mlsanityData to 13 Oct 2026

Intelligence of Astronomical Optical Telescope: Present Status and Future Perspectives

arXiv 2306.16834 ↗First posted 29 Jun 2023astro-ph.IM cs.AIcited by 0

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Abstract (arXiv)

Artificial intelligence technology has been widely used in astronomy, and new artificial intelligence technologies and application scenarios are constantly emerging. There have been a large number of papers reviewing the application of artificial intelligence technology in astronomy. However, relevant articles seldom mention telescope intelligence separately, and it is difficult to understand the current development status and research hotspots of telescope intelligence from these papers. This paper combines the development history of artificial intelligence technology and the difficulties of critical technologies of telescopes, comprehensively introduces the development and research hotspots of telescope intelligence, then conducts statistical analysis on various research directions of telescope intelligence and defines the research directions' merits. All kinds of research directions are evaluated, and the research trend of each telescope's intelligence is pointed out. Finally, according to the advantages of artificial intelligence technology and the development trend of telescopes, future research hotspots of telescope intelligence are given.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 8 of its 173 references that are in the map. First posted on arXiv 29 Jun 2023.

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References · 8

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,304
  2. 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,409
  3. 2020Scalable End-to-end Recurrent Neural Network for Variable star classificationcited by 9
  4. 2021Uncertainty-Aware Learning for Improvements in Image Quality of the Canada-France-Hawaii Telescopecited by 6
  5. 2014Supervised detection of anomalous light-curves in massive astronomical catalogscited by 4
  6. 2020Point Spread Function Estimation for Wide Field Small Aperture Telescopes with Deep Neural Networks and Calibration Datacited by 3
  7. 2020RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networkscited by 2
  8. 2023A Machine Learning approach for correcting radial velocities using physical observablescited by 1

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