Meson Excitation at Finite Chemical Potential

We consider a probe stable meson in the holographic quark-gluon plasma at zero temperature and chemical potential. Due to the energy injection into the plasma, the temperature and chemical potential are increased to arbitrary finite values and the meson is also excited. Excitation time tex is the time at which the meson falls into the final excited state. We study the effect of various parameters of theory on the excitation time and observe that for larger values of final temperature and chemical potential the excitation time increases. Furthermore, our outcomes show that the more stable mesons are excited sooner.Comment: 10 pages, 9 figures, references added, appendix added, typos correcte

Bayesian inference for inverse problems

Traditionally, the MaxEnt workshops start by a tutorial day. This paper summarizes my talk during 2001'th workshop at John Hopkins University. The main idea in this talk is to show how the Bayesian inference can naturally give us all the necessary tools we need to solve real inverse problems: starting by simple inversion where we assume to know exactly the forward model and all the input model parameters up to more realistic advanced problems of myopic or blind inversion where we may be uncertain about the forward model and we may have noisy data. Starting by an introduction to inverse problems through a few examples and explaining their ill posedness nature, I briefly presented the main classical deterministic methods such as data matching and classical regularization methods to show their limitations. I then presented the main classical probabilistic methods based on likelihood, information theory and maximum entropy and the Bayesian inference framework for such problems. I show that the Bayesian framework, not only generalizes all these methods, but also gives us natural tools, for example, for inferring the uncertainty of the computed solutions, for the estimation of the hyperparameters or for handling myopic or blind inversion problems. Finally, through a deconvolution problem example, I presented a few state of the art methods based on Bayesian inference particularly designed for some of the mass spectrometry data processing problems.Comment: Presented at MaxEnt01. To appear in Bayesian Inference and Maximum Entropy Methods, B. Fry (Ed.), AIP Proceedings. 20pages, 13 Postscript figure