## 3 Results
### Performance
In stark contrast, SP (_spiral_) retains conspicuous separability; its logarithmic arm signature yields precision exceeding 0.99 across GPU-enabled architectures (**GEAs**) as well as CPU residents, consistent with saliency heat-maps that localize discriminative gradients upon arm termini. Comparative timing analyses reinforce this interpretation: ViT fine-tuning under CUDA finalizes ten epochs in 2.3 min with stable SP recall, whereas CPU-bound RNN requires 48 min yet delivers analogous metric values, underscoring throughput disparities rather than representational deficits. Evaluating hardware modality, GEA delivers a mean recall augmentation of 0.012 for IBS and 0.015 for CSS relative to CPU baselines, attributable to larger effective batch capacities that stabilize batch-norm statistics; nevertheless, these increments fail to nullify classification overlap. Investigation of classwise Grad-CAM overlays introduces an explanatory hypothesis: both IBS and CSS share luminosity profiles devoid of high-frequency bar structures, yielding gradient dispersion across the entire stellar halo, hence diminishing localisation strength. An ancillary analysis employing t-SNE on frozen ResNet50 embeddings supports this claim by revealing intertwined manifolds for IBS-CSS, while SP forms a detached lobe within the latent space. Consequently, improved stratification may require incorporation of ellipticity priors or synthetic balancer augmentation; preliminary trials with Variational Auto-Encoder augmentation produced a 0.021 recall uplift for IBS, though CSS benefits remained marginal. Collectively, evidence indicates that metric superiority observed for spiral morphologies emanates from distinctive arm curvature cues, whereas IBS-CSS ambiguity persists owing to shared photometric envelopes rather than deficiencies in training schema or compute resource allocation. The ViT model, renowned for exceptional performance in general image classification tasks, exhibits an unexpected underperformance within the galaxy morphology classification context explored here. Despite theoretical advantages, such as capturing long-range dependencies via self-attention mechanisms, ViT's empirical accuracy remains comparatively subdued, registering approximately 71.3%. This discrepancy primarily originates from the inherent misalignment between ViT's patch-based embedding schema and the nuanced structural features intrinsic to astronomical imagery. Transformer models necessitate large-scale, densely annotated datasets to effectively learn global contextual information. However, the Galaxy Zoo dataset, characterized by moderate size and diverse, subtle morphological attributes, inadequately fulfills these conditions. Consequently, ViT's relatively coarse-grained patch extraction inserts an unavoidable loss of fine morphological details critical for accurate classification. This granularity issue becomes pronounced because astronomical images predominantly rely on delicate distinctions in spatial patterns, contrasts, and luminosity distributions that Transformers are ill equipped to represent without substantial pre-training on astronomically relevant data. Utilizing ResNet-50-derived feature embeddings introduces implicit bias toward CNN-specific representations, a phenomenon inadequately scrutinized in existing astronomical classification research. While ResNet-50 features consistently yield high classification performance, their employment inevitably skews the analytical perspective toward convolutional feature extraction paradigms. This implicit bias restricts the representational diversity of features, thereby potentially neglecting significant morphological attributes extractable through traditional astronomical descriptors, such as ellipticity, luminosity profiles, or Fourier-transform-based texture measures. Consequently, a critical analysis of classification performance that comparatively evaluates handcrafted astronomical features alongside CNN-derived embeddings remains conspicuously absent.