Xenon and radon time series analysis: A new methodological approach for characterising the local scale effects at CTBT radionuclide network

Longo, A.; Bianchi, Stefano; Plastino, W.

doi:10.1016/j.apradiso.2018.05.011

Cited by 8 publications

(2 citation statements)

References 16 publications

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“…The methodology adopted in this paper is intended to be complementary to the one described in [18] (hereafter referred to as MTsA), and aims at extending the range of analysis to strongly nonlinear and non-stationary time series. Detailed description of MTsA software and its applications can be found in [18,19,20,21]. Cosmogenic beryllium-7 time series measured at 28 different sites at ground level are analysed making use of the Empirical Mode Decomposition (EMD).…”

Section: Dataset and Methodologymentioning

confidence: 99%

tvf-EMD based time series analysis of 7Be sampled at the CTBTO-IMS network

Longo

Bianchi

Plastino

2019

Physica A: Statistical Mechanics and its Applications

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A methodology of adaptive time series analysis based on Empirical Mode Decomposition (EMD) has been employed to investigate 7 Be activity concentration variability, along with temperature. Analysed data were sampled at ground level by 28 different stations of the CTBTO-IMS network. The adaptive nature of the EMD algorithm allows it to deal with data that are both nonlinear and non-stationary, making no a priori assumptions on the expansion basis. Main purpose of the adopted methodology is to characterise the possible presence of a trend, occurrence of AM-FM modulation of relevant oscillatory modes, residuals distributions and outlier occurrence. Trend component is first estimated via simple EMD and removed. The recent time varying filter EMD (tvf-EMD) technique is then employed to extract local narrow band oscillatory modes from the data. To establish their relevance, a denoising step is then carried out, employing both the Hurst exponent as a thresholding parameter and further testing their statistical significance against white noise. The ones that pass the denoising step are considered to be meaningful oscillatory modes of the data, and their AM-FM modulation is investigated. Possible applications of the adopted methodology regarding site characterisation and suggestions for further research are given in the conclusions.

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Section: Dataset and Methodologymentioning

confidence: 99%

tvf-EMD based time series analysis of 7Be sampled at the CTBTO-IMS network

Longo

Bianchi

Plastino

2019

Physica A: Statistical Mechanics and its Applications

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“…In this paper, a methodology of adaptive time series analysis, based on the one described in [4], is tested on the Virgo [1] strain data in order to characterise sources of scattered light noise, which is a noise that can affect the sensitivity of the Virgo IFO in the gravitational wave detection frequency band, ranging from approximately 10 Hz to a few kHz [5]. Adaptive methodologies are widely used in many fields of science, for example in seismology [6,7,8], in speech pattern classification [9,10,11] and also in radionuclide time series analysis [12,13,14,15,16,17,18]. As described in [4], besides known features in the sensitivity curve, such as peaks due to mechanical resonances or main power harmonics, non-stationary noise can affect the sensitivity of gravitational wave interferometers, scattered light noise being an example.…”

Section: Introductionmentioning

confidence: 99%

Scattered light noise characterisation at the Virgo interferometer with tvf-EMD adaptive algorithm

Longo,

Bianchi,

Plastino

et al. 2020

Preprint

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A methodology of adaptive time series analysis, based on Empirical Mode Decomposition (EMD), and on its time varying version tvf-EMD has been applied to strain data from the gravitational wave interferometer (IFO) Virgo in order to characterise scattered light noise affecting the sensitivity of the IFO in the detection frequency band. Data taken both during hardware injections, when a part of the IFO is put in oscillation for detector characterisation purposes, and during periods of science mode, when the IFO is fully locked and data are used for the detection of gravitational waves, were analysed. The adaptive nature of the EMD and tvf-EMD algorithms allows them to deal with nonlinear non-stationary data and hence they are particularly suited to characterise scattered light noise which is an intrinsically nonlinear and non-stationary noise. Obtained results show that tvf-EMD algorithm allows to obtain more precise results compared to the EMD algorithm, yielding higher cross-correlation values with the auxiliary channels that are the culprits of scattered light noise.

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