MaxEnt power spectrum estimation using the Fourier transform for irregularly sampled data applied to a record of stellar luminosity

The principle of maximum entropy is applied to the spectral analysis of a data signal with general variance matrix and containing gaps in the record. The role of the entropic regularizer is to prevent one from overestimating structure in the spectrum when faced with imperfect data. Several arguments are presented suggesting that the arbitrary prefactor should not be introduced to the entropy term. The introduction of that factor is not required when a continuous Poisson distribution is used for the amplitude coefficients. We compare the formalism for when the variance of the data is known explicitly to that for when the variance is known only to lie in some finite range. The result of including the entropic measure factor is to suggest a spectrum consistent with the variance of the data which has less structure than that given by the forward transform. An application of the methodology to example data is demonstrated.Comment: 15 pages, 13 figures, 1 table, major revision, final version, Accepted for publication in Astrophysics & Space Scienc

Reconstruction of motional states of neutral atoms via MaxEnt principle

We present a scheme for a reconstruction of states of quantum systems from incomplete tomographic-like data. The proposed scheme is based on the Jaynes principle of Maximum Entropy. We apply our algorithm for a reconstruction of motional quantum states of neutral atoms. As an example we analyze the experimental data obtained by the group of C. Salomon at the ENS in Paris and we reconstruct Wigner functions of motional quantum states of Cs atoms trapped in an optical lattice