A novel approach for nearly-coincident events rejection

We present a novel technique, called DSVP (Discrimination through Singular Vectors Projections), to discriminate spurious events within dataset. The purpose of this paper is lay down general procedure which can be tailored for broad variety applications. After describing the concept, we apply algorithm problem identifying nearly coincident in low temperature microcalorimeters order push time resolution close its intrinsic limit. In fact, from simulated datasets it was possible achieve an effective even shorter than sampling system considered. obtained results are contextualized framework HOLMES experiment, aims at directly measuring neutrino mass with calorimetric approach, allowing significally improve statistical sensitivity.