Active deep learning method for the discovery of objects of interest in large spectroscopic surveys

Abstract

Context. Current archives of the LAMOST telescope contain millions of pipeline-processed spectra that have probably never been seen by human eyes. Most of the rare objects with interesting physical properties, however, can only be identified by visual analysis of their characteristic spectral features. A proper combination of interactive visualisation with modern machine learning techniques opens new ways to discover such objects. Aims. We apply active learning classification methods supported by deep convolutional neural networks to automatically identify complex emission-line shapes in multi-million spectra archives. Methods. We used the pool-based uncertainty sampling active learning method driven by a custom-designed deep convolutional neural network with 12 layers. The architecture of the network was inspired by VGGNet, AlexNet, and ZFNet, but it was adapted for operating on one-dimensional feature vectors. The unlabelled pool set is represented by 4.1 million spectra from the LAMOST data release 2 survey. The initial training of the network was performed on a labelled set of about 13 000 spectra obtained in the 400 Å wide region around Hα by the 2 m Perek telescope of the Ondřejov observatory, which mostly contains spectra of Be and related early-type stars. The differences between the Ondřejov intermediate-resolution and the LAMOST low-resolution spectrographs were compensated for by Gaussian blurring and wavelength conversion. Results. After several iterations, the network was able to successfully identify emission-line stars with an error smaller than 6.5%. Using the technology of the Virtual Observatory to visualise the results, we discovered 1 013 spectra of 948 new candidates of emission-line objects in addition to 664 spectra of 549 objects that are listed in SIMBAD and 2 644 spectra of 2 291 objects identified in an earlier paper of a Chinese group led by Wen Hou. The most interesting objects with unusual spectral properties are discussed in detail.

Astronomaly: Personalised Active Anomaly Detection in Astronomical Data

“…In astronomy, machine learning algorithms for anomaly detection have been applied to spectra (e.g. Škoda et al, 2020;Baron and Poznanski, 2016), time series data (e.g. Martínez-Galarza et al, 2020;Giles and Walkowicz, 2018) and images (e.g.…”