Global Hydrogen Cosmology Data Analysis for Rigorously Separating the Signal from Systematics

The first aims at independently validating the recent EDGES results, and the second at precision measurements of the Cosmic Dawn absorption trough plus the first detection of the purely cosmological Dark Ages trough at lower frequencies. For this, I will present how we self-consistently separate the signal from large systematics using a pattern recognition method based on training sets.

We then use the analytically calculated, spectral signal fit to start a Markov Chain Monte Carlo (MCMC) exploration of a nonlinear signal parameter space of choice. Efficiently, at each MCMC step, we marginalize over the weights of all linear beam-weighted foreground modes, allowing the complexity of the foreground model to be greatly increased without requiring additional MCMC parameters. We demonstrate the success of this methodology by recovering the input parameters from multiple randomly simulated signals (10-200 MHz), while rigorously accounting for realistically modeled foregrounds. Time permitting, I will also describe ongoing studies in which we benefit from our pipeline to both model foregrounds and implement two key experimental designs into our analysis, a time-series drift scan and induced polarization.