SUPPNet: Neural network for stellar spectrum normalisation^⋆

¹ Astronomical Institute, University of Wrocław, Kopernika 11, 51-622 Wrocław, Poland
e-mail: tomasz.rozanski@uwr.edu.pl
² Department of Computer Science, Faculty of Fundamental Problems of Technology, Wrocław University of Science and Technology, Wrocław, Poland
³ Faculty of Electronics, Wrocław University of Science and Technology, Wrocław, Poland

Received: 6 June 2021
Accepted: 9 December 2021

Abstract

Context. Precise continuum normalisation of merged échelle spectra is a demanding task that is necessary for various detailed spectroscopic analyses. Automatic methods have limited effectiveness due to the variety of features present in the spectra of stars. This complexity often leads to the necessity for manual normalisation which is highly time-consuming.

Aims. The aim of this work is to develop a fully automated normalisation tool that works with order-merged spectra and offers flexible manual fine-tuning, if necessary.

Methods. The core of the proposed method uses the novel, fully convolutional deep neural network (SUPP Network) that was trained to predict a pseudo-continuum. The post-processing step uses smoothing splines that give access to regressed knots, which are useful for optional manual corrections. The active learning technique was applied to deal with possible biases that may arise from training with synthetic spectra and to extend the applicability of the proposed method to features absent in this kind of spectra.

Results. The developed normalisation method was tested with high-resolution spectra of stars with spectral types from O to G, and gives a root mean squared (RMS) error over the set of test stars equal to 0.0128 in the spectral range from 3900 Å to 7000 Å and 0.0081 in the range from 4200 Å to 7000 Å. Experiments with synthetic spectra give a RMS of the order of 0.0050.

Conclusions. The proposed method leads to results that are comparable to careful manual normalisation. Additionally, this approach is general and can be used in other fields of astronomy where background modelling or trend removal is a part of data processing.