagogy (ISCAP) framework developed while creating and teaching an astrophysics course for the Vanderbilt Program for Talented Youth. This framework encourages links to fields beyond astronomy and connections to broader social issues to attract a broad audience to the field of astronomy.