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 Magnetism, Ionosphere and Radio Wave Propagation RAS (IZMIRAN), 142190, Troitsk, Moscow region, Russia

Brief review:

1.Analysis of the NM data quality2.The reasons and ways of apparatus variation

selection3.Method of the ratios4.Algorithm of the primary processing data of

multi channel detector5. Editor allowing a definition of each channel

efficiency6. Examples of normal and bad NM operating

Why should we perform primary processing?

Data of cosmic ray ground based observations are used to get, as a final result, information on cosmic ray distribution in the near Earth space.The quality of primary data processing determines an amount and quality of the information about interplanetary space, the Earth magnetosphere and atmosphere - obtained from cosmic ray NM observations.

Long term instrument variationsData from Kiel and Moscow stations relatively to 1987 during the initial period of registration. Blue line- initial data from Kiel.

Kiel data are corrected (green and red curves) by the ratio method using data from Leeds, Moscow, Lindau and Apatity stations.

The primary processing includes:

Data verification, checking of their reliability, estimation of the instrument performance quality;

Searching and removing of instrument variations;

Correction for the meteorological effects; Estimation of statistical errors of data

processed; Data preparing in a standard form for their

exchange, publication, storage and further processing.The main attention – to a test of experimental data by a control of detector efficiency (with an accuracy of a whole detector)

Types of the Instrument Variations

1. Peaks 2. Jumps 3. Drifts

All types of the instrument variations can be treated as efficiency changes of particular channel or the entire detector.

Types of the instrument variations

The peak is a short and considerable change of the efficiency with quickly recovery to its initial value.

The jump is a sharp change of the efficiency for a rather long period.

The drift is a gradual and rather slow change of the efficiency.

The Instrument variations effect on the expected poison distribution. They may decrease or increase its dispersion or change a distribution shape. It is apparently impossible to determine precisely instrument variations and edit data correctly without simultaneous determination of their dispersion.

Example of different kind of the ‘instrumental’ variations

18 channels of the Aragats neutron monitor in July 2008

Hourly data from Apatity, Kiel, Moscow and Oulu NMs (08.2007)

Two peaks in OULU NM data in August 2007

Data from the same stations with removed peaks in OULU

Methods of data quality control

From the beginning the problem of the NM data quality control was attempted to be solved by periodical calibration with a neutron source.

Modern methods for internal control of data quality are based on dividing of the detector into several (>3) identical sections (the section ratio method) and comparing of their data with each other (A/B, A/C, B/C).

The best realization of the method is in providing a section channel from each counter

Principal of Detector efficiency

The detector efficiency - is a number on which the observed count rate N(t) should be divided to compensate the variations associated with changes of the instrument origin .

A/B=ef0A; B/C=ef0

B; C/A=ef0C

EF=(ef0A+ef0

B+ef0C)/3; Each ef is taken with the weight of

section count rate; Ideally EF=1; If one section is spoiled, the program

accounts its new ef1,, and resulted EF1will be different, and then N(t)=Ncur/EF1

If the ratios are inside of nϭ the simple sum is calculated. In the other case the failured section is excluded from the sum and result is normalized accounting a contribution of this section.

1 2 3 4 5 6

1 1 J12 J13 J14 J15 J16

2 J21 1 J23 J24 J25 J26

3 J31 J32 1 J34 J35 J36

4 J41 J42 J43 1 J45 J46

5 J51 J52 J53 J54 1 J56

6 J61 J62 J63 J64 J65 1

Efficiency of each counter via the ratios of CR variations (n2)

1) Each of 6 channel is analyzed;

2) If one counter is failure the others are used completely;

3) Statistical accuracy is defined by a number of good channels;

4) Editor is working stable when a number of right channels is more then half of total

If the trust interval is chosen as 3ϭ a number of points outside the trust interval for efficiency Is allowed to be during one month about (1-0.997)*24*744 = 50 for the NM with 24 channels.

Effect of the drift in one channel

Systematical drift is artificially introduced in the 3-rd channel (from 11UT by the end of month). Efficiency was changed on 10 %.

Editing data for the drift in a spoiled channel

Pink curve is the data with systematical drift in the 3-rd channel. Black curve-initial data with normal channels; Blue curve is data corrected for a drift in the 3-rd channel.

Example of many channel deflections

The channels 1, 9, 12, 15, 16 are wrong even visually

Efficiencies of the separate channels

Correction data with accounting the efficiencies

Data without channels 1, 9, 12, 15, 16. The effect of gradual changing of the efficiency in some channels still exist.

Outer control We know only one disadvantage of the multi-section method: it can not select efficiency changes of the detector as a whole, i.e. when the changes occur simultaneously in all channels. These variations are not always of instrument origin, they might be associated with some changes of the detector environment (for instance, a change of snow cover or building reconstruction). They make worse the data quality and cause the instrument variations. Therefore, together with methods of the internal control the methods based on comparing of data from different detectors should be developed.

Long term instrument variationsData from Kiel and Moscow stations relatively to 1987 during the initial period of registration. Blue line- initial data from Kiel.

Kiel data are corrected (green and red curves) by the ratio method using data from Leeds, Moscow, Lindau and Apatity stations.

Long term changes of the efficiencies

Instrument variations at two mountain stations

February 2003: Nor-Amberd (red), Aragats, Almaty, Nor-Amber corrected (blue).

February 2003: Rome, Athens, ESOI with a snow effect (blue), ESOI corrected(yellow).

ESOI station: winter and summer.

February 2007: AATB, Tbilisi, ESOI. Very large snow effect at ESOI NM.

Good operating ESOI NM during the summer period. August 2007: Hermanus,Athens, ESOI.

Different reasons of unstable operating

Mountain station JUNG (NM64) -blue. Snow effect in August 2007: Kiel, Lomnicky Stit, JUN1 (IGY NM-on the top of mountain.

June 1996: Moscow, Kerguelen (green), Kiel. Peaks and jumps in a behavior of KERG NM

ConclusionsThe necessity of data quality estimation and control is evident;

The method of ratios (or, efficiencies) is elaborated and treated with data from many stations for many years;

The software on the efficiencies calculation and data quality control is successfully implemented at some stations in real time mode;

References on a detailed description of the method and algorithm, as well as software, may be found by the address:

(http://cr0.izmiran.rssi.ru/LongTimeVarCR/LongTimeStab/main.htm) A.V.Belov, Ya.L.Blokh, E.G.Klepach, V.G.Yanke “Primary Processing of Cosmic Ray Station Data: Algorithm, Computer Program and Realization” сб. Космические лучи, N25, 113-134, М., Наука, 1988..

Address for software

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