Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/68078
Title: Classifying Maser features with Fortran and shell script for proper motion study of Water masers in W49N
Authors: M. Phetra
K. Asanok
T. Hirota
B. H. Kramer
K. Sugiyama
W. Nuntiyakul
Authors: M. Phetra
K. Asanok
T. Hirota
B. H. Kramer
K. Sugiyama
W. Nuntiyakul
Keywords: Physics and Astronomy
Issue Date: 16-Dec-2019
Abstract: © Published under licence by IOP Publishing Ltd. The 22-GHz Water masers in the W49N star-forming region are one of the richest and most luminous sources in our Galaxy, at the distance of 11.11 kpc. Very Long Baseline Interferometric (VLBI) observations allow us to study both physics of these masers and the star formation process. However, observational results often show numerous (up to several thousand) maser spots, making manual data analysis time-consuming. Therefore, we have developed a new software with simple computer codes to classify maser features in this source observed with KaVA by Asanok et al. (in preparation). Fortran computer language and bash shell script are developed and tested by applying to 3 epochs of data sets. The peak flux and relative position of maser spots are measured using AIPS software. The analysis procedures are as follows: (1) the data are extracted to the package in table format (peak flux and x-y relative positions), (2) maser spots are counted under specified rules, (3) the maser feature is calculated by using flux-weighted mean method, and (4) the output results are compared with those obtained from the conventional method. Preliminary results show that these newly developed codes are able to automatically identify up to 30% of those maser features found by the conventional method in a consistent manner. Therefore, further improvement and development are needed for future application.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85077815933&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/68078
ISSN: 17426596
17426588
Appears in Collections:CMUL: Journal Articles

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