Gaussian mixture model clustering algorithms for the analysis of high-precision mass measurements

Weber, Colin M.; Ray, D.; Valverde, A. A.; Clark, J. A.; Sharma, K. S.

doi:10.1016/j.nima.2021.166299

Cited by 27 publications

(6 citation statements)

References 17 publications

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“…From the model, information on the amplitude, the center position, and the covariance matrix of each component could be obtained. The possibility to use the Gaussian Mixture Model to determine the center positions of the event clusters for mass measurements has been demonstrated by Weber et al [ 17 ]. The Gaussian distribution of each component is reflected by the contour lines in Fig.…”

Section: Discussionmentioning

confidence: 99%

Applying machine learning methods for the analysis of two-dimensional mass spectra

Gao¹,

Solders²,

Al-Adili³

et al. 2023

Eur. Phys. J. A

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In a measurement of isomeric yield-ratios in fission, the Phase-Imaging Ion-Cyclotron-Resonance technique, which projects the radial motions of ions in the Penning trap (JYFLTRAP) onto a position-sensitive micro-channel plate detector, has been applied. To obtain the yield ratio, that is the relative population of two states of an isomer pair, a novel analysis procedure has been developed to determine the number of detected ions in each state, as well as corrections for the detector efficiency and decay losses. In order to determine the population of the states in cases where their mass difference is too small to reach full separation, a Bayesian Gaussian Mixture model was implemented. The position-dependent efficiency of the micro-channel plate detector was calibrated by mapping it with $$^{133}$$ 133 Cs$$^+$$ + ions, and a Gaussian Process was trained with the position data to construct an efficiency function that could be used to correct the recorded distributions. The obtained numbers of counts of excited and ground-state ions were used to derive the isomeric yield ratio, taking into account decay losses as well as feeding from precursors.

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Section: Discussionmentioning

confidence: 99%

Applying machine learning methods for the analysis of two-dimensional mass spectra

Gao¹,

Solders²,

Al-Adili³

et al. 2023

Eur. Phys. J. A

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“…Ref. [41]. For the final phase measurement, there can be several spots along with the spot corresponding to the ion of interest depending on contaminants present in the beam.…”

Section: Experimental Descriptionmentioning

confidence: 99%

Precise Q value measurements of $^{112,113}$Ag and $^{115}$Cd with the Canadian Penning trap for evaluation of potential ultra-low Q value $β$-decays

Gamage¹,

Sandler²,

Buchinger³

et al. 2022

Preprint

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Background: An ultra-low Q value β-decay can occur from a parent nuclide to an excited nuclear state in the daughter such that QUL 1 keV. These decay processes are of interest for nuclear β-decay theory and as potential candidates in neutrino mass determination experiments. To date, only one ultra-low Q value β-decay has been observed-that of 115 In with Q β = 147(10) eV. A number of other potential candidates exist, but improved mass measurements are necessary to determine if these decay channels are energetically allowed and, in fact, ultra-low.Purpose: To perform precise β-decay Q value measurements of 112,113 Ag and 115 Cd and to use them in combination with nuclear energy level data for the daughter isotopes 112,113 Cd and 115 In to determine if the potential ultra-low Q value β-decay branches of 112,113 Ag and 115 Cd are energetically allowed and 1 keV. Method:The Canadian Penning Trap at Argonne National Laboratory was used to measure the cyclotron frequency ratios of singly-charged 112,113 Ag and 115 Cd ions with respect to their daughters 112,113 Cd and 115 In. From these measurements, the ground-state to ground-state β-decay Q values were obtained. Results: The 112 Ag → 112 Cd, 113 Ag → 113 Cd, and 115 Cd → 115 In β-decay Q values were measured to be Q β ( 112 Ag) = 3990.16(22) keV, Q β ( 113 Ag) = 2085.7(4.6) keV, and Q β ( 115 Cd) = 1451.36(34) keV. These results were compared to energies of excited states in 112 Cd at 3997.75(14) keV, 113 Cd at 2015.6(2.5) and 2080(10) keV, and 115 In at 1448.787(9) keV, resulting in precise QUL values for the potential decay channels of -7.59(26) keV, 6(11) keV, and 2.57(34) keV, respectively. Conclusion:The potential ultra-low Q value decays of 112 Ag and 115 Cd have been ruled out. 113 Ag is still a possible candidate until a more precise measurement of the 2080(10) keV, 1/2 + state of 113 Cd is available. In the course of this work we have found the ground state mass of 113 Ag reported in the 2020 Atomic Mass Evaluation [Wang, et al., Chin. Phys. C 45, 030003 (2021)] to be lower than our measurement by 69(17) keV (a 4σ discrepancy).

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“…(5) Model-based clustering: Assuming that the data set can be aggregated into N clusters, a model is constructed based on the data objects in each cluster. A model-based algorithm may locate clusters by constructing a density function that reflects the spatial distribution of data points, and it also automatically decides on the number of clusters based on standard statistics, considering noisy data or isolated points, resulting in robust clustering methods [39]. Typical model-based clustering methods include statistical methods (e.g., COBWEB, CLASSIT, and AutoClass) or neural network methods (e.g., competitive learning and self-organizing feature maps) [40,41].…”

Section: Current Status Of Research Applications Of Data Processing A...mentioning

confidence: 99%

An Improved Optimization Algorithm Based on Density Grid for Green Storage Monitoring System

et al. 2022

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This study takes a sample of green storage monitoring data for corn from a biochemical energy enterprise, based on the enterprise’s original storage monitoring system while establishing a “green fortress” intending to achieve green and sustainable grain storage. This paper proposes a set of processing algorithms for real-time flow data from the storage system based on cluster analysis to detect abnormal storage conditions, achieve the goal of green grain storage and maximize benefits for the enterprises. Firstly, data from the corn storage monitoring system and the current status of research on data processing algorithms are analyzed. Our study summarizes the processing of re-al-time stream data together with the characteristics of the monitoring system and discusses the application of clustering analysis algorithms. The study includes an in-depth study of the green storage monitoring system data for corn and the processing requirements for real-time stream data. As the main novelty of this research, the optimization algorithm model is applied to the green storage monitoring system for maize and is validated. Finally, the processing results for the green storage monitoring data for maize are presented in graphical and textual formats.

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Gaussian mixture model clustering algorithms for the analysis of high-precision mass measurements

Cited by 27 publications

References 17 publications

Applying machine learning methods for the analysis of two-dimensional mass spectra

Applying machine learning methods for the analysis of two-dimensional mass spectra

Precise Q value measurements of $^{112,113}$Ag and $^{115}$Cd with the Canadian Penning trap for evaluation of potential ultra-low Q value $β$-decays

An Improved Optimization Algorithm Based on Density Grid for Green Storage Monitoring System

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