Dust extinction removal from BP/RP spectra gathered from the Gaia Space Telescope using machine learning algorithms

4 pages•Published: February 16, 2023

Lara Pallas-Quintela, Ness Melissa, Minia Manteiga Outeiro and José Carlos Dafonte Vázquez

Abstract

The current project aims to tackle with Gaia’s BR/RP spectra distortion caused by interstellar dust, called reddening or extinction, which makes data not to be correctly classified. For such, it is proposed a machine learning algorithm that is able to learn how to correct such effect making use of denoising autoencoders. In addition, it was also developed a method to estimate the extinction degree, since for almost any spectra that is going to be corrected, such value was not computed. The previous tasks are going to be resourceful at our research group, since we take part at the Gaia project and deal with outlier spectra. In this way, we will be able to do a finer data-preprocessing prior to their classification.

Keyphrases: astronomy, autoencoders, disentangling, gaia, signal processing, unsupervised learning

In: Alvaro Leitao and Lucía Ramos (editors). Proceedings of V XoveTIC Conference. XoveTIC 2022, vol 14, pages 73-76.

Links:	https://easychair.org/publications/paper/xr48
	https://doi.org/10.29007/24zd

BibTeX entry

@inproceedings{XoveTIC2022:Dust_extinction_removal_from,
  author    = {Lara Pallas-Quintela and Ness Melissa and Minia Manteiga Outeiro and José Carlos Dafonte Vázquez},
  title     = {Dust extinction removal from BP/RP spectra gathered from the Gaia Space Telescope using machine learning algorithms},
  booktitle = {Proceedings of V XoveTIC Conference. XoveTIC 2022},
  editor    = {Alvaro Leitao and Lucía Ramos},
  series    = {Kalpa Publications in Computing},
  volume    = {14},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/xr48},
  doi       = {10.29007/24zd},
  pages     = {73-76},
  year      = {2023}}

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