The model fits the observed spectrum from X-rays to highest energies.Spectral fitting is a data analysis technique used in astrophysics to interpret the observed electromagnetic spectrum of an astronomical object, such as a black hole, galaxy, or supernova. The observed spectrum — from X-rays to gamma rays or from radio to optical — is compared to theoretical models that predict the emission based on physical processes like thermal radiation, synchrotron emission, or inverse Compton scattering. By adjusting model parameters, researchers can infer properties like temperature, density, magnetic field strength, and the geometry of the emitting region.
Spectral fitting has been a cornerstone of X-ray astronomy since the 1970s, with the development of spectral analysis software like XSPEC, which remains widely used today.
The process involves constructing a model spectrum that includes contributions from various emission mechanisms, each with its own functional form. The model is convolved with the instrument's response function and then compared to the observed data using statistical methods like chi-squared minimization or Bayesian inference. The best-fit parameters are found by exploring the parameter space, often with Markov Chain Monte Carlo methods, to account for uncertainties and degeneracies.
It is used to determine the mass and spin of black holes from X-ray spectra, to measure the temperature and composition of galaxy clusters, and to identify the physical conditions in gamma-ray bursts.
The same spectral fitting techniques are used to analyze data from laboratory plasmas, such as those in fusion experiments, to diagnose temperature and density.



