SUPPNet: Neural network for stellar spectrum normalisation

Abstract

Precise continuum normalisation of merged échelle spectra is a demanding task 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 of manual normalisation which is a time demanding task. 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. 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 gives access to regressed knots 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. The developed normalisation method was tested with high-resolution spectra of stars having spectral types from O to G, and gave root mean squared (RMS) error over the set of test stars equal $0.0128$ in the spectral range from $3900\mathring{A}$ to $7000\mathring{A}$ and $0.0081$ in the range from $4200\mathring{A}$ to $7000\mathring{A}$. Experiments with synthetic spectra give RMS of the order of $0.0050$. The proposed method gives results 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. The algorithm is available online at https://git.io/JqJhf.

arXiv comment20 pages (incl. appendix), 30 figures, 4 tables. Revised based on the reviewers' comments and submitted to A&A

References in corpus (28)

• Adam: A Method for Stochastic Optimization
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• The Gaia mission
• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• Machine learning and the physical sciences
• The Apache Point Observatory Galactic Evolution Experiment (APOGEE)
• A Grid of NLTE Line-Blanketed Model Atmospheres of O-type Stars
• A Grid of NLTE Line-Blanketed Model Atmospheres of Early B-type Stars
• Data Mining and Machine Learning in Astronomy
• Aggregated Residual Transformations for Deep Neural Networks
• Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
• Rotational velocities of A-type stars IV. Evolution of rotational velocities
• Improved Methods for Fluorescence Background Subtraction from Raman Spectra
• Machine Learning for the Zwicky Transient Facility
• Machine Learning in Astronomy: a practical overview
• High-precision abundances of elements in solar-type stars. Evidence of two distinct sequences in abundance-age relations
• Lithium abundance patterns of late-F stars: an in-depth analysis of the lithium desert
• Catalog for the ESPRESSO blind radial velocity exoplanet survey
• RASSINE: Interactive tool for normalising stellar spectra I. Description and performance of the code
• On the class of Oe stars
• ODUSSEAS: A machine learning tool to derive effective temperature and metallicity for M dwarf stars
• Modeling the Echelle Spectra Continuum with Alpha Shapes and Local Regression Fitting
• Deep Learning Applied to the Asteroseismic Modeling of Stars with Coherent Oscillation Modes
• Abundances in HD27411 and the helium problem in Am stars
• A Catalog of Calibrator Stars for Next-Generation Optical Interferometers
• Active deep learning method for the discovery of objects of interest in large spectroscopic surveys
• Is the Wind of the Galactic Oe Star HD 155806 Magnetically Confined?

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