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We present a new supervised deep-learning approach to the problem of theextraction of smeared spectral densities from Euclidean lattice correlators. Adistinctive feature of our method is a model-independent training strategy thatwe implement by parametrizing the training sets over a functional space spannedby Chebyshev polynomials. The other distinctive feature is a reliable estimateof the systematic uncertainties that we achieve by introducing severalensembles of machines, the broad audience of the title. By training an ensembleof machines with the same number of neurons over training sets of fixeddimensions and complexity, we manage to provide a reliable estimate of thesystematic errors by studying numerically the asymptotic limits of infinitelylarge networks and training sets. The method has been validated on a very largeset of random mock data and also in the case of lattice QCD data. We extractedthe strange-strange connected contribution to the smeared $R$-ratio from alattice QCD correlator produced by the ETM Collaboration and compared theresults of the new method with the ones previously obtained with the HLT methodby finding a remarkably good agreement between the two totally unrelatedapproaches.

Teaching to extract spectral densities from lattice correlators to a broad audience of learning-machines