arXiv Machine Learning August 18, 2026 By Adam Umra, Oways Alsoloh, Oliver Nagy, Aydin Sezgin, Clara Saraceno

Self-Supervised Noise2Noise-Enhanced Denoising for Continuous-Scan Air-Plasma THz Spectroscopy

Read the original on arXiv Machine Learning →

arXiv:2608. 16454v1 Announce Type: cross Abstract: Terahertz time-domain spectroscopy (THz-TDS) based on air-plasma generation and balanced air-biased coherent detection offers gap-free broadband coverage, but individual continuous-scan traces are strongly affected by pulse-to-pulse fluctuations and electronic noise.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Efficient reduction of stellar contamination and noise in planetary transmission spectra using neural networks

arXiv:2602. 10330v3 Announce Type: replace-cross Abstract: Context: The characterization of exoplanetary atmospheres has been transformed by the James Webb Space Telescope (JWST), whose infrared sensitivity enables transmission spectroscopy at unprecedented precision.

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Cycle-Consistent and Uncertainty-Aware Neural Surrogates for Tokamak Edge Plasmas

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Limits of Resolution Equivariance in Fourier Neural Operators

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arXiv Machine Learning

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DREMnet: An Interpretable Denoising Framework for Semi-Airborne Transient Electromagnetic Signal

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