Crescent Moon Visibility: A New Criterion using Deep learned Artificial Neural-Network

Authors

DOI:

https://doi.org/10.24996/ijs.2024.65.4.45

Keywords:

Crescent Moon Early Sighting, Machine Learning, Neural Networks, Pattern Classification

Abstract

     Many authors investigated the problem of the early visibility of the new crescent moon after the conjunction and proposed many criteria addressing this issue in the literature. This article presented a proposed criterion for early crescent moon sighting based on a deep-learned pattern recognizer artificial neural network (ANN) performance. Moon sight datasets were collected from various sources and used to learn the ANN. The new criterion relied on the crescent width and the arc of vision from the edge of the crescent bright limb. The result of that criterion was a control value indicating the moon's visibility condition, which separated the datasets into four regions: invisible, telescope only, probably visible, and certainly visible. This criterion was used on the dataset for ANN learning to compare its efficiency with the actual moon visibility events.

Downloads

Published

2024-04-30

Issue

Section

Astronomy and Space

How to Cite

Crescent Moon Visibility: A New Criterion using Deep learned Artificial Neural-Network. (2024). Iraqi Journal of Science, 65(4), 2332-2343. https://doi.org/10.24996/ijs.2024.65.4.45

Similar Articles

11-20 of 594

You may also start an advanced similarity search for this article.