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The next core-collapse supernova in the Milky Way or its satellites will represent a once-in-a-generation opportunity to obtain detailed information about the explosion of a star and provide significant scientific insight for a variety of fields because of the extreme conditions found within. Supernovae in our galaxy are not only rare on a human timescale but also happen at unscheduled times, so it is crucial to be ready and use all available instruments to capture all possible information from the event. The first indication of a potential stellar explosion will be the arrival of a bright burst of neutrinos. Its observation by multiple detectors worldwide can provide an early warning for the subsequent electromagnetic fireworks, as well as signal to other detectors with significant backgrounds so they can store their recent data. The supernova early warning system (SNEWS) has been operating as a simple coincidence between neutrino experiments in automated mode since 2005. In the current era of multi-messenger astronomy there are new opportunities for SNEWS to optimize sensitivity to science from the next galactic supernova beyond the simple early alert. This document is the product of a workshop in June 2019 towards design of SNEWS 2.0, an upgraded SNEWS with enhanced capabilities exploiting the unique advantages of prompt neutrino detection to maximize the science gained from such a valuable event.

 

Dual-phase liquid xenon time projection chambers (LXeTPC) have been successfully applied in rare event searches in astroparticle physics because of their ability to reach low backgrounds and detect small scintillation signals with photosensors. Accurate modeling of optical properties is essential for reconstructing particle interactions within these detectors as well as for developing data selection criteria. This is commonly achieved with discretized maps derived from Monte Carlo simulation or approximated with empirical analytical models. In this work, we employ a novel approach to this using a neural network trained with a Poisson log-likelihood ratio loss to model the mapping from light source location to the expected light intensity for each photosensor. We demonstrate its effectiveness by integrating it into a likelihood fitter for position reconstruction, simultaneously providing insights into the uncertainty associated with the reconstructed position. 

Neutrino experiments study the least understood of the Standard Model particles by observing their direct interactions with matter or searching for ultra-rare signals. The study of neutrinos typically requires overcoming large backgrounds, elusive signals, and small statistics. The introduction of state-of-the-art machine learning tools to solve analysis tasks has made major impacts to these challenges in neutrino experiments across the board. Machine learning algorithms have become an integral tool of neutrino physics, and their development is of great importance to the capabilities of next generation experiments. An understanding of the roadblocks, both human and computational, and the challenges that still exist in the application of these techniques is critical to their proper and beneficial utilization for physics applications. This review presents the current status of machine learning applications for neutrino physics in terms of the challenges and opportunities that are at the intersection between these two fields. 

Abstract Xenon dual-phase time projection chambers designed to search for weakly interacting massive particles have so far shown a relative energy resolution which degrades with energy above $$\sim $$ ∼ 200 keV due to the saturation effects. This has limited their sensitivity in the search for rare events like the neutrinoless double-beta decay of $$^{136} \hbox {Xe}$$ 136 Xe at its Q value, $$Q_{\beta \beta }\simeq 2.46\,\hbox {MeV}$$ Q β β ≃ 2.46 MeV . For the XENON1T dual-phase time projection chamber, we demonstrate that the relative energy resolution at $$1\,\sigma /\mu $$ 1 σ / μ is as low as ( $$0.80 \pm 0.02$$ 0.80 ± 0.02 ) % in its one-ton fiducial mass, and for single-site interactions at $$Q_{\beta \beta }$$ Q β β . We also present a new signal correction method to rectify the saturation effects of the signal readout system, resulting in more accurate position reconstruction and indirectly improving the energy resolution. The very good result achieved in XENON1T opens up new windows for the xenon dual-phase dark matter detectors to simultaneously search for other rare events.