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We present new, original and alternative method for searching signals coded in noisy data. The method is based on the properties of random matrix eigenvalue density. First, we describe general ideas and support them with results of numerical simulations for basic periodic signals hidden in artificial stochastic noise. Then, the main effort is put to examine the strength of a new method in investigation of data content taken from the real astrophysical NAUTILUS detector designed to search for gravitational waves. Our method discovers some previously unknown problems with data aggregation in this experiment. We provide also the results of new method applied to the entire respond signal from ground-based bar detectors in future experimental activities with reduced background noise level. A good performance of our method is indicated what makes it a positive predictor for further applications in many areas. [ABSTRACT FROM AUTHOR]

We perform a thorough analysis of the spectral statistics of experimental molecular resonances, of bosonic erbium 166Er and 168Er isotopes, produced as a function of magnetic field(B) by Frisch et al. (Nature, 507 (2014) 475), utilizing some recently derived surmises which interpolate between Poisson and GOE and without unfolding. Supplementing this with an analysis using unfolded spectrum, it is shown that the resonances are close to semi-Poisson distribution. There is an earlier claim of missing resonances by Molina et al. (Phys. Rev. E, 92 (2015) 042906). These two interpretations can be tested by more precise measurements in future experiments. [ABSTRACT FROM AUTHOR]