Wavelet theory applied to the study of spectra of trans-Neptunian objects

TL;DR

This paper introduces a wavelet-based filtering method to improve the analysis of low signal-to-noise ratio spectra of Trans-Neptunian objects, aiding in surface composition identification.

Contribution

It presents a novel wavelet filtering methodology tailored for TNO spectra, enhancing noise removal while preserving spectral features for better interpretation.

Findings

Successfully identified water and methanol ices in TNO spectra

Wavelet filtering improved spectral clarity and feature detection

Detected new spectral features in some TNOs previously unreported

Abstract

Reflection spectroscopy in the Near Infrared (NIR) is used to investigate the surface composition of Trans-Neptunian objects (TNOs). In general, these spectra are difficult to interpret due to the low apparent brightness of the TNOs, causing low signal-to-noise ratio even in spectra obtained with the largest telescopes available on the Earth, making necessary to use filtering techniques to analyze and interpret them. The purpose of this paper is to present a methodology to analyze the spectra of TNOs. Specifically, we aim at filtering these spectra in the best possible way: maximizing the remotion of noise, while minimizing the loss of signal. We use wavelets to filter the spectra. The wavelets are a mathematical tool that decomposes the signal into its constituent parts, allowing to analyze the data in different areas of frequencies with the resolution of each component tied to its scale. To check the reliability of our method, we compare the filtered spectra with spectra of water and methanol ices to identify some common structures between them. Of the 50 TNOs of our sample, we identify traces of the presence of water ices and methanol in the spectra of several of them, some with previous reports, while for other objects there were no previous reports. We conclude that the wavelet technique is successful in filtering spectra of TNOs.