Narrow Results

The Cryogenic Underground Observatory for Rare Events (CUORE) is a tonne-scale cryogenic experiment located at the Laboratori Nazionali del Gran Sasso that exploits bolometric technique to search for neutrinoless double beta decay $(0\nu \beta \beta )$ of ${}^{130}$Te. The detector consists of a segmented array of 988 natural ${\mathrm{\text{TeO}}}_2$ cubic crystals arranged in a cylindrical compact structure of 19 towers. The detector construction was completed in August 2016 and data taking started in Spring 2017. In this work, we present a brief description of the bolometric technique for rare events search and the CUORE detector, then we concentrate on the data analysis results. In this respect, we focus on the procedure for data processing and on the first $0\nu \beta \beta$ results we obtained from a total ${\mathrm{\text{TeO}}}_2$ exposure of $86.3\mathrm{\text{kg}}\cdot \mathrm{\text{yr}}$. Next, we illustrate the main background sources and the CUORE background model, from which we obtain the most precise measurement of ${}^{130}$Te $2\nu \beta \beta$ half-life to date. Finally, we discuss the improvements achieved with 2018 and 2019 detector optimization campaigns and the current perspectives of our experiment.

The Cryogenic Underground Observatory for Rare Events (CUORE) is the first bolometric experiment searching for neutrino-less double-beta (0$\nu \beta \beta$) decay that has been able to reach the one-ton scale. The detector, located at the Laboratori Nazionali del Gran Sasso in Italy, consists of an array of 988 TeO₂ crystals arranged in a compact cylindrical structure of 19 towers. Following the completion of the detector construction in August 2016, CUORE began its first physics data run in 2017 at a base temperature of about 10 mK. Following multiple optimization campaigns in 2018, CUORE is currently in stable operating mode. In 2019, CUORE released its second result of the search for 0$\nu \beta \beta$ corresponding to a TeO₂ exposure of 372.5 kg⋅yr and a median exclusion sensitivity to a ¹³⁰Te 0$\nu \beta \beta$ decay half-life of $1.7\cdot 10^{25}$ yr. We find no evidence for 0$\nu \beta \beta$ decay and set a 90% C.I. Bayesian lower limit of $3.2\cdot 10^{25}$ yr on the ¹³⁰Te 0$\nu \beta \beta$ decay half-life. We present the current status of CUORE’s search for 0$\nu \beta \beta$. We give an update of the CUORE background model and the measurement of the ¹³⁰Te two neutrino double-beta (2$\nu \beta \beta$) decay half-life. Eventually, we show the preliminary results on half-life limits from the analysis of ¹³⁰Te 0$\nu \beta \beta$ and 2$\nu \beta \beta$ decay to the first 0${}^{+}$ excited state of ${}^{130}$Xe.

A novel video-based finger writing virtual character recognition system (FVCRS) is described in this paper. With this FVCR system, one can enter characters into a computer by just using the movement of fingertip, without any additional device such as a keyboard or a digital pen. This provides a new wireless character-inputting method. A simple but effective background model is built for segmenting human-finger movements from cluttered background. A robust fingertip detection algorithm based on feature matching is given, and recognition of the finger-writing character is by a DTW-based classifier. Experiments show that the FVCRS can successfully recognize finger-writing uppercase and lowercase English alphabet with the accuracy of 95.3% and 98.7%, respectively.

Continuously Adaptive Mean shift (CAMSHIFT) is a popular algorithm for visual tracking, providing speed and robustness with minimal training and computational cost. While it performs well with a fixed camera and static background scene, it can fail rapidly when the camera moves or the background changes since it relies on static models of both the background and the tracked object. Furthermore it is unable to track objects passing in front of backgrounds with which they share significant colours.

We describe a new algorithm, the Adaptive Background CAMSHIFT (ABCshift), which addresses both of these problems by using a background model which can be continuously relearned for every frame with minimal additional computational expense. Further, we show how adaptive background relearning can occasionally lead to a particular mode of instability which we resolve by comparing background and tracked object distributions using a metric based on the Bhattacharyya coefficient.