New test of modulated electron capture decay of hydrogen-like 142Pm ions: Precision measurement of purely exponential decay

Ozturk, Ferhat; Akkuš, B.; Atanasov, D.; Beyer, H. F.; Bosch, F.; Boutin, D.; Brandau, C.; Bühler, P.; Cakirli, R. B.; Chen, R.J.; Chen, Weidong; Chen, X.C.; Dillmann, I.; Dimopoulou, C.; Enders, W.; Essel, H.G.; Faestermann, T.; Forstner, O.; Gao, Bo; Geißel, H.; Gernhäuser, R.; Grisenti, R. E.; Gumberidze, A.; Hagmann, S.; Heftrich, T.; Heil, M.; Herdrich, Marc Oliver; Hillenbrand, P.‐M.; Izumikawa, T.; Kienle, Paul; Klaushofer, C.; Kleffner, C.; Kozhuharov, C.; Knöbel, R.; Kovalenko, O.; Kreim, Susanne; Kühl, T.; Lederer-Woods, C.; Lestinsky, M.; Litvinov, S.; Litvinov, Yu. A.; Liu, Z.; Ma, Xueli; Maier, L.; Mei, B.; Miura, H.; Mukha, I.; Najafi, Amirali; Nagae, D.; Nishimura, Tomoaki; Nociforo, C.; Nolden, F.; Ohtsubo, T.; Öktem, Y.; Omika, Shunichiro; Ozawa, A.; Petridis, N.; Piotrowski, J.; Reifarth, R.; Roßbach, J.; Sánchez, R.; Sanjari, M. S.; Scheidenberger, C.; Sidhu, R. S.; Simon, H.; Spillmann, U.; Steck, M.; Stöhlker, Th.; Sun, B.; Susam, Lidya Amon; Suzaki, F.; Suzuki, Takeshi; Torilov, S. Yu.; Trageser, C.; Trassinelli, Martino; Trotsenko, S.; Tu, X. L.; Walker, P. M.; Wang, M.; Weber, G.; Weick, H.; Winckler, N.; Winters, D.F.A.; Woods, P. J.; Yamaguchi, Takayuki; Xu, Xing; Yan, X. L.; Jiang, Yang; Yuan, Y.J.; Zhang, Y.H.; Zhou, Xianming

doi:10.1016/j.physletb.2019.134800

Cited by 20 publications

(14 citation statements)

References 77 publications

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“…It is written in Fortran90 with some subroutines in Fortran77, and parallelized via OPEN- MPI. It is mainly developed and implemented for the analysis in the fields of atomic, nuclear and solid-state physics [ 16 , 39 , 40 , 47 , 48 , 49 , 50 ]. It is accompanied by a Python function library for visualization of the results and for automatization of series of analyses.…”

Section: Nested Sampling and Nestedfitmentioning

confidence: 99%

“…Here, an exponential decay with a sinusoidal modulation is used as a model, considered parameters are the relative amplitude, pulsation and phase (see Ref. [ 16 ] for more details). Both data sets, presented in Figure 2 , are characterized by low statistic and the presence of many local maxima of the likelihood function, which makes them therefore difficult to analyze.…”

Section: Mean Shift Clustering Algorithm and Its Implementationmentioning

confidence: 99%

“… Data corresponding to the high-resolution X-ray spectrum of the helium-like uranium

intrashell transition obtained by Bragg diffraction from a curved crystal [ 57 ] ( left ) and of the single decay of H-like

Pm ions to the stable

Nd bare nucleus via electron capture [ 16 ] ( right ). …”

Section: Figurementioning

confidence: 99%

“…At present, Bayesian methods are routinely used in many fields: astrophysics and cosmology [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ], particle physics [ 9 ], plasma physics [ 10 , 11 ], machine learning [ 12 ] and many others [ 13 , 14 ]. In the past few years, they were also applied to nuclear [ 15 , 16 ] and atomic physics [ 17 , 18 , 19 , 20 , 21 ]. On one hand, one of the reasons for this success is related to the possibility of assigning a probability value to models (hypotheses) from the analysis of the same set of data in a very defined framework.…”

Section: Introductionmentioning

confidence: 99%

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Mean Shift Cluster Recognition Method Implementation in the Nested Sampling Algorithm

Trassinelli

Ciccodicola

2020

Entropy

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Nested sampling is an efficient algorithm for the calculation of the Bayesian evidence and posterior parameter probability distributions. It is based on the step-by-step exploration of the parameter space by Monte Carlo sampling with a series of values sets called live points that evolve towards the region of interest, i.e. where the likelihood function is maximal. In presence of several local likelihood maxima, the algorithm converges with difficulty. Some systematic errors can also be introduced by unexplored parameter volume regions. In order to avoid this, different methods are proposed in the literature for an efficient search of new live points, even in presence of local maxima. Here we present a new solution based on the mean shift cluster recognition method implemented in a random walk search algorithm. The clustering recognition is integrated within the Bayesian analysis program NestedFit. It is tested with the analysis of some difficult cases. Compared to the analysis results without cluster recognition, the computation time is considerably reduced. At the same time, the entire parameter space is efficiently explored, which translates into a smaller uncertainty of the extracted value of the Bayesian evidence.

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Section: Nested Sampling and Nestedfitmentioning

confidence: 99%

Section: Mean Shift Clustering Algorithm and Its Implementationmentioning

confidence: 99%

“… Data corresponding to the high-resolution X-ray spectrum of the helium-like uranium

intrashell transition obtained by Bragg diffraction from a curved crystal [ 57 ] ( left ) and of the single decay of H-like

Pm ions to the stable

Nd bare nucleus via electron capture [ 16 ] ( right ). …”

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mean Shift Cluster Recognition Method Implementation in the Nested Sampling Algorithm

Trassinelli

Ciccodicola

2020

Entropy

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“…(8) Notice that the definition of p 0 (t) takes into account that the nondecay probability decreases with time. Temporal changes of p 0 (t) are often measured in experiments, since it corresponds to the number of decay products per unit time (for instance the lifetime measurement of the neutron by the beam method [53] or the decay of H-like ions via electron capture and neutrino emission [54]). Note, a simple interpretation holds: p L(R) (t)dt is the probability that the decay occurs to the right (left) between t and t + dt.…”

Section: The Modelmentioning

confidence: 99%

Capturing non-exponential dynamics in the presence of two decay channels

Giacosa,

Kościk,

Sowiński

2019

Preprint

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The most unstable quantum states and elementary particles possess more than a single decay channel. At the same time, it is well known that typically the decay law is not simply exponential. Therefore, it is natural to ask how to spot the non-exponential decay when (at least) two decay channels are opened. In this work, we study the tunneling phenomenon of an initially localized particle in two spatially opposite directions through two different barriers, mimicking two decay channels. In this framework, through a specific quantum mechanical examples which can be accurately solved, we study general properties of a two-channel decay that apply for various unstable quantum states (among which also for unstable particles). Apart from small deviations at early times, the survival probability and the partial tunneling probability along the chosen direction are very well described by the exponential-decay model. In contrast, the ratios of the decay probabilities and probability densities are evidently not a simple constant (as they would be in the exponential limit), but display time-persisting oscillations. Hence, these ratios are optimal witnesses of deviations from the exponential decay law.

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