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 GRAPES-3 July 13, 2017 1 / 22

Outline Introduction 1 Atmospheric Effects on Particle Density 2 Atmospheric Effects on Trigger Rate 3 Summary 4 Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 2 / 22

GRAPES-3 EAS Array Located at Ooty, India (11.4 ◦ N, 76.7 ◦ E and 2.2 km m.s.l.) 400 plastic scintillation detectors (1 m 2 each) covers an area of 25,000 m 2 3712 proportional counters form a tracking muon telescope of area 560 m 2 Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 3 / 22

GRAPES-3 Shower Trigger 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

Motivation 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 observed due to atmospheric effects. These corrections are important for more accurate measurement of shower parameters. Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 5 / 22

Atmospheric Parameters The secondary cosmic ray flux can be influenced by the atmospheric parameters such as: Atmospheric Pressure 1 Temperature 2 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

Atmospheric Effect on Particle Density Year 2014 EAS data was used for this study. Total particles passing through each detector was obtained on hourly basis. Det. 286 10 (%) 0 UC I/I) ∆ ( − 10 − 20 0 50 100 150 Time (hours) Particle density variation from 1 Jan 2014 to 7 Jan 2014 Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 7 / 22

Fast Fourier Transform (FFT) 25 Det. 286 20 Amplitude 15 10 5 0 1 2 3 4 5 Cycles/Day Power spectrum of particle density Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 8 / 22

Filter  1 , if | f − f c | ≤ ∆ f   | f − f c | W ( f ) ≡ sin π if ∆ f < | f − f c | ≤ 2∆ f ∆ f , 2  0 , if | f − f c | < 2∆ f  Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 9 / 22

Fast Fourier Transform (FFT) Temperature Dependence of Particle Density 25 1.5 Det. 286 20 1 Amplitude Amplitude 15 10 0.5 5 0 0 1 2 3 4 5 1 2 3 4 5 Cycles/Day Cycles/Day Power spectrum of temperature Power spectrum of particle density Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 10 / 22

Fast Fourier Transform (FFT) Temperature Dependence of Particle Density 25 Det. 286 1.5 20 1 Amplitude Amplitude 15 10 0.5 5 0 0 1 2 3 4 5 1 2 3 4 5 Cycles/Day Cycles/Day Power spectrum of temperature Power spectrum of particle density Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 10 / 22

Fast Fourier Transform (FFT) The Inverse FFT was obtained by using filter on 1 cycle/day. The particle density was found to have lag with the temperature data. Particle Density Det. 286 8 6 Temperature 6 4 4 Particle density (%) 2 2 C) o 0 0 T ( ∆ − 2 − 2 − 4 − 4 − 6 − 6 − 8 0 0 10 10 20 20 30 30 40 40 50 50 Hours Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 11 / 22

Fast Fourier Transform (FFT) 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. − 0.86 Det. 286 − 0.88 Correlation Coeff − 0.9 − 0.92 − 0.94 − 0.96 0 50 100 150 200 Lag (min) Lag of ∼ 80 min. at -0.96 Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 12 / 22

Fast Fourier Transform (FFT) Temperature Coefficient Distribution For All Detectors 70 Mean -0.65 60 RMS 0.24 50 No of Detectors 40 30 20 10 0 − 2.5 − 2 − 1.5 − 1 − 0.5 0 0.5 1 Temperature Coefficient ( β ) T Mean = (-0.65 ± 0.01)%/ ◦ C Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 13 / 22

Fast Fourier Transform (FFT) Temperature Corrected Particle Density 10 Det. 286 Det. 286 10 8 (%) Amplitude 6 0 TC I/I) ∆ 4 ( − 10 2 − 20 0 0 50 100 150 1 2 3 4 5 Hours Cycles/Day Time Variation Power Spectrum Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 14 / 22

Fast Fourier Transform (FFT) Pressure Dependence of Particle Density 1.2 10 Det. 286 1 8 0.8 Amplitude Amplitude 6 0.6 4 0.4 2 0.2 0 0 1 2 3 4 5 1 2 3 4 5 Cycles/Day Cycles/Day Power Spectrum of Pressure Power Spectrum of Particle Density Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 15 / 22

Fast Fourier Transform (FFT) Pressure Dependence of Particle Density 1.2 10 Det. 286 1 8 0.8 Amplitude Amplitude 6 0.6 4 0.4 2 0.2 0 0 1 2 3 4 5 1 2 3 4 5 Cycles/Day Cycles/Day Power Spectrum of Pressure Power Spectrum of Particle Density Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 15 / 22

Fast Fourier Transform (FFT) Pressure Coefficient Distribution For All Detectors 160 Mean -0.71 140 RMS 0.11 120 No of Detectors 100 80 60 40 20 0 − 1.4 − 1.2 − 1 − 0.8 − 0.6 − 0.4 − 0.2 0 Pressure Coefficient ( β ) P Mean = (-0.710 ± 0.001)%/hPa Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 16 / 22

After Correction of Atmospheric Effects We have performed the FFT on temperature and pressure corrected particle density data. Temperature and Pressure Corrected Particle Density Det. 286 20 15 Amplitude 10 5 0 1 2 3 4 5 Cycles/Day Power Spectrum Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 17 / 22

After Correction of Atmospheric Effects D# 286 (a) 10 (%) 0 UC I/I) ∆ ( − 10 − 20 0 50 100 150 (b) 10 (%) 0 PTC I/I) ∆ − 10 ( − 20 0 50 100 150 Time (Hours) 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

Atmospheric Effects on Trigger Rate Trigger rate also shows the similar variations. 32.5 (a) 32 Trigger Rate (Hz) 31.5 31 30.5 30 0 50 100 150 Hours 600 Trigger Rate Amplitude (Hz) (b) 500 400 300 200 100 0 1 2 3 4 5 Cycles/Day Trigger rate variation (a) and its power spectrum (b) Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 19 / 22

Atmospheric Effects on Trigger Rate 2.5 1 2 0.8 ± o ± ( -0.392 0.002 ) % / C ( -0.597 0.008 ) % /hPa 1.5 0.6 1 0.4 0.5 0.2 I/I) % I/I) % 0 0 ∆ ∆ ( − ( − 0.5 0.2 − − 1 0.4 − − 1.5 0.6 − − 2 0.8 − − 2.5 1 − − − − − − − − − 10 8 6 4 2 0 2 4 6 8 10 2 1.5 1 0.5 0 0.5 1 1.5 2 ∆ o ∆ T ( C) P (hPa) Trigger Rate Vs Temperature Trigger Rate Vs Pressure Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 20 / 22

Summary 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

Thanks Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 22 / 22

FFT Coefficient 10 2 Det. 286 Det. 286 8 1.5 6 1 o ± ± -1.254 0.005 % / C -0.796 0.005 % / hPa 4 0.5 2 I/I) % I/I) % 0 0 ∆ ∆ ( − ( 2 − 0.5 − 4 − 1 − 6 − 1.5 − 8 − − 10 2 − − − − − − − − 10 8 6 4 2 0 2 4 6 8 10 3 2 1 0 1 2 3 ∆ o ∆ T ( C) P (hPa) Particle Density Vs Temperature Particle Density Vs Pressure Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 22 / 22

Simulation We have generated the 2 13 ( = 8192) sine wave hourly samples with 1 cycle / day periodicity. After performing FFT on the data a well spreaded power spectrum has been observed. 2 1 Amplitude 0.9 1.5 0.8 1 0.7 0.5 Amplitude 0.6 0 0.5 0.4 0.5 − 0.3 1 − 0.2 1.5 − 0.1 3 10 × 2 0 − 0 100 200 300 400 500 600 700 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Time (Sec) Cycles/Day Time Variation Power Spectrum Meeran Zuberi (TIFR) Atmospheric Effects on GRAPES-3 July 13, 2017 22 / 22