###### Abstract  
Asteroseismology studies the physical structure of stars by analyzing their solar-type oscillations as seismic waves and frequency spectra. The physical processes in stars and oscillations are similar to the Sun, which is more evolved to the red-giant branch (RGB), representing the Sun's future. In stellar astrophysics, the RGB is a crucial problem to determine. An RGB is formed when a star expands and fuses all the hydrogen in its core into helium which starts burning, resulting in helium burning (HeB). According to a recent state by NASA Kepler mission, 7000 HeB and RGB were observed. A study based on an advanced system needs to be implemented to classify RGB and HeB, which helps astronomers. The main aim of this research study is to classify the RGB and HeB in asteroseismology using a deep learning approach. Novel bidirectional-gated recurrent units and a recurrent neural network (BigCR)-based deep learning approach are proposed. The proposed model achieved a 93% accuracy score for asteroseismology classification. The proposed technique outperforms other state-of-the-art studies. The analyzed fundamental properties of RGB and HeB are based on the frequency separation of modes in consecutive order with the same degree, maximum oscillation power frequency, and mode location. Asteroseismology Exploratory Data Analysis (AEDA) is applied to find critical fundamental parameters and patterns that accurately infer from the asteroseismology dataset. Our key findings from the research are based on a novel classification model and analysis of root causes for the formation of HeB and RGB. The study analysis identified that the cause of HeB increases when the value of feature Numax is high and feature Epsilon is low. Our research study helps astronomers and space star oscillations analyzers meet their astronomy findings. asteroseismology; deep learning; red-giant branch classification; helium burning classification; stars oscillations analysis +
Footnote †: journal: Appl. Sci. 0000-0001-588-958X/0000-0001-588X/00000-0001-588X/00000-0001-588X/00000-00001-588X/  
Stellar evolution refers to how stars change with time [3]. The primary factor in determining how a star evolves is its mass. In asteroseismology, the evolution of a star after the red-giant phase depends on its mass [4]. An RGB is formed when a star expands and fuses all the hydrogen in its core into helium, which then starts burning. This burning results in HeB. The RGB is a cool, bright, and evolved star. The RGB stars have a helium core surrounded by a shell of hydrogen. The ranging amplitudes of RGB oscillate from a few tens to thousands of parts per million. In the final stages, an RGB is a dying star of stellar evolution. In about five billion years, our Sun will turn into an RGB. The Sun will expand and overwhelm the inner planets, possibly even Earth [5]. The change in the Sun may serve as a source of hope for other planets. When a star transforms into RGB, it changes the habitable zone. The range of habitable zone distance where liquid water can exist on a world's surface. Due to stars remaining RGB for a billion years, it may be possible for life to arise on orbiting planets and moons by receiving some warmth [6]. According to a recent state by the NASA Kepler mission, 7000 helium-burning red giants were observed [7]. The RGB bloats to 62 million to 620 million miles in diameter, which is 100 to 1000 times wider than our Sun. The RGB stars cover a large area to spread energy. The surface temperatures of RGB stars are relatively calm. The temperature reaches from 4000 to 5800 degrees Fahrenheit, which is slightly over half as hot as the Sun. This temperature causes stars to shine in the spectrum. Deep learning is a sub-type of the machine learning area [8] and a core element in the data science field. Deep learning [9] algorithms are inspired by the working function of the human brain called artificial neural networks. Deep learning models are used for automated predictive modeling tasks [10].