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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 stillmore »
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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 lowmore »
