A density-based clustering algorithm for the CYGNO data analysis

Baracchini, E.; Benussi, L.; Bianco, S.; Capoccia, C.; Caponero, M.; Cavoto, G.; Cortez, A.F.V.; Costa, I. A.; Marco, E. Di; D'imperio, Giulia; Dho, Giorgio; Iacoangeli, F.; Maccarrone, G.; Marafini, M.; Mazzitelli, G.; Messina, A.; Nobrega, R. Antunes; Orlandi, Alessio; Paoletti, E.; Passamonti, L.; Petrucci, F.; Piccolo, D.; Pierluigi, D.; Pinci, D.; Renga, F.; Rosatelli, F.; Russo, A.; Saviano, G.; Tesauro, Roberto; Tomassini, S.

doi:10.1088/1748-0221/15/12/t12003

Cited by 24 publications

(26 citation statements)

References 14 publications

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“…Acquisition is conducted on the premise of changes in adjacent data. With the changes in data, the system acquisition interval will change correspondingly, and the temporary changes will cause disorderly changes in the acquisition interval, which will consume the system and cause large errors [14]. erefore, the dual judgment method is used to calculate data fluctuation.…”

Section: Priority Discrimination Of Library Personalized Information Collectionmentioning

confidence: 99%

Research on Fast Recommendation Algorithm of Library Personalized Information Based on Density Clustering

Shi

Zhu

2022

Security and Communication Networks

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In order to improve the accuracy and efficiency of library information recommendation, this paper proposes a fast recommendation algorithm for library personalized information based on density clustering. According to the analysis of the clustering principle, the algorithm achieves the clustering of library information by designing density interval function. Then, the collection priority of library personalized information is judged, and the library personalized information is recommended quickly by designing tags according to the library users’ preferences. Experimental results show that the recommendation accuracy and F value of the proposed algorithm are higher than those of the two traditional algorithms, and its coverage rate is higher and the mean absolute error is lower, indicating that the proposed algorithm effectively achieves the design expectation.

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Section: Priority Discrimination Of Library Personalized Information Collectionmentioning

confidence: 99%

Research on Fast Recommendation Algorithm of Library Personalized Information Based on Density Clustering

Shi

Zhu

2022

Security and Communication Networks

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show abstract

“…In order to identify patterns of energy deposit due to both NR and ER a 2D cluster identification technique (based on DBSCAN and Geodesic active contour) was developed [11]. By exposing the prototypes to neutrons NR have been identified with a 40% efficiency and a 96% ER rejection at the energy of about 6 keV ee [12].…”

Section: Prototypesmentioning

confidence: 99%

The Cygno Experiment

Cavoto¹,

Baracchini²,

Benussi³

et al. 2022

Proceedings of the European Physical Society Conference on High Energy Physics — PoS(EPS-HEP2021)

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“…The challenge is to properly reconstruct the recoil trace being able to associate to it each single pixel hit by photons originating from the NR energy deposit. To accomplish this task, we first use i [6] an iterative and customised version of the algorithm to find isolated clusters, then we process clusters iteratively with a morphological geodesic active contour algorithm (GAC) to connect clusters together in a long trace. The GAC algorithm, using the light intensity as an additional variable, can successfully disentangle overlapping clusters belonging to different kinds of particles.…”

Section: Response To Nuclear Recoilsmentioning

confidence: 99%