An official website of the United States government
We present new constraints on dark matter interactions using 1.7 years of COSINE-100 data. The COSINE-100 experiment, consisting of 106 kg of tallium-doped sodium iodide [NaI(Tl)] target material, is aimed to test DAMA’s claim of dark matter observation using the same NaI(Tl) detectors. Improved event selection requirements, a more precise understanding of the detector background, and the use of a larger dataset considerably enhance the COSINE-100 sensitivity for dark matter detection. No signal consistent with the dark matter interaction is identified and rules out model-dependent dark matter interpretations of the DAMA signals in the specific context of standard halo model with the same NaI(Tl) target for various interaction hypotheses.
more »
« less
The environmental monitoring system at the COSINE-100 experiment
Abstract The COSINE-100 experiment is designed to test the DAMA experiment which claimed an observation of a dark matter signal from an annual modulation in their residual event rate. To measure the 1 %-level signal amplitude, it is crucial to control and monitor nearly all environmental quantities that might systematically mimic the signal. The environmental monitoring also helps ensure a stable operation of the experiment. Here, we describe the design and performance of the centralized environmental monitoring system for the COSINE-100 experiment.
more »
« less
- Award ID(s):
- 1913742
- PAR ID:
- 10335768
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Date Published:
- Journal Name:
- Journal of Instrumentation
- Volume:
- 17
- Issue:
- 01
- ISSN:
- 1748-0221
- Page Range / eLocation ID:
- T01001
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract The DAMA/LIBRA collaboration has reported the observation of an annual modulation in the event rate that has been attributed to dark matter interactions over the last two decades. However, even though tremendous efforts to detect similar dark matter interactions were pursued, no definitive evidence has been observed to corroborate the DAMA/LIBRA signal. Many studies assuming various dark matter models have attempted to reconcile DAMA/LIBRA’s modulation signals and null results from other experiments, however no clear conclusion can be drawn. Apart from the dark matter hypothesis, several studies have examined the possibility that the modulation is induced by variations in detector’s environment or their specific analysis methods. In particular, a recent study presents a possible cause of the annual modulation from an analysis method adopted by the DAMA/LIBRA experiment in which the observed annual modulation could be reproduced by a slowly varying time-dependent background. Here, we study the COSINE-100 data using an analysis method similar to the one adopted by the DAMA/LIBRA experiment and observe a significant annual modulation, however the modulation phase is almost opposite to that of the DAMA/LIBRA data. Assuming the same background composition for COSINE-100 and DAMA/LIBRA, simulated experiments for the DAMA/LIBRA without dark matter signals also provide significant annual modulation with an amplitude similar to DAMA/LIBRA with opposite phase. Even though this observation does not directly explain the DAMA/LIBRA results directly, this interesting phenomenon motivates more profound studies of the time-dependent DAMA/LIBRA background data.more » « less
-
Abstract We present a background model for dark matter searches using an array of NaI(Tl) crystals in the COSINE-100 experiment that is located in the Yangyang underground laboratory. The model includes background contributions from both internal and external sources, including cosmogenic radionuclides and surface $$^{210}$$ 210 Pb contamination. To build the model in the low energy region, with a threshold of 1 keV, we used a depth profile of $$^{210}$$ 210 Pb contamination in the surface of the NaI(Tl) crystals determined in a comparison between measured and simulated spectra. We also considered the effect of the energy scale errors propagated from the statistical uncertainties and the nonlinear detector response at low energies. The 1.7 years COSINE-100 data taken between October 21, 2016 and July 18, 2018 were used for this analysis. Our Monte Carlo simulation provides a non-Gaussian peak around 50 keV originating from beta decays of bulk $$^{210}$$ 210 Pb in a good agreement with the measured background. This model estimates that the activities of bulk $$^{210}$$ 210 Pb and $$^{3}$$ 3 H are dominating the background rate that amounts to an average level of $$2.85\pm 0.15$$ 2.85 ± 0.15 counts/day/keV/kg in the energy region of (1–6) keV, using COSINE-100 data with a total exposure of 97.7 kg $$\cdot $$ · years.more » « less
-
Abstract Several technosignature techniques focus on historic events such as SN 1987A as the basis to search for coordinated signal broadcasts from extraterrestrial agents. The recently discovered SN 2023ixf in the spiral galaxy M101 is the nearest Type II supernova in over a decade, and will serve as an important benchmark event. Here we review the potential for SN 2023ixf to advance ongoing techonsignature searches, particularly signal-synchronization techniques such as the “SETI Ellipsoid” that identifies over time stars that could transmit signals after observing a supernovae event. We find that more than 100 stars within 100 pc are already close to intersecting this SETI Ellipsoid, providing numerous targets for real-time monitoring within ∼3° of SN 2023ixf. We are commencing a radio technosignature monitoring campaign of these targets with the Allen Telescope Array and the Green Bank Telescope.more » « less
-
Abstract While preventive maintenance is crucial in wind turbine operation, conventional condition monitoring systems face limitations in terms of cost and complexity when compared to innovative signal processing techniques and artificial intelligence. In this paper, a cascading deep learning framework is proposed for the monitoring of generator winding conditions, specifically to promptly detect and identify inter-turn short circuit faults and estimate their severity in real time. This framework encompasses the processing of high-resolution current signal samples, coupled with the extraction of current signal features in both time and frequency domains, achieved through discrete wavelet transform. By leveraging long short-term memory recurrent neural networks, our aim is to establish a cost-efficient and reliable condition monitoring system for wind turbine generators. Numeral experiments show an over 97% accuracy for fault diagnosis and severity estimation. More specifically, with the intrinsic feature provided by wavelet transform, the faults can be 100% identified by the diagnosis model.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1088/1748-0221/17/01/T01001
