A Bayesian calibration framework for EDGES

• DOI: 10.1093/mnras/stac2600
• Publication Date: 2022-09-15T17:37:12Z
• Authors: Steven G Murray; Judd D Bowman; Peter H Sims; Nivedita Mahesh; Alan E E Rogers; Raul A Monsalve; Titu Samson; Akshatha Konakond...
• Keywords: SIGNAL (programming language)

We develop a Bayesian model that jointly constrains receiver calibration, foregrounds and cosmic 21cm signal for the EDGES global 21\,cm experiment. This simultaneously describes calibration data taken in lab along with sky-data low-band antenna. apply our to same (both sky calibration) used report evidence first star formation 2018. find does not contribute significant uncertainty inferred (<1%), though joint is able more robustly estimate foreground models are otherwise too inflexible describe data. identify presence of systematic data, which largely avoided analysis, but must be examined closely future work. Our likelihood provides foundation analyses other instrumental systematics, such as beam corrections reflection parameters, may added modular manner.