Dependence of the GRAPES-3 EAS particle density and trigger rate on - - PowerPoint PPT Presentation
Dependence of the GRAPES-3 EAS particle density and trigger rate on atmospheric pressure and temperature Meeran Zuberi (on behalf of GRAPES-3 Collaboration) PoS(ICRC2017)302 ICRC 2017 July 13, 2017 Meeran Zuberi (TIFR) Atmospheric Effects on
Meeran Zuberi
(on behalf of GRAPES-3 Collaboration) PoS(ICRC2017)302 ICRC 2017
July 13, 2017
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 1 / 22
1
Introduction
2
Atmospheric Effects on Particle Density
3
Atmospheric Effects on Trigger Rate
4
Summary
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 2 / 22
Located at Ooty, India (11.4◦ N, 76.7◦ E and 2.2 km m.s.l.) 400 plastic scintillation detectors (1 m2 each) covers an area of 25,000 m2 3712 proportional counters form a tracking muon telescope of area 560 m2
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 3 / 22
Two level trigger system : Level 0 : A three line coincidence used to generate this trigger. Level 1 : Minimum 10 detectors should trigger out of all detectors. Trigger Rate : 42 Hz (3.6 million EAS/day) Energy Range : 10 TeV-10 PeV
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 4 / 22
The scintillation detectors measure particle densities and relative arrival times of secondaries. These informations are used to reconstruct the shower size, core location and arrival direction. The periodic variations in both particle density and trigger rate were
These corrections are important for more accurate measurement of shower parameters.
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 5 / 22
The secondary cosmic ray flux can be influenced by the atmospheric parameters such as:
1
Atmospheric Pressure
2
Temperature
At Ooty, pressure (12 hrs) and temperature (24 hrs) display periodic behavior.
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 6 / 22
Year 2014 EAS data was used for this study. Total particles passing through each detector was obtained on hourly basis.
Time (hours) 50 100 150
(%)
UC
I/I) ∆ (
20 − 10 − 10
Particle density variation from 1 Jan 2014 to 7 Jan 2014
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 7 / 22
Cycles/Day
1 2 3 4 5
Amplitude
5 10 15 20 25
Power spectrum of particle density
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 8 / 22
W(f) ≡ 1, if |f − fc| ≤ ∆f sin π
2 |f −fc| ∆f ,
if ∆f < |f − fc| ≤ 2∆f 0, if |f − fc| < 2∆f
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 9 / 22
Temperature Dependence of Particle Density
Cycles/Day
1 2 3 4 5
Amplitude
0.5 1 1.5
Power spectrum of temperature
Cycles/Day
1 2 3 4 5
Amplitude
5 10 15 20 25
Power spectrum of particle density
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 10 / 22
Temperature Dependence of Particle Density
Cycles/Day
1 2 3 4 5
Amplitude
0.5 1 1.5
Power spectrum of temperature
Cycles/Day
1 2 3 4 5
Amplitude
5 10 15 20 25
Power spectrum of particle density
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 10 / 22
The Inverse FFT was obtained by using filter on 1 cycle/day. The particle density was found to have lag with the temperature data.
10 20 30 40 50 Particle density (%) 8 − 6 − 4 − 2 − 2 4 6 8 Hours 10 20 30 40 50 C)
∆ 6 − 4 − 2 − 2 4 6
Particle Density Temperature
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 11 / 22
The temperature data was shifted by ±3 hours in the interval of 5 min w.r.t. particle density data. Lag for each detector was calculated at maximum correlation coefficient.
Lag (min)
50 100 150 200
Correlation Coeff
0.96 − 0.94 − 0.92 − 0.9 − 0.88 − 0.86 −
Lag of ∼80 min. at -0.96
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 12 / 22
Temperature Coefficient Distribution For All Detectors
)
T
β Temperature Coefficient ( 2.5 − 2 − 1.5 − 1 − 0.5 − 0.5 1 No of Detectors 10 20 30 40 50 60 70
Mean -0.65
RMS 0.24
Mean = (-0.65 ± 0.01)%/◦C
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 13 / 22
Temperature Corrected Particle Density
Hours
50 100 150
(%)
TC
I/I) ∆ (
20 − 10 − 10
Time Variation
Cycles/Day
1 2 3 4 5
Amplitude
2 4 6 8 10
Power Spectrum
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 14 / 22
Pressure Dependence of Particle Density
Cycles/Day
1 2 3 4 5
Amplitude
0.2 0.4 0.6 0.8 1 1.2
Power Spectrum of Pressure
Cycles/Day
1 2 3 4 5
Amplitude
2 4 6 8 10
Power Spectrum of Particle Density
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 15 / 22
Pressure Dependence of Particle Density
Cycles/Day
1 2 3 4 5
Amplitude
0.2 0.4 0.6 0.8 1 1.2
Power Spectrum of Pressure
Cycles/Day
1 2 3 4 5
Amplitude
2 4 6 8 10
Power Spectrum of Particle Density
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 15 / 22
Pressure Coefficient Distribution For All Detectors
)
P
β Pressure Coefficient (
1.4 − 1.2 − 1 − 0.8 − 0.6 − 0.4 − 0.2 −
No of Detectors
20 40 60 80 100 120 140 160
Mean -0.71 RMS
0.11
Mean = (-0.710 ± 0.001)%/hPa
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 16 / 22
We have performed the FFT on temperature and pressure corrected particle density data. Temperature and Pressure Corrected Particle Density
Cycles/Day
1 2 3 4 5
Amplitude
5 10 15 20
Power Spectrum
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 17 / 22
50 100 150
(%)
UC
I/I) ∆ (
20 − 10 − 10
D# 286
(a)
Time (Hours) 50 100 150
(%)
PTC
I/I) ∆ (
20 − 10 − 10
(b)
Particle density (%) variation before (a) and after correction (b) of atmospheric effects
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 18 / 22
Trigger rate also shows the similar variations.
Hours 50 100 150 Trigger Rate (Hz) 30 30.5 31 31.5 32 32.5
(a)
Cycles/Day 1 2 3 4 5 Trigger Rate Amplitude (Hz) 100 200 300 400 500 600
(b)
Trigger rate variation (a) and its power spectrum (b)
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 19 / 22
C)
∆
10 − 8 − 6 − 4 − 2 − 2 4 6 8 10
I/I) % ∆ (
2.5 − 2 − 1.5 − 1 − 0.5 − 0.5 1 1.5 2 2.5
C
± ( -0.392
Trigger Rate Vs Temperature
P (hPa) ∆
2 − 1.5 − 1 − 0.5 − 0.5 1 1.5 2
I/I) % ∆ (
1 − 0.8 − 0.6 − 0.4 − 0.2 − 0.2 0.4 0.6 0.8 1
0.008 ) % /hPa ± ( -0.597
Trigger Rate Vs Pressure
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 20 / 22
The measured particle density shows the anti-correlation with atmospheric pressure and temperature. We have corrected the atmospheric effects from particle density by using FFT method. The similar effects have been observed in trigger rate for which temperature and pressure coefficient values have been determined by using FFT method. The correction of atmospheric effects from particle density can be used to calculate the hourly detector’s gain, which will improve the accuracy in the measurement of shower parameters.
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 21 / 22
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 22 / 22
C)
∆
10 − 8 − 6 − 4 − 2 − 2 4 6 8 10
I/I) % ∆ (
10 − 8 − 6 − 4 − 2 − 2 4 6 8 10
C
±
Particle Density Vs Temperature
P (hPa) ∆
3 − 2 − 1 − 1 2 3
I/I) % ∆ (
2 − 1.5 − 1 − 0.5 − 0.5 1 1.5 2
0.005 % / hPa ±
Particle Density Vs Pressure
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 22 / 22
We have generated the 213 ( = 8192) sine wave hourly samples with 1 cycle / day periodicity. After performing FFT on the data a well spreaded power spectrum has been observed.
Time (Sec)
100 200 300 400 500 600 700
310 ×
Amplitude
2 − 1.5 − 1 − 0.5 − 0.5 1 1.5 2
Time Variation
Cycles/Day 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
Amplitude
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Power Spectrum
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 22 / 22
C
5 10 15 20 25 Pressure (hPa) 774 776 778 780 782 784 786
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 22 / 22
Apart from pressure (2 cycle/day) & temperature (1 cycle/day) dominant cycles we can see other periodicity also in their power spectrums
Cycles/Day
1 2 3 4 5 6 7 8
Amplitude
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
Power Spectrum of Pressure
Cycles/Day
1 2 3 4 5 6 7 8
Amplitude
0.5 1 1.5 2 2.5 3 3.5 4
Power Spectrum of Temperature
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 22 / 22
The regression equation for two independent variable can be written as : Particle Density = a + b1 * Temperature + b2 * Pressure
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 22 / 22
6 months of air shower data (Jan to Jun, 2014) have been used for this study (same as FFT). Lag between particle density and temperature data has been calculated for all the detectors We have shifted the temperature data according to their lag for each detector.
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 22 / 22
Temperature Coefficient Distribution For All Detectors
Mean 0.8 − RMS 0.4778 Constant 6.15 ± 80.27 Mean 0.0225 ± 0.7308 − Sigma 0.0239 ± 0.3274
Temperature Coefficient
5 − 4 − 3 − 2 − 1 − 1 2 3 4 5
No of Detectors
10 20 30 40 50 60 70 80 Mean 0.8 − RMS 0.4778 Constant 6.15 ± 80.27 Mean 0.0225 ± 0.7308 − Sigma 0.0239 ± 0.3274
Mean = (-0.73 ± 0.02)%/◦C
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 22 / 22
Pressure Coefficient Distribution For All Detectors
Pressure Coefficient 2 − 1.5 − 1 − 0.5 − 0.5 1 1.5 2 No of Detectors 10 20 30 40 50 60
Pres_Coeff
Mean = (-0.54 ± 0.02)%/hPa
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 22 / 22
Temperature and Pressure Corrected Particle Density
Time (Sec)
100 200 300 400 500 600 700
310 ×
Particle Density
19000 20000 21000 22000 23000 24000 25000
Det#_2
Time variation
Cycles/Day 1 2 3 4 5 6 7 8 Amplitude 50 100 150 200 250 300 350 400 450 500
Det#_2
Power Spectrum
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 22 / 22
A close agreement has been found between the coefficient values from both the methods. % Pressure Coeff (/hPa) % Temperature Coeff(/◦C) FFT
Regression
Table : Coefficients comparison
Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 22 / 22