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We analyzed the X-ray photoemission spectra (XPS) of carbon 1s states ingraphene and oxygen-intercalated graphene grown on SiC(0001) using Bayesianspectroscopy. To realize highly accurate spectral decomposition of the XPSspectra, we proposed a framework for discovering physical constraints from theabsence of prior quantified physical knowledge, in which we designed the priorprobabilities based on the found constraints and the physically requiredconditions. This suppresses the exchange of peak components during replicaexchange Monte Carlo iterations and makes possible to decompose XPS in the casewhere a reliable structure model or a presumable number of components is notknown. As a result, we have successfully decomposed XPS of one monolayer (1ML),two monolayers (2ML), and quasi-freestanding 2ML (qfs-2ML) graphene samplesdeposited on SiC substrates with the meV order precision of the binding energy,in which the posterior probability distributions of the binding energies wereobtained distinguishably between the different components of buffer layer eventhough they are observed as hump and shoulder structures because of theiroverlapping.

Quantifying physical insights cooperatively with exhaustive search for Bayesian spectroscopy of X-ray photoelectron spectra