Implementation of Frequency Drift for Identification of Solar Radio Burst Type II

Nur Zulaikha Mohd Afandi (1), Zamri Zainal Abidin (2), Roslan Umar (3), Nor Hazmin Sabri (4), Zainol Abidin Ibrahim (5), Christian Monstein (6)
(1) East Coast Environmental Research Institute, Universiti Sultan Zainal Abidin, 21300 Kuala Nerus, Terengganu, Malaysia
(2) Department of Physics, University of Malaya, 50603 Kuala Lumpur, Malaysia
(3) East Coast Environmental Research Institute, Universiti Sultan Zainal Abidin, 21300 Kuala Nerus, Terengganu, Malaysia
(4) School of Fundamental Science, Universiti Malaysia Terengganu, 21030 Terengganu, Malaysia
(5) Department of Physics, University of Malaya, 50603 Kuala Lumpur, Malaysia
(6) Institute for Astronomy, ETH Zurich, HIT J13.2, Wolfgang-Pauli-Strasse 27, CH-8093 Zurich, Switzerland
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How to cite (IJASEIT) :
Mohd Afandi, Nur Zulaikha, et al. “Implementation of Frequency Drift for Identification of Solar Radio Burst Type II”. International Journal on Advanced Science, Engineering and Information Technology, vol. 6, no. 5, Oct. 2016, pp. 775-80, doi:10.18517/ijaseit.6.5.1152.
Sun is constantly produced mass and radiation during its natural activities, which will interact with ionosphere and affect the earth weather. In radio astronomer community, CALLISTO is used to capture the radio signal comes from solar activities such as solar burst. Solar flares and Coronal Mass Ejections (CMEs) were closely associated with the production of solar radio burst Type II and III. However, the determination of solar burst existence is done manually using spectrograph which appears for every 15 minutes.  In order to assist the solar radio researcher to speed up the process of solar burst identification and detection, this work presents a new algorithm to auto classify solar radio burst Type II and III. The value of frequency drift was used as the main idea in this auto classify algorithm because it can easily implemented using MATLAB. There are three main steps involved named as pre-processing, identification and classification. Auto calculation of frequency drift burst on spectra was obtained from two parts which are frequency axis (df) and time axis (dt). The results of the frequency drift implementation in classification algorithm show that the algorithm developed gave almost similar determination as in manual detection. However, there are always have rooms for improvement for better detection system in future which may include specific characterization of bursts and improved noise elimination.

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