Machine learning in astronomy

Kembhavi, Ajit; Pattnaik, Rohan

doi:10.1007/s12036-022-09871-2

Cited by 10 publications

(2 citation statements)

References 44 publications

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“…This development in astronomical observation technology has led to a noticeable increase in the number of collected elements, which far exceeds human capacity to analyze findings. For this reason, researchers must now turn to machine learning to analyze such data, identifying and classifying transient objects or events within extensive observations of the firmament [5].…”

Section: Introductionmentioning

confidence: 99%

Random forest Algorithm for the Classification of Spectral Data of Astronomical Objects

et al. 2023

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Over time, human beings have built increasingly large astronomical observatories to increase the number of discoveries related to celestial objects. However, the amount of collected elements far exceeds the human capacity to analyze findings without help. For this reason, researchers must now turn to machine learning to analyze such data, identifying and classifying transient objects or events within extensive observations of the firmament. Algorithms from the family of random forests (an ensemble of decision trees) have become a powerful tool that can be used to classify astronomical events and objects. This work aims to illustrate the versatility of machine learning algorithms, such as decision trees, to facilitate the identification and classification of celestial bodies by manipulating hyperparameters and studying the attributes of celestial body datasets. By applying a random forest algorithm to a well-known dataset that includes three types of celestial bodies, its effectiveness was compared against some supervised classifiers of the most important approaches (Bayes, nearest neighbors, support vector machines, and neural networks). The results show that random forests are a good alternative for data analysis and classification in astronomical observations.

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Section: Introductionmentioning

confidence: 99%

Random forest Algorithm for the Classification of Spectral Data of Astronomical Objects

et al. 2023

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“…• Analyzing data and images [35,44,66,90,108,117,124,126,151,163]. • Interpreting data and images [13,14,21,42].…”

Section: Introduction: Exponential Growth In the Use Of Artificial In...mentioning

confidence: 99%

The Ethics of Using Artificial Intelligence in Scientific Research: New Guidance Needed for a New Tool

Resnik,

Hosseini

2023

Preprint

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The use of artificial intelligence (AI) in research offers many important benefits for science and society but also creates some novel and complex ethical issues. While the issues raised by AI use will not necessitate a radical change in the established ethical norms of science, they will require the scientific community to develop new guidance for the appropriate use of AI. In this article, we provide a brief introduction to AI and how it can be used in research, examine some of the ethical issues raised by using AI in research, and offer recommendations for appropriate use of this technology. We recommend that: 1) researchers and software developers are responsible for identifying, describing, reducing, and controlling AI-related biases and random errors; 2) researchers should disclose and explain their use of AI in language that can be understood by non-experts; 3) if appropriate, researchers should engage with impacted communities, populations, and other stakeholders concerning the use of AI in research to obtain their advice and assistance and address their interests and concerns; 4) researchers may be liable for misconduct if they intentionally, knowingly, or recklessly use AI to fabricate or falsify data or commit plagiarism; 5) AI systems should not be named authors, inventors, or copyright holders but their contributions to research should be disclosed and described; 6) AI systems should not be used in situations that may involve unauthorized disclosure of confidential information related to human research subjects, unpublished research, potential intellectual property claims, or proprietary or classified research; and 7) education and mentoring in responsible conduct of research should include discussion of ethical use of AI.

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Ephemeris accuracy improvement for moons of gas giants: a deep learning based method

Guo,

Jiang,

Liu

2024

Discov Sp

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Ephemerides accuracy of gas giant planetary system is of paramount importance in astronomical research, planetary exploration missions, and space navigation. Traditional mathematical methods face challenges when processing with data of different observation precisions, which may lead to extra noise and reduce the ephemeris accuracy. This paper proposes an Denoising Autoencoder based Method for Ephemeris Improvement (DAMEI) to improve the accuracy of ephemerides for moons in gas giant planetary system. Utilizing multiple sources of data efficiently, the DAMEI method can mitigate the impact of noise and uncertainty introduced by observational data with different precisions. Based on the symmetrical structure, the DAMEI method learns to encode essential motion features of gas giant planetary system into a latent space and captures the intricate patterns in planetary motion from observational data, subsequently decodes it to improve the ephemeris accuracy. The experimental results show that, for Jupiter’s major satellites (Galilean moons), the DAMEI method achieves more accurate ephemeris of up to 91.65% compared with current mathematical method. The proposed method is also assessed with satellites of Saturn, Uranus, and Neptune. It is shown that the DAMEI method also presents a better performance of up to 95.37%. The promising performance of DAMEI method can reduce the uncertainty introduced by low-accuracy data and improve ephemeris accuracy making the utmost of observational data with different precisions.

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Machine learning in astronomy

Cited by 10 publications

References 44 publications

Random forest Algorithm for the Classification of Spectral Data of Astronomical Objects

Random forest Algorithm for the Classification of Spectral Data of Astronomical Objects

The Ethics of Using Artificial Intelligence in Scientific Research: New Guidance Needed for a New Tool

Ephemeris accuracy improvement for moons of gas giants: a deep learning based method

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