We report on a search for nuclear recoil signals from solar ^{8}B neutrinos elastically scattering off xenon nuclei in XENON1T data, lowering the energy threshold from 2.6 to 1.6 keV. We develop a ...variety of novel techniques to limit the resulting increase in backgrounds near the threshold. No significant ^{8}B neutrinolike excess is found in an exposure of 0.6 t×y. For the first time, we use the nondetection of solar neutrinos to constrain the light yield from 1-2 keV nuclear recoils in liquid xenon, as well as nonstandard neutrino-quark interactions. Finally, we improve upon world-leading constraints on dark matter-nucleus interactions for dark matter masses between 3 and 11 GeV c^{-2} by as much as an order of magnitude.
This paper details the first application of a software tagging algorithm to reduce radon-induced backgrounds in liquid noble element time projection chambers, such as XENON1T and XENONnT. The ...convection velocity field in XENON1T was mapped out using \(^{222}\text{Rn}\) and \(^{218}\text{Po}\) events, and the root-mean-square convection speed was measured to be \(0.30 \pm 0.01\) cm/s. Given this velocity field, \(^{214}\text{Pb}\) background events can be tagged when they are followed by \(^{214}\text{Bi}\) and \(^{214}\text{Po}\) decays, or preceded by \(^{218}\text{Po}\) decays. This was achieved by evolving a point cloud in the direction of a measured convection velocity field, and searching for \(^{214}\text{Bi}\) and \(^{214}\text{Po}\) decays or \(^{218}\text{Po}\) decays within a volume defined by the point cloud. In XENON1T, this tagging system achieved a \(^{214}\text{Pb}\) background reduction of \(6.2^{+0.4}_{-0.9}\%\) with an exposure loss of \(1.8\pm 0.2 \%\), despite the timescales of convection being smaller than the relevant decay times. We show that the performance can be improved in XENONnT, and that the performance of such a software-tagging approach can be expected to be further improved in a diffusion-limited scenario. Finally, a similar method might be useful to tag the cosmogenic \(^{137}\text{Xe}\) background, which is relevant to the search for neutrinoless double-beta decay.
The XENONnT experiment searches for weakly-interacting massive particle (WIMP) dark matter scattering off a xenon nucleus. In particular, XENONnT uses a dual-phase time projection chamber with a ...5.9-tonne liquid xenon target, detecting both scintillation and ionization signals to reconstruct the energy, position, and type of recoil. A blind search for nuclear recoil WIMPs with an exposure of 1.1 tonne-years yielded no signal excess over background expectations, from which competitive exclusion limits were derived on WIMP-nucleon elastic scatter cross sections, for WIMP masses ranging from 6 GeV/\(c^2\) up to the TeV/\(c^2\) scale. This work details the modeling and statistical methods employed in this search. By means of calibration data, we model the detector response, which is then used to derive background and signal models. The construction and validation of these models is discussed, alongside additional purely data-driven backgrounds. We also describe the statistical inference framework, including the definition of the likelihood function and the construction of confidence intervals.
The multi-staged XENON program at INFN Laboratori Nazionali del Gran Sasso aims to detect dark matter with two-phase liquid xenon time projection chambers of increasing size and sensitivity. The ...XENONnT experiment is the latest detector in the program, planned to be an upgrade of its predecessor XENON1T. It features an active target of 5.9 tonnes of cryogenic liquid xenon (8.5 tonnes total mass in cryostat). The experiment is expected to extend the sensitivity to WIMP dark matter by more than an order of magnitude compared to XENON1T, thanks to the larger active mass and the significantly reduced background, improved by novel systems such as a radon removal plant and a neutron veto. This article describes the XENONnT experiment and its sub-systems in detail and reports on the detector performance during the first science run.
We perform a blind search for particle signals in the XENON1T dark matter detector that occur close in time to gravitational wave signals in the LIGO and Virgo observatories. No particle signal is ...observed in the nuclear recoil, electronic recoil, CE\(\nu\)NS, and S2-only channels within \(\pm\) 500 seconds of observations of the gravitational wave signals GW170104, GW170729, GW170817, GW170818, and GW170823. We use this null result to constrain mono-energetic neutrinos and Beyond Standard Model particles emitted in the closest coalescence GW170817, a binary neutron star merger. We set new upper limits on the fluence (time-integrated flux) of coincident neutrinos down to 17 keV at 90% confidence level. Furthermore, we constrain the product of coincident fluence and cross section of Beyond Standard Model particles to be less than \(10^{-29}\) cm\(^2\)/cm\(^2\) in the 5.5-210 keV energy range at 90% confidence level.
The precision in reconstructing events detected in a dual-phase time projection chamber depends on an homogeneous and well understood electric field within the liquid target. In the XENONnT TPC the ...field homogeneity is achieved through a double-array field cage, consisting of two nested arrays of field shaping rings connected by an easily accessible resistor chain. Rather than being connected to the gate electrode, the topmost field shaping ring is independently biased, adding a degree of freedom to tune the electric field during operation. Two-dimensional finite element simulations were used to optimize the field cage, as well as its operation. Simulation results were compared to \({}^{83m}\mathrm{Kr}\) calibration data. This comparison indicates an accumulation of charge on the panels of the TPC which is constant over time, as no evolution of the reconstructed position distribution of events is observed. The simulated electric field was then used to correct the charge signal for the field dependence of the charge yield. This correction resolves the inconsistent measurement of the drift electron lifetime when using different calibrations sources and different field cage tuning voltages.
We report on the first search for nuclear recoils from dark matter in the form of weakly interacting massive particles (WIMPs) with the XENONnT experiment which is based on a two-phase time ...projection chamber with a sensitive liquid xenon mass of \(5.9\) t. During the approximately 1.1 tonne-year exposure used for this search, the intrinsic \(^{85}\)Kr and \(^{222}\)Rn concentrations in the liquid target were reduced to unprecedentedly low levels, giving an electronic recoil background rate of \((15.8\pm1.3)~\mathrm{events}/(\mathrm{t\cdot y \cdot keV})\) in the region of interest. A blind analysis of nuclear recoil events with energies between \(3.3\) keV and \(60.5\) keV finds no significant excess. This leads to a minimum upper limit on the spin-independent WIMP-nucleon cross section of \(2.58\times 10^{-47}~\mathrm{cm}^2\) for a WIMP mass of \(28~\mathrm{GeV}/c^2\) at \(90\%\) confidence level. Limits for spin-dependent interactions are also provided. Both the limit and the sensitivity for the full range of WIMP masses analyzed here improve on previous results obtained with the XENON1T experiment for the same exposure.
We developed a detector signal characterization model based on a Bayesian network trained on the waveform attributes generated by a dual-phase xenon time projection chamber. By performing inference ...on the model, we produced a quantitative metric of signal characterization and demonstrate that this metric can be used to determine whether a detector signal is sourced from a scintillation or an ionization process. We describe the method and its performance on electronic-recoil (ER) data taken during the first science run of the XENONnT dark matter experiment. We demonstrate the first use of a Bayesian network in a waveform-based analysis of detector signals. This method resulted in a 3% increase in ER event-selection efficiency with a simultaneously effective rejection of events outside of the region of interest. The findings of this analysis are consistent with the previous analysis from XENONnT, namely a background-only fit of the ER data.
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