Estimating Variations in the Intensity of the Muon Flux, Based on a Time Series of Matrix Observations by the URAGAN Hodoscope

2021 ◽  
Vol 85 (5) ◽  
pp. 585-587
Author(s):  
V. E. Chinkin ◽  
V. G. Getmanov ◽  
A. D. Gvishiani ◽  
I. I. Yashin ◽  
A. A. Kovylyaeva
Keyword(s):  
Time Series ◽  
Muon Flux

A method for recognizing the muon flux intensity modulations using the normalized variation functions for the URAGAN hodoscope matrix data

2021 ◽  
Author(s):  
Vladislav Chinkin ◽  
Viktor Getmanov ◽  
Roman Sidorov ◽  
Alexei Gvishiani ◽  
Mikhail Dobrovolsky ◽  
...  
Keyword(s):  
Time Series ◽  
Muon Flux ◽  
Time Interval ◽  
Two Dimensional ◽  
Recognition Method ◽  
Atmospheric Disturbances ◽  
Flux Intensity ◽  
Two Dimensional Filtering ◽  
Muon Hodoscope ◽  
Hardware Function

<p>Muon flux intensity modulation (MFIM) recognition is a relevant solar-terrestrial physics problem. The considered MFIM, recorded on the Earth's surface, are caused by extreme heliospheric events – the geoeffective solar coronal mass ejections.</p><p>The URAGAN muon hodoscope (MH), developed by NRNU MEPhI, a computerized device that measures the intensities of muon fluxes, is used. In the MH, the number of muons falling per unit time on the MH aperture is calculated for the selected system of zenith and azimuthal angles. MH matrix data time series are formed. In the MH data, there are angular modulations due to the action of the hardware function HF, temporal modulations due to atmospheric disturbances and noise: the values of these modulations significantly exceed the values of MFIM of cosmic origin. This circumstance prevents effective MFIM recognition.</p><p>A method for MFIM recognition is proposed, based on the mathematical apparatus of the introduced normalized variation functions for MH matrix data, and focused on overcoming the noted circumstance.</p><p>A two-dimensional normalized HF is defined for MH. A quite realistic hypothesis is accepted about the initialiy uniform muon flux intensity distributions on a small reference time interval, where there are no extreme heliospheric events and the corresponding reference MH data do not contain significant MFIMs. The estimation of the two-dimensional normalized HF is carried out on the basis of a multiparameter model and its optimization fit to the reference MH data. In order to reduce noise errors, the estimate of the two-dimensional normalized HF is subjected to two-dimensional filtering and subsequent threshold filtering.</p><p>Two-dimensional functions of variations of matrix MH datas with respect to two-dimensional normalized AF are calculated. The normalized variation functions are calculated by dividing the two-dimensional functions of variations of matrix MH data by the two-dimensional normalized HF. MFIM recognition method was tested on model and experimental MH data.</p><p>A time series of model matrix MH data containing model MFIM was generated. Testing led to a conclusion that it is possible to recognize MFIM with decreases of about 2-3%. A time series of experimental matrix MH data was generated, in which the model MFIM-containing areas were made. Testing led to a conclusion that it is possible to recognize MFIM with the magnitudes of the decreases almost commensurate with the decreases for the case of model MH data.</p><p>The proposed MFIM recognition method based on the normalized variation functions for matrix MH data has a favorable perspective for its application in solving problems of geomagnetic storm early diagnostics.</p>

A method for muon flux intensity modulations recognition using the indicator matrices for the URAGAN hodoscope matrix data

2021 ◽  
Author(s):  
Roman Sidorov ◽  
Victor Getmanov ◽  
Vladislav Chinkin ◽  
Alexei Gvishiani ◽  
Michael Dobrovolsky ◽  
...  
Keyword(s):  
Time Series ◽  
Confidence Intervals ◽  
Geomagnetic Storms ◽  
Muon Flux ◽  
Observation Data ◽  
Recognition Method ◽  
Current Time ◽  
Temporal Filtering ◽  
Flux Intensity ◽  
Recognition Errors

<p>Muon flux intensity modulation (MFIM) recognition is a relevant solar-terrestrial physics problem. The MFIM discussed are due to geoeffective solar coronal mass ejections.</p><p>The necessary observations are carried out using a computerized muon hodoscope (MH) URAGAN developed by NRNU MEPhI, registering muon fluxes intensity. In the MH, the number of muons falling on its aperture per unit time is counted. MH matrix data time series are formed, in which angular and temporal modulations take place due to MH design features, athmospheric disturbances and noises, the values of which significantly exceed the MFIM values.</p><p>The MFIM recognition method based on the mathematical apparatus of indicator matrices (IM) and spatial-temporal filtering is proposed.</p><p>The time series of MH matrix data, consisting of a set of Poisson processes corresponding to azimuthal and zenithal elements of MH matrices, are considered.</p><p>A reference time span is assigned where MFIM are known to be missing. For it, matrices of estimates of mathematical expectations are calculated and, taking into account the Poisson property, the matrices of reference confidence intervals are calculated. Next, the current time sections are formed, on which the matrices of the current confidence intervals are calculated. Based on the comparison of the matrices of the reference and current confidence intervals, the current matrices of anomalies are formed, which are compared with the specified threshold matrix. Thresholds exceedings correspond to anomalous events. Binary IM are formed: ones correspond to anomalous events, zeros correspond to the absence of anomalies. Recognition is to analyze IM sequence and identify areas of non-zero elements condensation that lead to the conclusion that there are significant MFIM. To reduce the recognition errors, the space-time IM filtering has been developed.</p><p>MFIM recognition technique, based on the use of IM time series with spatial-temporal filtering has been tested on model and experimental MH data.</p><p>Testing on the generated time series of model Poisson MH matrix data with model MFIM confirmed the conclusion about the possibility of MFIM recognition by the proposed method with a decrease level of 3-4%. Application of spatial-temporal filtering made it possible to recognize MFIM with  decreases with a level half as much.</p><p>Testing on the formed experimental matrix MH data time series with model MFIM led to a conclusion that it is possible to recognize MFIM with the magnitudes of decreases almost commensurate with the decreases for the case of model MH data.</p><p>The proposed MFIM recognition method based on indicator matrices for MH observation data allows optimization of parameters and can be successfully applied to solve problems of MFIM recognition and early diagnostics of geomagnetic storms.</p>

The LDE-Type Flare Occurrence (1969-1993)

1994 ◽  
Vol 144 ◽  
pp. 279-282
Author(s):  
A. Antalová
Keyword(s):  
Time Series ◽  
Fourier Analysis ◽  
Sunspot Cycle ◽  
List Type ◽  
Type Number ◽  
Solar Cycles ◽  
Type Iv ◽  
Long Duration ◽  
Cycle Maximum ◽  
Flare Index

AbstractThe occurrence of LDE-type flares in the last three cycles has been investigated. The Fourier analysis spectrum was calculated for the time series of the LDE-type flare occurrence during the 20-th, the 21-st and the rising part of the 22-nd cycle. LDE-type flares (Long Duration Events in SXR) are associated with the interplanetary protons (SEP and STIP as well), energized coronal archs and radio type IV emission. Generally, in all the cycles considered, LDE-type flares mainly originated during a 6-year interval of the respective cycle (2 years before and 4 years after the sunspot cycle maximum). The following significant periodicities were found:• in the 20-th cycle: 1.4, 2.1, 2.9, 4.0, 10.7 and 54.2 of month,• in the 21-st cycle: 1.2, 1.6, 2.8, 4.9, 7.8 and 44.5 of month,• in the 22-nd cycle, till March 1992: 1.4, 1.8, 2.4, 7.2, 8.7, 11.8 and 29.1 of month,• in all interval (1969-1992):a)the longer periodicities: 232.1, 121.1 (the dominant at 10.1 of year), 80.7, 61.9 and 25.6 of month,b)the shorter periodicities: 4.7, 5.0, 6.8, 7.9, 9.1, 15.8 and 20.4 of month.Fourier analysis of the LDE-type flare index (FI) yields significant peaks at 2.3 - 2.9 months and 4.2 - 4.9 months. These short periodicities correspond remarkably in the all three last solar cycles. The larger periodicities are different in respective cycles.

Automatic Identification of Autoregressive Integrated Moving Average Time Series

1982 ◽  
Vol 14 (3) ◽  
pp. 156-166 ◽  
Author(s):  
Chin-Sheng Alan Kang ◽  
David D. Bedworth ◽  
Dwayne A. Rollier
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Automatic Identification ◽  
Autoregressive Integrated Moving Average

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen
Keyword(s):  
Time Series ◽  
Type I Error ◽  
Electrodermal Activity ◽  
Moving Average ◽  
Type I ◽  
Facial Electromyography ◽  
Response Characteristics ◽  
Smoothing Methods ◽  
Response Systems ◽  
Different Response

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.

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