A hybrid CNN-Transformer denoiser trained on synthetic spectra substantially reduces noise and improves stellar population recovery for low-S/N galaxy observations in controlled tests.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3representative citing papers
ASTERIS, a self-supervised spatiotemporal denoising algorithm, improves astronomical detection limits by 1 magnitude at 90% completeness while identifying three times more redshift >9 galaxy candidates in JWST images.
A comprehensive public dataset of simulated Ariel exoplanet transmission spectra is released to benchmark detrending algorithms, with an ML baseline highlighting dataset shift risks.
citing papers explorer
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A CNN--Transformer Denoiser for low-$S/N$ Galaxy Spectra: Stellar Population Recovery in Synthetic Tests
A hybrid CNN-Transformer denoiser trained on synthetic spectra substantially reduces noise and improves stellar population recovery for low-S/N galaxy observations in controlled tests.
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Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising
ASTERIS, a self-supervised spatiotemporal denoising algorithm, improves astronomical detection limits by 1 magnitude at 90% completeness while identifying three times more redshift >9 galaxy candidates in JWST images.
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A public dataset of Ariel simulated observations for developing exoplanetary atmosphere data reduction pipelines
A comprehensive public dataset of simulated Ariel exoplanet transmission spectra is released to benchmark detrending algorithms, with an ML baseline highlighting dataset shift risks.