TY - JOUR
T1 - Improved detection of transient events in wide area sky survey using convolutional neural networks
AU - Liu, Jing Jing
AU - Boongoen, Tossapon
AU - Iam-On, Natthakan
N1 - Funding Information:
This work is partly supported by the Newton STFC-NARIT project: Using astronomy surveys to train Thai researchers in Big Data analysis (ST/P005594/1). It is also delivered as the collaboration between Mae Fah Luang University, Aberystwyth University, and University of Sheffield. The authors would like to thank Dr James Mullaney for his support for data preparation and suggestion
Publisher Copyright:
© 2023
PY - 2023/2/4
Y1 - 2023/2/4
N2 - The aim of data science is to catch up with the data-intensive life style as well as the demand for decision support, which becomes common in various domains such as medical, education and other smart solutions. As such, high quality of data analysis is greatly desired for accurate and effective downstreaming exploitations. This is also true for the domain of astronomical survey like GOTO (Gravitational-wave Optical Transient Observer), where large amount of raw data has been collected daily. This is one of recognised projects that search for transient events with the new breed of optical survey telescopes that can detect the sky faster and deeper. This is accomplished by comparing the night-specific data with the reference such that new bright sources are obtained for further study. However, the huge size of data makes it difficult to sift by naked eyes, thus requiring an automated system. Yet, many conventional machine-learning models have been sub-optimal for this task, as true positives can hardly be recognised due to the nature of imbalance data. This motivates the exploration of convolutional neural networks or CNN for this binary classification problem. Based on existing technologies, the paper reports the original application of basic CNN model to a representative data, which has been designed and generated within the GOTO project. In addition to the improvement over those previous works, this empirical study also includes details of parameter analysis, which will be useful for practice and further investigation.
AB - The aim of data science is to catch up with the data-intensive life style as well as the demand for decision support, which becomes common in various domains such as medical, education and other smart solutions. As such, high quality of data analysis is greatly desired for accurate and effective downstreaming exploitations. This is also true for the domain of astronomical survey like GOTO (Gravitational-wave Optical Transient Observer), where large amount of raw data has been collected daily. This is one of recognised projects that search for transient events with the new breed of optical survey telescopes that can detect the sky faster and deeper. This is accomplished by comparing the night-specific data with the reference such that new bright sources are obtained for further study. However, the huge size of data makes it difficult to sift by naked eyes, thus requiring an automated system. Yet, many conventional machine-learning models have been sub-optimal for this task, as true positives can hardly be recognised due to the nature of imbalance data. This motivates the exploration of convolutional neural networks or CNN for this binary classification problem. Based on existing technologies, the paper reports the original application of basic CNN model to a representative data, which has been designed and generated within the GOTO project. In addition to the improvement over those previous works, this empirical study also includes details of parameter analysis, which will be useful for practice and further investigation.
KW - Astronomy
KW - CNN
KW - Image classification
KW - Sky survey
KW - Transient events
UR - http://www.scopus.com/inward/record.url?scp=85148721717&partnerID=8YFLogxK
U2 - 10.1016/j.dim.2023.100035
DO - 10.1016/j.dim.2023.100035
M3 - Article
AN - SCOPUS:85148721717
JO - Data and Information Management
JF - Data and Information Management
M1 - 100035
ER -