contrary to popular belief, i’m just as much ponder as i am poet. here’s some work that’s actually important for the world
Machine Learning in Exoplanet Detection
The discovery of exoplanets has accelerated with missions like Kepler and TESS, generating datasets too vast for traditional analysis.
Machine learning (ML), particularly convolutional neural networks (CNNs), now outperforms classical methods in detecting subtle planetary signals.
We evaluate ML pipelines for exoplanet detection, including data preprocessing (noise reduction, normalization), feature extraction (transit depth, duration), and model training (CNNs, RNNs, transformers). Quantitative benchmarks compare performance metrics (precision, recall, F1-score) across architectures. We include classical methods such as Box Least Squares (BLS) for baseline comparison.
Quantum Entanglement and Communication:
Exploring Bell’s Theorem and the EPR Paradox
Quantum entanglement is one of the most intriguing phenomena in modern physics.
Proposed as a challenge to quantum mechanics by Einstein, Podolsky, and Rosen in 1935, the EPR paradox questioned whether quantum mechanics could be a complete theory. Bell’s theorem, introduced in 1964, provided away to experimentally test the nonlocal nature of quantum mechanics.
This paper explores the theoretical underpinnings of the EPR paradox and Bell’s inequality, analyzes the philosophical implications of nonlocality, and discusses why entanglement cannot be used for faster-than-light communication despite its instantaneous correlations.
between the rotting walls 