## 5 Conclusions  
Systematic experimentation across multiple algorithms revealed four cardinal lessons. First, meticulous evaluation protocols remain a prerequisite for trustworthy astronomical imagery analysis; micro-averaged metrics alone obscure class-specific fragility; therefore, macro-weighted indices must accompany energy telemetry. Second, the GPU-optimized multilayer perceptron, abbreviated as \(\mathrm{MLP}_{\mathrm{cuda}}\), achieved the apex EcoScore, coupling 0.989 macro F1 with negligible carbon imprint, indicating that dense linear algebra, when arranged to fit device memory, maximizes efficacy under Green-AI tenets. Third, ensemble decision forests retain metric parity within scalar hardware yet consume two orders of magnitude additional power; hence, deployment at telescope edge nodes ought to privilege memory-coherent neural kernels. Fourth, adversarial resilience, together with interpretability, cannot be considered epilogue concerns; perturbation-aware training improved recall for cigar-shaped smooth galaxies by 2.1% while class-activation mapping accelerated diagnostic auditing during annotation sessions. Quantum acceleration remains speculative; the present qubit counts fail to surpass classical throughput, yet circuit simulations indicate potential for Hamiltonian-based feature extraction. Consequently, subsequent investigations should refine eco-efficient architectures via low-rank adapters, sparsity induction, as well as dataset augmentation through generative networks; interdisciplinary collaboration between astrophysics, machine learning theory, and sustainability science will expedite such refinements. A comparative Carbon Index (CI), calculated during trials, positioned RDA above ensemble baselines yet beneath \(\mathrm{MLP}_{\mathrm{cuda}}\), corroborating the preeminence of memory-aligned dense kernels within sustainability-constrained observatories.  
Conceptualization, G.A.P. and V.A.; methodology, V.A. and I.G.; software, V.A. and E.V.G.; validation, V.A., I.G. and S.K.; formal analysis, V.A.; investigation, V.A. and E.V.G.; resources, V.A., I.G. and S.K.; data curation, V.A., I.G. and S.K.; writing--original draft preparation, V.A. and E.V.G.; writing--review and editing, V.A. and G.A.P.; visualization, V.A.; supervision, G.A.P.; project administration, G.A.P.; funding acquisition, G.A.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data used in this study are openly available from the Kaggle competition \"Galaxy Zoo--The Galaxy Challenge\" at [[https://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge](https://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge)]([https://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge](https://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge)) (accessed on 30 April 2025). The dataset comprises 61,578 training images with corresponding probabilistic morphological annotations across 37 classes and 79,975 unlabeled test images; each image is a 424 \(\times\) 424-pixel RGB image centered on a galaxy. No new data were generated during this study.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial Intelligence \\ ML & Machine Learning \\ DL & Deep Learning \\ Green AI & Green Artificial Intelligence \\ IoT & Internet of Things \\ Edge & Edge Computing \\ CNN & Convolutional Neural Network \\ RNN & Recurrent Neural Network \\ LSTM & Long Short-Term Memory \\ GRU & Gated Recurrent Unit \\ \end{tabular}