A. Hermann | Searcharxiv

arXiv2026

Characterizing Photonic Ring Resonator Filters for OH Suppressed Near-infrared Astronomy

Supernova cosmology relies on accurate measurement of the absolute magnitudes of Type Ia supernovae. Observation in the visible incurs significant systematic uncertainties in these measurements due to their high degree of interstellar dust extinction. Observing in the near-infrared (750-2500 nm) mitigates this issue by decreasing attenuation, decreasing dispersion, and increasing supernova observation distances. However, ground-based observations in the near-infrared suffer from sky background caused by atmospheric OH emission lines. Advancements in photonic ring resonator filter devices make it feasible to suppress these lines. In this paper, we evaluate the performance of a prototype photonic ring resonator device developed for use in the lower H-band (1480-1620 nm). Specifically, we characterize the progression of suppressed wavelengths into the upper H-band (1620-1800 nm) enabling the suppression of emission lines over a broader wavelength range.

astro-ph.IM↗

arXiv2025

Tadah! A Swiss Army Knife for Developing and Deployment of Machine Learning Interatomic Potentials

The Tadah! code provides a versatile platform for developing and optimizing Machine Learning Interatomic Potentials (MLIPs). By integrating composite descriptors, it allows for a nuanced representation of system interactions, customized with unique cutoff functions and interaction distances. Tadah! supports Bayesian Linear Regression (BLR) and Kernel Ridge Regression (KRR) to enhance model accuracy and uncertainty management. A key feature is its hyperparameter optimization cycle, iteratively refining model architecture to improve transferability. This approach incorporates performance constraints, aligning predictions with experimental and theoretical data. Tadah! provides an interface for LAMMPS, enabling the deployment of MLIPs in molecular dynamics simulations. It is designed for broad accessibility, supporting parallel computations on desktop and HPC systems. Tadah! leverages a modular C++ codebase, utilizing both compile-time and runtime polymorphism for flexibility and efficiency. Neural network support and predefined bonding schemes are potential future developments, and Tadah! remains open to community-driven feature expansion. Comprehensive documentation and command-line tools further streamline the development and application of MLIPs.

physics.comp-ph↗