Median Filtering Algorithms for Multichannel Detectors and their influence on Thunderstorm Peaks in Cosmic Ray Data Armen Hovhannisyan Alikhanyan Physics.

Slides:



Advertisements
Similar presentations
December 2008NMDB Kiel midterm Meeting1 Checking the simultaneous profiles on 1min data from Lomnický štít during strong CR variations R. Langer, K. Kudela.
Advertisements

Investigation of daily variations of cosmic ray fluxes in the beginning of 24 th solar activity cycle Ashot Chilingarian, Bagrat Mailyan IHY-ISWI Regional.
Cosmic Ray Using for Monitoring and Forecasting Dangerous Solar Flare Events Lev I. Dorman (1, 2) 1. Israel Cosmic Ray & Space Weather Center and Emilio.
NMDB Kiel Meeting, 3-5/12/2008 On the possibility to use on-line one-minute NM data of NMDB network and available from Internet satellite CR data for.
Optimizing Windows There are several ways to optimize (perform regular maintenance) Windows to keep it performing smoothly and quickly. Most of these discussed.
Petukhov I.S., Petukhov S.I. Yu.G. Shafer Institute for Cosmophysical Research and Aeronomy SB RAS 21st European Cosmic Ray Symposium in Košice, Slovakia.
The HAWC Detector ??? Type of connection Select which (of all 1,200) PMT is the source of the connection Select the destination of the.
Forecasting based on creeping trend with harmonic weights Creeping trend can be used if variable changes irregularly in time. We use OLS to estimate parameters.
GLAST LAT ProjectGLAST Flight Software IDT, October 16, 2001 JJRussell1 October 16, 2001 What’s Covered Activity –Monitoring FSW defines this as activity.
EFFICIENCY of the NEUTRON MONITOR NETWORK Functioning in the long term and real time mode E.Eroshenko, A. Belov, V. Yanke 1) Pushkov Institute of Terrestrial.
Math Calculus I Part 8 Power series, Taylor series.
R. Werner Solar Terrestrial Influences Institute - BAS Time Series Analysis by descriptive statistic.
From real-time to final data — Some thoughts on quality control Rolf Bütikofer, Erwin O. Flückiger University of Bern, Switzerland.
DEMONSTRATION FOR SIGMA DATA ACQUISITION MODULES Tempatron Ltd Data Measurements Division Darwin Close Reading RG2 0TB UK T : +44 (0) F :
A modern NM registration system capable of sending data to the NMDB Helen Mavromichalaki - Christos Sarlanis NKUA TEAM National & Kapodistrian University.
Yuan March 10, 2010 Automatic Solar Filaments Segmentation.
Leroy Nicolas, HESS Calibration results, 28 th ICRC Tsukuba Japan, August Calibration results of the first two H·E·S·S· telescopes Nicolas Leroy.
Neutron Monitor Splinter Neutron Monitor Data and the ESA SSA Program European Neutron Monitor Service ATHENS NEUTRON MONITOR STATION Nuclear & Particle.
Ground Level Enhancement of May 17, 2012 Observed at South Pole SH21A-2183 Takao Kuwabara 1,3 ; John Bieber 1 ; John Clem 1,3 ; Paul Evenson 1,3 ; Tom.
Lightning Detection at Aragats Station
Size and Energy Spectra of incident cosmic radiation obtained by the MAKET - ANI surface array on mountain Aragats. (Final results from MAKET-ANI detector)‏
Snow effect and practical questions of how to take it into account Korotkov V.*, Berkova M.*, Basalayev M.*, Belov A.*, Eroshenko E.*, Yudachin K.* and.
Ground level enhancement of the solar cosmic rays on January 20, A.V. Belov (a), E.A. Eroshenko (a), H. Mavromichalaki (b), C. Plainaki(b), V.G.
© 2012 IBM Corporation Rational Insight | Back to Basis Series Chao Zhang Code Review.
1 xCAL monitoring Yu. Guz, IHEP, Protvino I.Machikhiliyan, ITEP, Moscow.
The short particle bursts during thunderstorms: EAS or lightning seeds? G. Hovsepyan, A. Chilingarian, A. Alikhanian National Laboratory, Armenia.
1 IGY The ALERT signal of ground level enhancements of solar cosmic rays: physics basis, the ways of realization and development Anashin V., Belov A.,
Teaching and Learning with the Cosmic Ray e-Lab
Spectra of the Thunderstorm Correlated Electron and Gamma-Ray Measured at Aragats Bagrat Mailyan and Ashot Chilingarian.
Operation of the Space Environmental viewing and Analysis Network (Sevan) in 24-th Solar Activity Cycle A. Chilingarian A. Chilingarian Yerevan Physics.
Task 2.6. Products and services. Sub-tasks Definition and design of added value products Definition and design of added value products
DVIN Data Visualization Interactive Network. CRD Network.
On the origin of the huge natural electron accelerators operated in the thunderclouds Ashot Chilingarian Artem Alikhanyan National Laboratory (Yerevan.
Calibration instructions for Quarknet Cosmic-Ray Detector How to Plateau the Counters A friendly guide for students and teachers Edited by Jeremy Paschke,
Michele de Gruttola 2008 Report: Online to Offline tool for non event data data transferring using database.
Japan, ICRC 2003 Daejeon, UN/ESA/NASA/JAXA Workshop, Sept 2009 Satellite Anomalies and Space Weather By Lev Dorman for INTAS team (A. Belov, L. Dorman,,
Geophysical Research Network operating in the Aragats Space Environmental Center A. Chilingaryan 1, K. Arakelyan 1, S. Chilingarian 2, A. Reymers 1 1 A.
EXAMPLE 1 Apply the distributive property
Athens University – Faculty of Physics Section of Nuclear and Particle Physics Athens Neutron Monitor Station Study of the ground level enhancement of.
Using Kalman Filter to Track Particles Saša Fratina advisor: Samo Korpar
EQUILIBRIUM CONSTANTS. As its name suggests, “equilibrium” is a balance – a balance between the forward and reverse rates of a chemical reaction. But.
Space Environmental Viewing and Analysis Network (SEVAN) Chilingarian 1, Ch.Angelov 2, K. Arakelyan 1, T.Arsov 2, Avakyan 1, S. Chilingaryan 1,4, K. A.Hovhanissyan.
1 TEMPERATURE EFFECT OF MUON COMPONENT AND PRACTICAL QUESTIONS OF ITS ACCOUNT IN REAL TIME Berkova 1,2 M., Belov 1 A., Eroshenko 1 E., Yanke 1 V. 1 Institute.
PHY 202 (Blum)1 More basic electricity Non-Ideal meters, Kirchhoff’s rules, Power, Power supplies.
MultiModality Registration Using Hilbert-Schmidt Estimators By: Srinivas Peddi Computer Integrated Surgery II April 6 th, 2001.
Some results of observation of ultraviolet and infrared emission from lightning discharges at Aragats Cosmic Station LEAD workshop Armenia, NOR AMBERD.
Course 5 Edge Detection. Image Features: local, meaningful, detectable parts of an image. edge corner texture … Edges: Edges points, or simply edges,
Online Consumers produce histograms (from a limited sample of events) which provide information about the status of the different sub-detectors. The DQM.
The Gulmarg Neutron Monitor Ramesh Koul Astrophysical Sciences Division Bhabha Atomic Research Centre Mumbai
Registration of the KeV and MeV neutron burst associated with thunderstorms or lightning Ashot Chilingaryan and Nikolay Bostanjyan A. Alikhanyan National.
09/06/06Predrag Krstonosic - CALOR061 Particle flow performance and detector optimization.
M. Martemianov, ITEP, October 2003 Analysis of ratio BR(K     0 )/BR(K    ) M. Martemianov V. Kulikov Motivation Selection and cuts Trigger efficiency.
Data Analysis in the Energy Monitoring System Georgii Mikriukov 1 Perm National Research Polytechnic University Electrical.
ARENA08 Roma June 2008 Francesco Simeone (Francesco Simeone INFN Roma) Beam-forming and matched filter techniques.
10/20/11SESAPS111 Correlation study of atmospheric weather and cosmic ray flux variation Kanishka Dayananda Georgia State University.
CALORIMETER CELL MONITORING TOOL Viacheslav Shary.
Neutron emission in TGE’s L.Vanyan*, A.Chilingaryan, N.Bostanjyan, T.Karapetyan Yerevan Physics Institute.
Happy 2016!. Inverse polarity electric field at Aragats!
The Behavior of the Mean Multiplicity Coefficients of ASEC Neutron Monitors During Thunderstorm Activity Armen Hovhannisyan.
Calibration Instructions for Quarknet Cosmic Ray Detector
Early Alert of Solar Radiation Hazard
A.S. Lidvansky, M.N. Khaerdinov, N.S. Khaerdinov
Design of Digital Filter Bank and General Purpose Digital Shaper
OPSE 301: Lab13 Data Analysis – Fitting Data to Arbitrary Functions
3x 2x -5 x + 11 (4x + 7)° 90° (8x - 1)°.
Chapter 1: An Overview of Computers and Programming Languages
Chapter 2 Basic Models for the Location Problem
Chapter 6 Discrete-Time System
Bagrat Mailyan, Alikhanian National Laboratory (Former YerPhI)
FEMAS Development - Progress
Presentation transcript:

Median Filtering Algorithms for Multichannel Detectors and their influence on Thunderstorm Peaks in Cosmic Ray Data Armen Hovhannisyan Alikhanyan Physics Institute, Armenia

ASEC Monitors

Aragats Neutron Monitor Data

Different Kinds of Errors 1 Spike 2 Slow Drift 3 Abrupt change of mean

Algorithm 1 – Moving Median Filter(MMF) Moving window width – L; L=2I+1, where I is number of detections to the left and to the right of the filtering (smoothing) measurement; Maximal possible value – Pmax Minimal possible value – Pmin Maximal value of window width – Lmax Algorithm Description 1Select time series from database with N elements; 2Start smoothing from the (I+1)th measurement of time time series – Vi, i=I+1; 3Calculate the median value Mi,L; 4Validate the median value: Mi,L ɛ (Pmin;Pmax) if not, enlarge L by 2, and after checking L<=Lmax go to 2; 5ELSE go to STOP and report operator about data failure; 6Substitute selected measurment by median value Mi,L →Vi; 7Move to next i+1 element of time series; 9If i+1<=N THEN GO TO 2 10ELSE STOP, ask operator where to write smoothed time series.

Algorithm 2(3) – Median filter for multichannel measurements Let's suppose that we have M channels of one monitor, and several of them have been down for some period., where ni are mean values of each channel, Sum is the sum of means of all channels (1),where Med is median of all working channels at same minute, (2) Fi are coefficients of each channel. These coefficients are the relation of total median value of all channels for same minute and the i-th channel mean. For each minute of corrupted period of corrupted channel we calculate Med value(which is median of working channels), and then calculate value for that minute using equation (2).

Combination of 2 Algorithms We have some date to start the smoothing, for that beginning we calculate and coefficients and write them to a file Then we take 1 day data and start smoothing all channels with constant and not big I.e. (10 minute) window. If some channels have been not corrected by 1 algorithm, second one turns on, it reads the means and algorithms from files we have created After correcting with 2 algorithm, if everithing is ok, we calculate again means and coefficients for corrected data and write them to a the same file. If second algorithm didn't corrected the data (which means that all or nearly all channels have been corrupted) send an e- mail to operator. Take next Period and do 2-5 again.

Atmospheric Pressure, Nor-Amberd Station, Period – 6 Months

Atmospheric Pressure - Corrected

Aragats Neutron Monitor, MAY 2008 – after filtering

Correction of the AMMM time series, >5 Gev Muons

Correction of the ARNM time series

Simulation of Data For NANM

Simulation of Spoiled Timeserie

The Same Timeserie After Correction

CR Intensity Modulation during 23th solar cycle, Measured by NANM

Comparison of Corrected Data of NANM with Alma-Ata Data

Day-to-day changes of the channel coefficients of Aragats Neutron Monitor

Neutron Monitors used in the “coherence” test Neutron Monitor Altitude,mRigidity,GVType Alma Ata NM64 Rome NM64 Aragats NM64 Nor-Amberd NM64 Moscow NM64 Oulu00.819NM64 Athens NM64

Pressure Corrected Data from NMDB

Pressure Corrected and Median Smoothed Data from NMDB

Equalizing coefficients of the NMDB facilities

Enhancements associated with thunderstorms

Types of Algorithms Algorithm1 – Spike Removal Algorithm1 – Spike Removal Algorithm2 – Correction of EACH channel by coefficients Algorithm2 – Correction of EACH channel by coefficients Algorithm3 – Getting WEGHTED TimeSerie using data of all channels and coefficients Algorithm3 – Getting WEGHTED TimeSerie using data of all channels and coefficients

Filtering with Algorithm 1 and 2 Window=60 minutes

Filtering with Algorithm 1 and 2 Window=20 minutes

Filtering with Algorithm 3

Filtering with Algorithm 1 and 2 Window=60 minutes

Filtering with Algorithm 3

Conclusions We use this method for online and offline filtering of ASEC data. The program is online filtering the data of Nor-Amberd and Aragats Neutron Monitors, and soon it’ll be implemented it to other monitors. Also the corrected data is being transferred to NMDB. Changing Parameters of different algorithms it is possible not to oversmooth enhancements associated with lightning events. Besides the filtering, this programme also provides an automatic alerting system in case of malfunctioning of some of 280 detecting channels.