Articles written in Journal of Astrophysics and Astronomy
Volume 44 All articles Published: 9 February 2023 Article ID 0011 TECHNICAL REVIEW
High-performance computing for SKA transient search: Use of FPGA-based accelerators
R. AAFREEN R. ABHISHEK B. AJITHKUMAR ARUNKUMAR M. VAIDYANATHAN INDRAJIT V. BARVE SAHANA BHATTRAMAKKI SHASHANK BHAT B. S. GIRISH ATUL GHALAME Y. GUPTA HARSHAL G. HAYATNAGARKAR P. A. KAMINI A. KARASTERGIOU L. LEVIN S. MADHAVI M. MEKHALA M. MICKALIGER5 V. MUGUNDHAN ARUN NAIDU J. OPPERMANN B. ARUL PANDIAN N. PATRA A. RAGHUNATHAN JAYANTA ROY SHIV SETHI B. SHAW K. SHERWIN O. SINNEN S. K. SINHA K. S. SRIVANI B. STAPPERS C. R. SUBRAHMANYA THIAGARAJ PRABU C. VINUTHA Y. G. WADADEKAR HAOMIAO WANG C. WILLIAMS
This paper presents high-performance computing efforts with FPGA for the accelerated pulsar/transient search for the square kilometre array (SKA). Case studies are presented from within SKA and pathfinder telescopes highlighting future opportunities. It reviews the scenario that has shifted from offline processing of the radio telescope data to digitizing several hundreds/thousands of antenna outputs over huge bandwidths, forming several hundreds of beams, and processing the data in the SKA real-time pulsar search pipelines. A briefaccount of the different architectures of the accelerators, primarily, the new generation field programmable gate array-based accelerators, showing their critical roles to achieve high-performance computing and in handlingthe enormous data volume problems of the SKA is presented here. It also presents power-performance efficiency of this emerging technology and presents potential future scenarios.
Volume 44 All articles Published: 20 April 2023 Article ID 0036 ORIGINAL RESULTS
Investigation of a Machine learning methodology for the SKA pulsar search pipeline
SHASHANK SANJAY BHAT THIAGARAJ PRABU BEN STAPPERS ATUL GHALAME SNEHANSHU SAHA T. S. B SUDARSHAN ZAFIIRAH HOSENIE
The SKA pulsar search pipeline will be used for real time detection of pulsars. Modern radio telescopes, such as SKA will be generating petabytes of data in their full scale of operation. Hence, experience based and data-driven algorithms are being investigated for applications, such as candidate detection. Here, we describe our findings from testing a state of the art object detection algorithm called Mask R-CNN to detect candidate signatures in the SKA pulsar search pipeline. We have trained the Mask R-CNN model to detect candidate images. A custom semi-auto annotation tool was developed and investigated to rapidly mark the regions of interest in large datasets. We have used a simulation dataset to train and build the candidate detection algorithm. A more detailed analysis is planned. This paper presents details of this initial investigationhighlighting the future prospects.
Volume 44, 2023
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