Titlebook: Big-Data-Analytics in Astronomy, Science, and Engineering; 9th International Co Shelly Sachdeva,Yutaka Watanobe,Subhash Bhalla Conference p

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Introduction to Optimization Methodstly from data. The combination of SR with deep learning (e.g. Graph Neural Network and Autoencoders) provides a powerful toolkit for scientists to push the frontiers of scientific discovery in a data-driven manner. We briefly overview SR, autoencoders and GNN and highlight examples where they have b

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https://doi.org/10.1007/978-94-009-5705-3 evolution and big variety. Prior research has revealed several indicators that developers consider important when selecting a framework. In this study, we propose and develop a system that assists developers in the selection process of a front-end framework, which collects data from repository and

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https://doi.org/10.1007/978-94-009-3153-4es for years to come. However, their data throughput has overwhelmed the ability to manually synthesize alerts for devising and coordinating necessary follow-up with limited resources. The advent of Rubin Observatory, with alert volumes an order of magnitude higher at otherwise sparse cadence, prese

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Functions, Transformations, Operators,rging method to measure large-scale intensity fluctuations of spectral lines emitted from galaxies and intergalactic medium. Observing their large-scale distributions enables us to study cosmology and galaxy formation and evolution. One of the problems with the LIM is observational noises and line i

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Introduction to Optimization of Structuresaging and spectroscopic data is technically challenging, and producing scientific outputs from the big data will remain a key task in the next decade. We develop novel methods based on modern machine learning and deep learning to analyze data from Subaru Hyper Suprime-Cam. In this contribution, we f

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