MSIHP-Net: A Multiscale Deep Learning Method for Identifying Green Pea Galaxies

Green Pea galaxies (GPs) are a rare class of compact, low metallicity star-forming galaxies characterized by strong [O iii] λ5007 emission lines, and high specific star formation rates in surveys such as Sloan Digital Sky Survey (SDSS) and Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). However, traditional manual or semi-automated approaches to identifying GPs face significant challenges in processing large-scale spectroscopic datasets from SDSS and LAMOST due to data volume and spectral complexity, and lack of efficient automated methods tailored for spectra hinders comprehensive GP studies. To address these issues, we propose a lightweight deep learning model with a small parameter count, termed Multiscale Inception Hybrid Pooling Network (MSIHP-Net) for automated and efficient GP identification. To rigorously evaluate the model’s performance, we construct different datasets sourced from SDSS and LAMOST, incorporating various signal-to-noise ratios (SNRs). The MSIHP-Net architecture is built upon a sequence of three consecutive InceptionBlock1D modules. Each module employs parallel convolutions with varying kernel sizes to perform multiscale feature extraction across different receptive fields. This is followed by a hybrid pooling layer that concatenates the outputs of adaptive average pooling and adaptive max pooling, thereby preserving both local and global information. Experimental results demonstrate that MSIHP-Net consistently achieves the highest accuracy, F1-score, precision, and recall for the GP category in all datasets with varying SNRs. Extensive experiments indicate the superior performance and robustness of MSIHP-Net. These advances offer a promising approach to further exploring the characteristics and evolutionary behaviors of GPs.