# Unsupervised Method for Correlated Noise Removal for Multi-wavelength Exoplanet Transit Observations

## TL;DR

This paper introduces an unsupervised neural-network-based method utilizing clustering to remove correlated noise from multi-wavelength exoplanet transit light curves, enhancing measurement precision in certain cases.

## Contribution

A novel adaptive, unsupervised neural network approach combined with clustering techniques for noise removal in exoplanet transit data, improving measurement accuracy.

## Key findings

- Smaller error bars achieved for high-quality data from WASP-19b.
- Method did not significantly improve low-quality GJ-1214 data.
- Effectiveness depends on data quality and specific observational circumstances.

## Abstract

Exoplanetary atmospheric observations require an exquisite precision in the measurement of the relative flux among wavelengths. In this paper, we aim to provide a new adaptive method to treat light curves before fitting transit parameters in order to minimize systematic effects that affect, for instance, ground-based observations of exo-atmospheres. We propose a neural-network-based method that uses a reference built from the data itself with parameters that are chosen in an unsupervised fashion. To improve the performance of proposed method, K-means clustering and Silhouette criteria are used for identifying similar wavelengths in each cluster. We also constrain under which circumstances our method improves the measurement of planetary-to-stellar radius ratio without producing significant systematic offset. We tested our method in high quality data from WASP-19b and low quality data from GJ-1214. We succeed in providing smaller error bars for the former when using JKTEBOP, but GJ-1214 light curve was beyond the capabilities of this method to improve as it was expected from our validation tests.

---
Source: https://tomesphere.com/paper/1706.08556