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. However, stellar heterogeneities (e.g., spots and faculae) remain a dominant source of contamination that can bias atmospheric retrievals if not properly corrected. Aims: We present a methodology for reducing stellar contamination and instrument-specific noise from exoplanet transmission spectra using neural networks, in particula
The increasing sophistication of neural networks and the operational maturity of the James Webb Space Telescope enable more refined analysis of exoplanetary data.
This development enhances the precision of exoplanet atmospheric characterization, crucial for identifying potentially habitable worlds and understanding planetary formation.
The ability to more accurately filter out stellar contamination and noise will lead to more reliable exoplanet atmospheric models, shifting previous understandings.
- · Astrophysicists
- · Space agencies
- · AI researchers
- · Exoplanet research programs
- · Researchers relying on less robust contamination correction methods
More accurate exoplanet atmospheric compositions will be determined, leading to a clearer picture of their habitability.
This improved understanding could refine theories of planetary evolution and the conditions necessary for life.
Advances in AI for astrophysical data analysis might cross-pollinate into other scientific domains requiring complex signal-to-noise reduction.
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Read at arXiv cs.LG