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The 21cm global signal is an important probe to reveal the properties of thefirst astrophysical objects and the processes of the structure formation fromwhich one can constrain astrophysical and cosmological parameters. To extractthe information of such parameters, one needs to efficiently evaluate the 21cmglobal signal for statistical analysis. First we developed an artificial neuralnetwork-based emulator to predict the 21cm global signal, which works withsignificantly less computational cost and high precision. Then we apply ouremulator to demonstrate the parameter estimation based on the Bayesian analysisby using the publicly available EDGES low-band data. We find that the result issensitive to the foreground model, the assumption of noise, and the frequencyrange used in the analysis. The Bayesian evidence suggests the models withhigher order polynomial function and enhanced noise are preferred. We alsocompare models suggested from the EDGES low-band data and the ones from recentJWST measurements of the galaxy luminosity function at $z=16$. We find that themodel which produces the 21cm absorption line at $z\approx15$ is wellconsistent with the central value of the observed luminosity function at$z=16$.

EDGES and JWST with 21cm global signal emulator