![]() This larger target (or “cross section”) makes CE □ NS much more likely than other neutrino interactions with individual nucleons or electrons. Compared to other neutrino-nucleus interactions in which the neutrino interacts with a single neutron or proton, CE □ NS is a coherent interaction between the neutrino and all the neutrons and protons in the nucleus. At the Weak Interactions and Neutrinos 2021 conference earlier this month, Strigari gave an overview of what CE □ NS holds in store for researchers.įirst postulated in the 1970s, CE □ NS occurs when a neutrino “bumps” into a nucleus and gives it a kick. Now, a dozen other experiments are aiming to retrieve a CE □ NS signal. The first observation of CE □ NS happened just four years ago in an accelerator-based experiment. If you’re searching for neutrinos, it’s a signal,” says Louis Strigari from Texas A&M University. “If you’re searching for dark matter, CE □ NS is a background. The neutrino floor is the result of a particular neutrino interaction called coherent elastic neutrino nucleus scattering, or CE □ NS (pronounced “sevens”). Reaching this floor might sound like bad news, but some researchers see it as an opportunity for gaining new information about neutrinos, as well as for potentially uncovering particles and interactions beyond the standard model of particle physics. ![]() This neutrino background is still below the sensitivity of dark matter detectors, but as such detectors continue to become more sensitive, it’s only a matter of time before neutrino events will begin to dominate the signal. The “neutrino floor” has been looming under dark matter searches for years. Soon, however, detectors like this will have to contend with a background from neutrinos. The bottom array of photomultiplier tubes is designed to capture light produced from dark matter interactions. This work concludes by assessing the impact of the tools and methods developed in this work on particle energy estimation in MicroBooNE.XENON Collaboration The XENON1T detector shown from below. The performance of the two approaches is compared and contrasted with PIDA, the default PID algorithm used at MicroBooNE. A deep learning PID method (PidNet) is also proposed, based on convolutional neural networks (CNNs) and a semi-supervised representation learning method. A robust PID method (FOMA) is developed using a novel analytic approximation to the mode of the dE/dx distribution. Improvements to the vertex reconstruction are made through the development of powerful new variables and the application of machine learning techniques these algorithms are now the default used at MicroBooNE and have enabled new studies of neutrino interactions with up to six charged particles in the final state. The experiment therefore requires high-quality neutrino interaction vertex reconstruction and PID, which together strongly influence event reconstruction quality and energy/momentum estimation. MicroBooNE's primary physics goal is to resolve the low-energy electron neutrino appearance anomalies observed at MiniBooNE and LSND. The detector comprises a liquid argon time-projection chamber (LArTPC) with a light-collection system, permitting precise tracking of neutrino interaction final states. This thesis presents the results of a study measuring and improving the quality of neutrino interaction vertex reconstruction and particle identification (PID) in the MicroBooNE detector. ![]()
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