Dark matter, supernova neutrinos and other backgrounds in direct dark matter searches. The ANDES laboratory prospects

Dark Matter particles can be detected directly via their elastic scattering with nuclei. Next generation experiments can eventually find physical evidences about dark matter candidates. With this motivation in mind, we have calculated the expected signals of dark matter particles in xenon detectors. The calculations were performed by considering different masses and parameters within the minimal supersymmetric standard model. Since the detectors can also detect neutrinos, we have analyzed the supernova neutrino signal including a sterile neutrino in the formalism. Using this [Formula: see text] scheme, we make predictions for both the normal and inverse mass hierarchy. In order to perform a study of the response of planned direct-detection experiments, to be located in ANDES (Agua Negra Deep Experimental Site), we have calculated the neutrino contributions to the background by taken into account reactor’s neutrinos and geoneutrinos at the site of the lab. As a test detector, we take a Xenon1T-like array.

Combined detection of supernova neutrino signals

Supernova neutrino detection in neutrino and dark matter experiments is usually implemented as a real-time trigger system based on counting neutrino interactions within a moving time window. The sensitivity reach of such experiments can be improved by taking into account the time profile of the expected signal. We propose a shape analysis of the incoming experimental data based on a log likelihood ratio variable containing the assumed signal shape. This approach also allows a combination of potential supernova signals in different detectors for a further sensitivity boost. The method is tested on the NOvA detectors to study their combined sensitivity to the core-collapse supernova signal, and also on KamLAND, Borexino and SK-Gd as potential detectors of presupernova neutrinos. Using the shape analysis enhances the signal significance for supernova detection and prediction, as well as the sensitivity reach of the experiment. It also extends the supernova prediction time when applied to the presupernova neutrino signal detection. Enhancements achieved with the shape analysis persist even in the case when the actual signal doesn't match the expected signal model.

CNNs for enhanced background discrimination in DSNB searches in large-scale water-Gd detectors

Gadolinium-loading of large water Cherenkov detectors is a prime method for the detection of the Diffuse Supernova Neutrino Background (DSNB). While the enhanced neutron tagging capability greatly reduces single-event backgrounds, correlated events mimicking the IBD coincidence signature remain a potentially harmful background. Neutral-Current (NC) interactions of atmospheric neutrinos potentially dominate the DSNB signal especially in the low-energy range of the observation window that reaches from about 12 to 30 MeV. The present paper investigates a novel method for the discrimination of this background. Convolutional Neural Networks (CNNs) offer the possibility for a direct analysis and classification of the PMT hit patterns of the prompt events. Based on the events generated in a simplified SuperKamiokande-like detector setup, we find that a trained CNN can maintain a signal efficiency of 96% while reducing the residual NC background to 2% of the original rate. Comparing to recent predictions of the DSNB signal and measurements of the NC background levels in Super-Kamiokande, the corresponding signal-to-background ratio is about 4:1, providing excellent conditions for a DSNB discovery.

Gravitational Lensing of Supernova Neutrino Bursts

Supernova neutrino bursts have been observed from extragalactic distances. This note addresses the question of how gravitational lensing could distort the information in the burst. We apply the gravitational lens hypothesis to try to understand the time and brightness structure of the SN1987A neutrino observations. Estimates of a possible lensing mass and alignment are made. These estimates suggest a path to verification.