A bstract We present a particle physics model to explain the observed enhancement in the Xenon-1T data at an electron recoil energy of 2.5 keV. The model is based on a U(1) extension of the Standard Model where the dark sector consists of two essentially mass degenerate Dirac fermions in the sub-GeV region with a small mass splitting interacting with a dark photon. The dark photon is unstable and decays before the big bang nucleosynthesis, which leads to the dark matter constituted of two essentially mass degenerate Dirac fermions. The Xenon-1T excess is computed via the inelastic exothermic scattering of the heavier dark fermion from a bound electron in xenon to the lighter dark fermion producing the observed excess events in the recoil electron energy. The model can be tested with further data from Xenon-1T and in future experiments such as SuperCDMS.
A next-generation liquid xenon observatory for dark matter and neutrino physics
The nature of dark matter and properties of neutrinos are among the most pressing issues in contemporary particle physics. The dual-phase xenon time-projection chamber is the leading technology to cover the available parameter space for weakly interacting massive particles, while featuring extensive sensitivity to many alternative dark matter candidates. These detectors can also study neutrinos through neutrinoless double-beta decay and through a variety of astrophysical sources. A next-generation xenon-based detector will therefore be a true multi-purpose observatory to significantly advance particle physics, nuclear physics, astrophysics, solar physics, and cosmology. This review article presents the science cases for such a detector.
- Authors:
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Award ID(s):
- 1944826
- Publication Date:
- NSF-PAR ID:
- 10386754
- Journal Name:
- Journal of Physics G: Nuclear and Particle Physics
- Volume:
- 50
- Issue:
- 1
- Page Range or eLocation-ID:
- Article No. 013001
- ISSN:
- 0954-3899
- Publisher:
- IOP Publishing
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Two-neutrino double electron capture (2νECEC) is a second-order Weak process with predictedhalf-lives that surpass the age of the Universe by many orders of magnitude [1]. Until now, indi-cations for 2νECEC decays have only been seen for two isotopes,78Kr [2, 3] and130Ba [4, 5], andinstruments with very low background levels are needed to detect them directly with high statisticalsignificance [6, 7]. The 2νECEC half-life provides an important input for nuclear structure models[8–14] and its measurement represents a first step in the search for the neutrinoless double electroncapture processes (0νECEC). A detection of the latter would have implications for the nature of theneutrino and give access to the absolute neutrino mass [15–17]. Here we report on the first directobservation of 2νECEC in124Xe with the XENON1T Dark Matter detector. The significance of thesignal is 4.4σand the corresponding half-lifeT2νECEC1/2= (1.8±0.5stat±0.1sys)×1022y is the longestever measured directly. This study demonstrates that the low background and large target massof xenon-based Dark Matter detectors make them well suited to measuring other rare processes aswell, and it highlights the broad physics reach for even larger next-generation experiments
-
The Deep Underground Neutrino Experiment (DUNE) is an international, world-class experiment aimed at exploring fundamental questions about the universe that are at the forefront of astrophysics and particle physics research. DUNE will study questions pertaining to the preponderance of matter over antimatter in the early universe, the dynamics of supernovae, the subtleties of neutrino interaction physics, and a number of beyond the Standard Model topics accessible in a powerful neutrino beam. A critical component of the DUNE physics program involves the study of changes in a powerful beam of neutrinos, i.e., neutrino oscillations, as the neutrinos propagate a long distance. The experiment consists of a near detector, sited close to the source of the beam, and a far detector, sited along the beam at a large distance. This document, the DUNE Near Detector Conceptual Design Report (CDR), describes the design of the DUNE near detector and the science program that drives the design and technology choices. The goals and requirements underlying the design, along with projected performance are given. It serves as a starting point for a more detailed design that will be described in future documents.
-
Abstract For the first time, time-dependent internal charge amplification through impact ionization has been observed in a planar germanium (Ge) detector operated at cryogenic temperature. In a time period of 30 and 45 min after applying a bias voltage, the charge energy corresponding to a baseline of the 59.54 keV
$$\gamma $$ $$^{241}$$ -
This work proposes a domain-informed neural network architecture for experimental particle physics, using particle interaction localization with the time-projection chamber (TPC) technology for dark matter research as an example application. A key feature of the signals generated within the TPC is that they allow localization of particle interactions through a process called reconstruction (i.e., inverse-problem regression). While multilayer perceptrons (MLPs) have emerged as a leading contender for reconstruction in TPCs, such a black-box approach does not reflect prior knowledge of the underlying scientific processes. This paper looks anew at neural network-based interaction localization and encodes prior detector knowledge, in terms of both signal characteristics and detector geometry, into the feature encoding and the output layers of a multilayer (deep) neural network. The resulting neural network, termed Domain-informed Neural Network (DiNN), limits the receptive fields of the neurons in the initial feature encoding layers in order to account for the spatially localized nature of the signals produced within the TPC. This aspect of the DiNN, which has similarities with the emerging area of graph neural networks in that the neurons in the initial layers only connect to a handful of neurons in their succeeding layer, significantly reduces the number of parametersmore »
