NEURAL FILTERS FOR JET ANALYSIS

We study the efficiency of a neural-net filter and deconvolution method for estimating jet energies and spectra in high-background reactions such as nuclear collisions at the relativistic heavy-ion collider and the large hadron collider. The optimal network is shown to be surprisingly close but not identical to a linear high-pass filter. A suitably constrained deconvolution method is shown to uncover accurately the underlying jet distribution in spite of the broad network response. Finally, we show that possible changes of the jet spectrum in nuclear collisions can be analyzed quantitatively, in terms of an effective energy loss, with the proposed method.