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Results of the 2006 Ship-Borne Neutron Monitor Survey

..

PRESENTED BY

WARAPORN NUNTIYAKUL

5238713 SCPY/D

Page 3

INTRODUCTIONWHAT IS A NEUTRON MONITOR?

neutron monitor is a ground-based detector designed to measure the number of high-energy charged particles striking the Earth's atmosphere from outer space.

Developed to

Standard NM64

IGY monitor

(International Geophysical Year)

Counting Rate Vs Time

Pressure Vs Time

FIG. 1 NM model

Page 4

NEUTRON MONITOR PRINCIPLE

BARE NMs

Fig. 2 Image Credit :

Paul Evenson, January2009

Fig. 3

Image Credit :PSNM

- - Detects secondary neutrons generated by collision of primary
- cosmic rays with air molecules.
- Detection Method:
- Older type-proportional counter filled with BF3
- n + 10B + 7Li
- Modern type-proportional counter filled with 3He
- n + 3He p + 3H

Page 5

- Rigidity is a concept used to determine the effect of particular magnetic fields on the motion of the charged particles.It is defined as
- R = Bρ = p/q
- WhereB is the magnetic field
- ρ is the gyroradius of the particle due to this field
- p is the particle momentum
- and qis its charge.
- Vertical Cutoff Rigidity is the cutoff for a vertically incident particle
- Apparent Cutoff Rigidity is the cutoff depends upon the details of
- cosmic ray penumbra in each possible direction of incidence.
- Solar modulation is the flux (flow rate) of cosmic rays incident on the Earth’s upper atmosphere is modulated (varied) by two processes; the sun’s solar wind and the Earth's magnetic field.
- Differential response is the differentiation the curve relating counting rate and
- cutoff rigidity

FIG. 4

9

SURVEY TECHNIQUE

To improve

NM response function

Describes the input-output relationship of a signal transducer such as a neuron turning synaptic input into a response.

- Dorman Function
- Nagashima et al (1989)

Page 6

Dorman Function:

1

FIG. 5-1

Spectral Crossover

FIG. 5-2 Sample fit of a segment’s data to a Dorman function, along with the corresponding derivative.

SURVEY TECHNIQUE

To improve

Cutoff Sky Map

2

geomagnetic cutoff model

- DGRF Magnetic Field Model : Definitive International Geomagnetic Reference FieldplusTsyganenko magnetosphere
- Calculate “Efficient Apparent”
- (sky average) Cutoffs

“A sky map is calculated by tracing trajectories of charged particles through the geomagnetic field to determine allowed and forbidden rigidities” [Lin et al., 1995].

Fig 6 Example of an effective cutoff rigidity sky map for 43.92 OS, 76.64 OW at 2330 UT on day 75 of 1995. Here the effective vertical cutoff is 8.23 GV and the apparent cutoff is 8.65 GV. (Clem et al., 1997)

Page 8

SCIENTIFIC BACKGROUND:

Nagashima Response Function

Transportable Monitor (not to scale)

U.S. Coast Guard icebreakers, the Polar Seaor the Polar Star

carry a Neutron monitor standard 3-NM64

geomagnetic Transmission

Pc

heliosphericModulation

GCR spectrum

Yield function

Counting Rate

STEP FUNCTION

Assuming L is a limiting rigidity, Tis a step function

T

Differential Response fn.

1

P

0

Page 9

DEVELOP OPTIMAL METHODS( ) FOR EXTRACTING COSMIC RAY SPECTRA FROM LATITUDE SURVEYS.

[Calculate integrals of the Nagashima Response Function using Simpson’s rule]

Nagashima method

Set and manage the parameters of the response function calculations. There are a total of 14 adjustable parameters. In this routine they are numbered 1-14 and dealt with systematically. The defaults are the values in the paper.

Galactic Cosmic Ray Spectrum

GCR Spectrum

The total energy per nucleon (assuming proton) in unit of

Proton mass = 0.93827231

Best parameters from the paper --- > 1 = 1.2 ×108, 2 = 0.0, 3 = 2.585

Page 10

Yield Function

- The total energy per nucleon (assuming proton) in unit of

Best parameters from the paper --- > = 0.0, 1 = 2.2, 2 = 1.62,

3 = 12.7, 4 = 0.50, 5 = 0.42

Modulation Function

Yield Function

Modulation Function

- This term is due to the energy dependence of the neutron production and expresses the h-dependence of Y in high-energy region

- This term expresses the decrease of the production mainly due to the decrease of the number of effective nucleons in the atmosphere with the increase of h and with the decrease of u

where 1(t) as a function of other parameters and variables so it must be recomputed from time to time by the functions that use the parameters

Best parameters from the paper --- > 2 = 0.097, 3 = 1.02, 4 = 1.15, 5 = 14.9, 6 = 1.12

Page 11

ANALYZE THE DATA FROM A SHIP-BORNE MONITOR WITH THREE COUNTER TUBES

Made trips across the Pacific ocean from Seattle to Antarctica and back, over a wide range of cutoff rigidities, over 1994 to 2007.

U.S. Coast Guard icebreakers

Fig. 7Route of Latitude surveys

Page 12

WHAT ARE “PITCH” AND “ROLL” MOTION?

Ship Motions:

Pitchis when the vessel rotates about the transverse (side-to-side axis)

Row is when the vessel rotates about the longitudinal (front/back axis)

Yaw is when the vessel rotates about the longitudinal (up/down axis)

Fig. 8 Ship motions

YAW

The Clinometer Assembly

ROLL

Side View

PITCH

Page 14

Result of Simulated Survey fitting Nagashima parameters

Square of the accumulated counting rate difference

Fig. 10 Data (box) and model fit (line) to the moderated neutron detector Latitude survey conducted under award OPP-0838838. Fit parameters were used follow Nagashima (1989) paper

Best parameters from

Nagashima et al., 1989

Definitions

1 = An overall normalization

and = The parameters describe the yield function.

Page 15

Result of Simulated Survey fitting my parameters

Quality = 1.8139E+01

Quality = 2.7561E+01

Best parameters in Nagashima et al., 1989

Fig. 11 Data (box) and model fit (line) to the moderated neutron detector Latitude survey.The parameters in the figure show the best fit in my research.

Best parameters in my research

Box symbol: we show the counting rate (counts/second) plotted against the apparent cutoff calculated at the center point of the averaging interval. Most of the systematic wandering results from variations in barometric-pressure.

Line symbol: we show the model achieved after several iterations of my fitting routine.

Page 16

Comparing with Nagashima parameters

▫Start: 320.88680 (Acut =1.89 GV)

End: 344.00625 (Acut = 15.78 GV)

▫ Start: 344.02742 (Acut=15.77 GV)

End: 416.70694 (Acut = 0.10 GV)

▫ Start: 416.728111 (Acut=0.14 GV)

End: 439.93090 (Acut= 15.85 GV)

▫ Start: 439.93090 (Acut=15.84 GV)

End: 458.77152 (Acut = 1.83 GV)

FORBUSH

DECREASE

Forbushdecreaseis a rapid decrease in the observed GCRs intensity following a CME. It occurs due to the magnetic field of the plasmasolar wind sweeping some of the GCRs away from Earth.

Comparing with my parameters

DOY 352

DOY 348

black bunches occurring due to the problem of barometric pressure.

Fig. 12 Residuals (counts/second) from the fit shown in Fig. 11 as a function of geomagnetic cutoff.

Page 17

Result of residuals as a function of time

Fig. 13 Residuals (counts/second) from the fit shown in Fig. 11 as a function of time.

Page 18

Result of residuals as a function of barometric-pressure

Fig. 14 Residuals (counts/second) from the fit shown in Fig. 11 as a function of barometric-pressure (mmHg)

Page 19

Result of residuals as a function of modulation level

Mt Washington max =2465 counts/hour

McMurdo max =10600 counts/hour

FORBUSH DECREASE

Fig. 15 Residuals (counts/second) from the fit shown in Fig. 11 as a function of modulation level.

Page 20

Result of cosmic ray spectra

As a function of rigidity, depth (pressure), n

Atmospheric depth in atmospheres

(1.000=1033 g/cm2 = 760 mmHg

Differential Response Function

Differential Response Function

300 (g/cm2)

300 (g/cm2)

500 (g/cm2)

500 (g/cm2)

700 (g/cm2)

700 (g/cm2)

1033 (g/cm2)

1033 (g/cm2)

(a) (b)

Fig. 16 Differential response function of neutron monitor for several atmospheric depths in the periods of the maximum and minimum solar activities as derived by Nagashima et al. : (a) analyze by using the best parameters from paper and (b) analyze by using the best parameters from my research. Figures attached to each curve express the atmospheric depth (h g/cm2). Figure (above dots) is the minimum solar activity (JMtW=2465), Figure(below dots) is the maximum solar activity (JMtW=1990).

Page 21

CONCLUSION

The ultimate goal is to go beyond the Dorman functions to more physical response functions. At any rate in this work need to begin folding modulation levels into the analysis.

In my research is successful in determining the value of 14 parameters which it provides the simulation model better than Nagashima. As we vary parameters by using Simpson’s rule until we got the best model. The number of Quality is even low, the fit is even better.

When residuals are plotted as a function of apparent cutoff rigidity, barometric-pressure, modulation level or time there is no structure apart from statistical scatter, the deviation from the model is clearly visible; systematic analysis of the data from our thirteen surveys with the same instrument on sister ships (and therefore a truly constant yield function) should clearly define the spectral dependence. In this analysis particular attention will be paid to the implications of composition on the results by explicitly allowing for composition variation in the model. When combined with the results from other proposed tasks, this work will enable us to refine knowledge of the yield function.

Page 22

ACKNOWLEDGEMENT

The Royal Golden Jubilee Ph.D Program

[โครงการปริญญาเอกกาญจนาภิเษก]

Prof. Paul Evenson

Prof. David Ruffolo

Assoc.Prof. John Clem

Dr. Alejandro Sáiz

Dr. Andrew Snodin

Dr. Takao Kuwabara

Page 23

REFERENCES

1. Moraal, H., Potgieter, M.S., Stoker, P.H., and van derWalt, A.J., “NM Latitude Survey of the Cosmic Ray Intensity During the 1986/87 Solar Minimum”, J. Geophys.

Res. 94, 1,459–1,464, 1989.

2. Bieber, J.W., Evenson, P.E., Humble J.E., and Duldig, M.., 1997, “Cosmic Ray Spectra Deduced from Neutron Monitor Surveys”, Proc. 25th Intl. Cosmic Ray

Conf. (Durban) 2, 45– 48, 1997.

3. Lockwood, J.A. and Webber, W.R., “Comparison of the rigidity dependence of the 11-year cosmic ray variation at the Earth in two solar cycles of opposite magnetic

polarity”, J. Geophys. Res., 101, 21573-21580, 1996.

4. Reinecke, J.P.L., Moraal, H., Potgieter, M.S., Mc- Donald, F.B., and Webber, W.R., “Different Crossovers”, Proc. 25th Int. Cosmic Ray Conf. (Durban) 2,

49–52, 1997.

5. Lin, Z., J.W. Bieber and P. Evenson, “Electron trajectories in a model magnetosphere: Simulation and observation under active conditions”, J. Geophys. Res., 100, 23,543-23,549, 1995.

Page 24

6. Flückiger, E.O. and Kobel, E., “Aspects of Combining Models of the Earth's Internal and External Magnetic Fields”, J. Geomag. GeoElec., 42, 1123-1128, 1990.

7. Clem, J.M., Bieber, J.W., Evenson, P., Hall, D., Humble, J.E., and Duldig, M., “Contribution of Obliquely Incident Particles to Neutron Monitor Counting Rate”,

J. Geophys. Res., 102, 26919, 1997.

8. Bieber, J.W., Clem, J., Duldig, M.L., Evenson, P., Humble, J.E. and Pyle, R., “A continuing yearly neutron monitor yearly survey: Preliminary results from

1994-2001”, Proc. 27th Intl. Cosmic Ray Conf.(Hamburg) 10, 4087-4090, 2001.

9. Smith, C. W., and Bieber, J. W., “Detection of Steady

Magnetic Helicity in Low-Frequency IMF Turbulence”, Proc. 23rd Internat. Cosmic Ray Conf. (Calgary), 3, 493-496, 1993.

10. Bieber, J.W. and Evenson, P., “Spaceship Earth — an Optimized Network of Neutron Monitors”, Proc. 24th Intl. Cosmic Ray Conf. (Rome) 4, 1078-1081, 1995.

Thank you for your attention

I welcome your questions, suggestions, comments!

Epigram for Today

Lie In Center Of Believe!

Page 27

Result of Nagashima 1989 latitude surveys

Fig. 16 Display Nagashima 1989 latitude surveys , the relationship between the simulated counting rate and cutoff rigidity (GV), at Solar maximum (above dots) and solar minimum (below dots)

Page 28

Spectrum Crossover

FIGURE Western Pacific Segments: using 1980 Cutoffs (Top) vs.Tsyganenko Cutoffs (Bottom)

Page 29

FIGURE Course plots for the 8 surveys used in this paper. Each is labeled at 5-day intervals by the start year of the survey (e.g. 7 for 1997/98). “Segment” codes are given in the inset. Selected vertical cutoff contours are labeled.

Page 31

Page 32

RESULTS & DISCUSSION

Analysis of the variation of relative count rate with pitch bin

Pitch Bins

BIN Mean Sigma SigmaMu Cases

-5 0.99 0.17 0.01 211.00

-4 1.00 0.14 0.01 378.00

-3 0.99 0.15 0.01 715.00

-2 1.00 0.14 0.00 1445.00

-1 1.00 0.08 0.00 3374.00

0 1.00 0.00 0.00 6476.00

1 1.01 0.09 0.00 3395.00

2 1.00 0.14 0.00 1514.00

3 1.00 0.16 0.01 815.00

4 1.00 0.15 0.01 468.00

5 1.00 0.20 0.01 312.00

Fig. 9 Show the example of variation of the relative count rate with pitch bin of ship of the data in year 2006. The black points (□)are the input data all of the data are just plotted “on top of” each other to be sure that there are no funny values (we usually call these “fliers”). The red points (o) are the average of all the black points. The blue points (o)show the deviation of the red points from 1.00 expanded by a factor of ten, with statistical error bars.

Page 33

Analysis of the variation of relative count rate with roll bin

Pitch Bins

BIN Mean Sigma SigmaMu Cases

-5 0.98 0.16 0.01 250.00

-4 0.98 0.20 0.01 464.00

-3 0.99 0.16 0.01 950.00

-2 0.99 0.13 0.00 1932.00

-1 1.00 0.08 0.00 3524.00

0 1.00 0.01 0.00 6476.00

1 1.00 0.07 0.00 3464.00

2 1.00 0.15 0.00 1983.00

3 1.01 0.19 0.01 970.00

4 1.02 0.18 0.01 488.00

5 1.00 0.15 0.01 277.00

Fig. 10 Show the example of variation of the relative count rate with roll bin of ship of the data in year 2006. The black points (□)are the input data all of the data are just plotted “on top of” each other to be sure that there are no funny values (we usually call these “fliers”). The red points (o)are the average of all the black points. The blue points (o)show the deviation of the red points from 1.00 expanded by a factor of ten, with statistical error bars.

Page 34

Analysis of the apparent cutoff

Fig. 11 Apparent cutoff calculated (GV) during the Seattle-McMurdo of the year 2006-2007 latitude survey compared with time. The missing data occurs during DOY 330 due to the problem of data recorder.

Page 35

Analysis of the barometric-pressure

Fig. 12 Show the barometric-pressures were read by digiquartz barometer in millimeters of mercury (mmHg) as a function of time (DOY2006).

Page 36

Analysis of the counting rate

Fig. 13 Show the averaged counting rate as a function of time (DOY2006).

Page 37

Analysis of the modulation level

Mt Washington max =2465 counts/hour

McMurdo max =10600 counts/hour

Fig. 14 Show the modulation level of McMurdo simulated Mt. Washington as a function of time (DOY2006).

Page 38

Page 39

Rigidity = pc/q determines particle trajectory in magnetic field

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