RESUMO
Core-collapse Supernovae (SNe) are one of the most energetic events in the Universe, during which almost all the star's binding energy is released in the form of neutrinos. These particles are direct probes of the processes occurring in the stellar core and provide unique insights into the gravitational collapse. RES-NOVA will revolutionize how we detect neutrinos from astrophysical sources, by deploying the first ton-scale array of cryogenic detectors made from archaeological lead. Pb offers the highest neutrino interaction cross-section via coherent elastic neutrino-nucleus scattering (CEνNS). Such process will enable RES-NOVA to be equally sensitive to all neutrino flavours. For the first time, we propose the use archaeological Pb as sensitive target material in order to achieve an ultra-low background level in the region of interest (O(1 keV)). All these features make possible the deployment of the first cm-scale neutrino telescope for the investigation of astrophysical sources. In this contribution, we will characterize the radiopurity level and the performance of a small-scale proof-of-principle detector of RES-NOVA, consisting in a PbWO4 crystal made from archaeological-Pb operated as cryogenic detector.
RESUMO
The CRESST experiment employs cryogenic calorimeters for the sensitive measurement of nuclear recoils induced by dark matter particles. The recorded signals need to undergo a careful cleaning process to avoid wrongly reconstructed recoil energies caused by pile-up and read-out artefacts. We frame this process as a time series classification task and propose to automate it with neural networks. With a data set of over one million labeled records from 68 detectors, recorded between 2013 and 2019 by CRESST, we test the capability of four commonly used neural network architectures to learn the data cleaning task. Our best performing model achieves a balanced accuracy of 0.932 on our test set. We show on an exemplary detector that about half of the wrongly predicted events are in fact wrongly labeled events, and a large share of the remaining ones have a context-dependent ground truth. We furthermore evaluate the recall and selectivity of our classifiers with simulated data. The results confirm that the trained classifiers are well suited for the data cleaning task.
RESUMO
Nuclear explosions expose ubiquitous materials to large numbers of neutrons, producing a variety of radioactive isotopes. To simulate such phenomena from both fission and thermonuclear explosions, we irradiated 29 different targets with approximately 3 and 14 MeV neutrons and measured the beta-delayed gamma rays using germanium detectors. For each neutron energy, the expected radioisotopes, half-lives, and gamma ray energies were deduced. From measurements of the ratios of activities of the radionuclides produced by neutron irradiations, we were able to identify several materials that are particularly sensitive to the neutron energy spectra.
