# A Principal Component Analysis-based method to analyse high-resolution spectroscopic data

## TL;DR

This paper introduces an automatic pipeline utilizing PCA and CCF for analyzing high-resolution spectroscopic data of exoplanet atmospheres, successfully detecting key molecules and aligning with existing results.

## Contribution

A novel PCA-based analysis pipeline for high-resolution spectroscopic data, improving automation and validation in exoplanet atmospheric studies.

## Key findings

- Successful detection of CO and H₂O in exoplanet atmospheres
- Pipeline results agree with previous studies
- Enhanced automation in spectral data analysis

## Abstract

High-Resolution Spectroscopy (HRS) has been used to study the composition and dynamics of exoplanetary atmospheres. In particular, the spectrometer CRIRES installed on the ESO-VLT has been used to record high-resolution spectra in the Near-IR of gaseous exoplanets. Here we present a new automatic pipeline to analyze CRIRES data-sets. Said pipeline is based on a novel use of Principal Component Analysis (PCA) and Cross-Correlation Function (CCF). The exoplanetary atmosphere is modeled with the $\tau$-REx code using opacities at high temperature from the ExoMol project. In this work, we tested our analysis tools on the detection of CO and H$_2$O in the atmospheres of the hot-Jupiters HD209458b and HD189733b. The results of our pipeline are in agreement with previous results in the literature and other techniques.