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The Cryogenic Underground Observatory for Rare Events (CUORE) is the first cryogenic experiment searching for$$0\nu \beta \beta $$$0\nu \beta \beta$decay that has been able to reach the one-tonne mass scale. The detector, located at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy, consists of an array of 988$${\mathrm{TeO}}_{2}$$${\mathrm{TeO}}_2$crystals arranged in a compact cylindrical structure of 19 towers. CUORE began its first physics data run in 2017 at a base temperature of about 10 mK and in April 2021 released its$$3{\mathrm{rd}}$$$3\mathrm{rd}$result of the search for$$0\nu \beta \beta $$$0\nu \beta \beta$, corresponding to a tonne-year of$$\mathrm{TeO}_{2}$$${\mathrm{TeO}}_2$exposure. This is the largest amount of data ever acquired with a solid state detector and the most sensitive measurement of$$0\nu \beta \beta $$$0\nu \beta \beta$decay in$${}^{130}\mathrm{Te}$$${}^{130}\mathrm{Te}$ever conducted . We present the current status of CUORE search for$$0\nu \beta \beta $$$0\nu \beta \beta$with the updated statistics of one tonne-yr. We finally give an update of the CUORE background model and the measurement of the$${}^{130}\mathrm{Te}$$${}^{130}\mathrm{Te}$$$2\nu \beta \beta $$$2\nu \beta \beta$decay half-life and decay to excited states of$${}^{130}\mathrm{Xe}$$${}^{130}\mathrm{Xe}$, studies performed using an exposure of 300.7 kg yr.

 

Abstract CUORE Upgrade with Particle IDentification (CUPID) is a foreseen ton-scale array of Li 2 MoO 4 (LMO) cryogenic calorimeters with double readout of heat and light signals. Its scientific goal is to fully explore the inverted hierarchy of neutrino masses in the search for neutrinoless double beta decay of 100 Mo. Pile-up of standard double beta decay of the candidate isotope is a relevant background. We generate pile-up heat events via injection of Joule heater pulses with a programmable waveform generator in a small array of LMO crystals operated underground in the Laboratori Nazionali del Gran Sasso, Italy. This allows to label pile-up pulses and control both time difference and underlying amplitudes of individual heat pulses in the data. We present the performance of supervised learning classifiers on data and the attained pile-up rejection efficiency.