Atomic Many-Body Effects in Direct Detection of Neutrinos and Dark Matters

博士 === 國立臺灣大學 === 物理學研究所 === 105 === The processes of atomic ionization of detectors by scattering with neutrinos in a search of their electromagnetic properties and with light dark matter candidates (e.g. sterile neutrino) are interested for many experimental groups, because those studies on neutri...

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Bibliographic Details
Main Authors: Chih-Pan Wu, 吳峙磐
Other Authors: Jiunn-Wei Chen
Format: Others
Language:en_US
Published: 2017
Online Access:http://ndltd.ncl.edu.tw/handle/s55p99
Description
Summary:博士 === 國立臺灣大學 === 物理學研究所 === 105 === The processes of atomic ionization of detectors by scattering with neutrinos in a search of their electromagnetic properties and with light dark matter candidates (e.g. sterile neutrino) are interested for many experimental groups, because those studies on neutrinos and dark matters rely on the direct detection with detectors composed by pure atom or crystal. As current experimental searches for neutrinos and dark matters have lowered the detector threshold down to the sub-keV regime, accurate many-body calculations for atomic ionization are warranted for giving reliable results of experimental comparisons. With the benchmark of photoionization and analytic hydrogen calculations, we perform ab initio many-body methods to show how atomic effects modify the cross sections of neutrino or dark matter scattering with electrons in Ge, Xe and other targets within 5~10% accuracy. The main goal is applying these methods to study (1) neutrino electromagnetic properties, (2) dark matter interactions within effective field theory framework, and (3) low-energy electronic recoil caused by solar neutrinos in multi-ton xenon detectors, which is an important subject not only because it is a source of the irreducible background for direct searches of weakly interacting massive particles (WIMPs), but also because it provides a viable way to measure the solar pp and 7Be neutrinos at the precision level of current standard solar model predictions.