MSV-2035 Astronomy Document - Inside Design - FINAL - FINAL

Astronomy & Astrophysics 110 large format / large volume data sets and computational astrophysics. India is already making considerable headway in the application of quantum technology in several areas such as developing techniques for quantum communication, development of quantum clocks, ultra-stable and sub-Hz line- width lasers/astrocombs and fiber-optic quantum links. All of these developments can be combined with the proposal of networking optical telescopes to build a prototype optical synthetic telescope, a technology that in the future can, in principle, enable the proposed NLOT to be part of a global optical synthetic telescope. It is important for Indian astronomers to develop interdisciplinary, international collaborations to begin development in this futuristic area of application of quantum technology inA&A.Akey technology for the development of squeezed light sources is that of ultra-low loss optical coatings. At present, this technology is not available in the country and it is essential to develop the same through international collaborations. The technology of squeezed light used in GW detectors has ramifications for other QETs such as quantum computers, microelectronics as well as for experiments in fundamental physics. 9.6 DataAnalytics, BigData, Machine Learning andArtificial Intelligence The last two decades have seen an (almost) exponential increase in the application of artificial intelligence (AI), machine learning (ML) and related techniques to almost all areas of human endeavour. Analysis of data sets has always been an integral part of astronomy. However, the advent of digital sky surveys in the early 1990s brought in data sets on tera scales that demanded automation of data processing and analysis tasks. Large data sets frommultiple surveys and large facilities (both ground and space) also saw the development of data archives and database systems that have evolved into global data grids providing access to data from multiple facilities. The growth in data volume and the need for value-added data products that can be used for follow-up research led to the application of Machine Learning (ML) techniques for aspects such as source detection, morphological and structural classifications. Some of the early works included those of astronomers from India, for example the application of neural networks in classification of stellar spectra, and separation of stars and galaxies. The wealth and growth of data volumes at increasing rates, together with the complexity of the data, are leading to increased use of ML/AI techniques to quickly discover features in astrophysical images, light curves and spectra, and to characterise, cluster and classify features, sources and populations. AI/ML techniques are also being employed to extract physical information from simulations, for example in estimating the amount of dark energy and dark matter (and their evolution) in the Universe, and disentangling the myriad effects of dark matter and dark energy. Yet another application is in the area of heliospheric science and space weather. Application of AI/ML in astronomy is a growing landscape, with its scope also widening to data acquisition. AI systems can be trained to plan observations dynamically based on specified quality criteria. Such AI-based control systems can provide efficient planning, scheduling, and observing, leading to improved scientific outcomes in terms of both quality and quantity of observations. Facilities that can provide dynamic observingwill be critical in the area of Multi-Messenger and Time Domain Astronomy wherein prioritisation of the follow-up of transient events by a number of different facilities becomes necessary. In the radio regime, real-time identification and flagging of transient radio signals due to radio frequency interference (RFI) is crucial, and this can be done effectively by employingAI/ML methods. MEGA SCIENCE VISION-2035