Beyond the Luminescence Trap: A Relativistic kappa-Dense Autoencoder for the Objective Taxonomy of Native Raman Spectra

In Raman spectroscopy, the non-destructive analysis of raw samples is systematically hindered by intense luminescence and fluorescence backgrounds that mask critical structural signals, such as the D and G bands in carbonaceous materials. Traditional workflows rely on subjective, manual baseline subtraction, which often introduces geometric artifacts and bias. In this work, we propose a paradigm shift by analyzing native, un-preprocessed data using a novel kappa-Dense Autoencoder governed by the non-extensive, relativistic statistical framework of Kaniadakis. We implemented a comparative study between a classical Dense Autoencoder optimized via standard Shannon-based Mean Squared Error (MSE) and our kappa-deformed architecture. While the classical Shannon model exhibits "cluster collapse"—grouping spectra based on macroscopic instrumental noise and baseline slopes—the kappa-deformed loss function applies a mathematical "brake" to macroscopic errors. This mechanism allows the neural network to automatically prioritize localized molecular features over sweeping fluorescence gradients, trapping the core chemical signature within a compressed 12-dimensional latent bottleneck. Our results demonstrate that the kappa-model naturally performs an intrinsic, objective denoising, mapping samples into clusters defined by their genuine chemical taxonomy (e.g., true I_D/I_G ratios) rather than instrumental artifacts. Furthermore, we observe that the ability of the kappa-loss to mimic the selective attention of a human expert suggests a profound parallel with biological perception, such as the Weber-Fechner Law. We hypothesize that biological neural networks may natively operate via non-extensive frameworks to optimize cognitive efficiency under environmental noise. This approach proves that the most pristine chemical information can be extracted directly from raw data by looking through the noise rather than attempting to erase it.