Bayesian Modeling of NICER Cometary X-ray Spectra: A Legacy Survey of Solar-Wind Charge Exchange

TL;DR

This study applies Bayesian analysis to NICER cometary X-ray spectra, identifying charge-exchange components, measuring ion fluxes, and inferring solar-wind temperatures, revealing spectral families and variability driven by solar-wind fluctuations.

Contribution

It introduces a Bayesian modeling approach for analyzing cometary X-ray spectra, improving diagnostic accuracy and understanding of solar-wind interactions.

Findings

Spectral morphologies fall into distinct families: carbon-, nitrogen-, and oxygen-dominated.

Freeze-in temperatures cluster around 1.4-1.7 MK for carbon and 2.0-2.3 MK for nitrogen/oxygen.

Short-term variability indicates solar-wind charge-state fluctuations dominate spectral differences.

Abstract

We present a uniform, epoch-resolved analysis of soft X-ray observations of eight comets obtained with NICER, using Bayesian statistics to identify charge-exchange line components, measure relative ion fluxes, and infer nominal solar-wind freeze-in temperatures. The sample exhibits recurring spectral morphologies that fall into distinct families: carbon-dominated, intermediate, and nitrogen-/oxygen-dominated. Epoch-resolved flux ratios yield a robust separation between diagnostics: carbon-derived freeze-in temperatures cluster near T_freeze(C) about 1.4-1.7 MK, while nitrogen- and oxygen-derived diagnostics are systematically higher, typically T_freeze(N,O) about 2.0-2.3 MK. Short-timescale variability in inferred freeze-in conditions is common, indicating that instantaneous solar-wind charge-state fluctuations, rather than large changes in coma composition, dominate the spectral differences. We discuss instrumental and modeling limitations, demonstrate how our Bayesian fitting method mitigates degeneracies via physically motivated priors and Bayesian model selection, and recommend laboratory measurements and coordinated high-resolution X-ray observations to refine charge-exchange diagnostics and validate low-resolution inferences.