Applications of machine-learning algorithms for infrared colour selection of Galactic Wolf–Rayet stars

Abstract

We have investigated and applied machine-learning algorithms for Infrared Colour Selection of Galactic Wolf-Rayet (WR) candidates. Objects taken from the GLIMPSE catalogue of the infrared objects in the Galactic plane can be classified into different stellar populations based on the colours inferred from their broadband photometric magnitudes (J, H and K s from 2MASS, and the four Spitzer /IRAC bands). The algorithms tested in this pilot study are variants of the k-Nearest Neighbours (k-NN) approach, which is ideal for exploratory studies of classification problems where interrelations between variables and classes are complicated. The aims of this study are (1) to provide an automated tool to select reliable WR candidates and potentially other classes of objects, (2) to measure the efficiency of infrared colour selection at performing these tasks and, (3) to lay the groundwork for statistically inferring the total number of WR stars in our Galaxy. We report the performance results obtained over a set of known objects and selected candidates for which we have carried out follow-up spectroscopic observations, and confirm the discovery of 4 new WR stars.

The detectability of Wolf–Rayet stars in M33-like spirals up to 30 Mpc

“…From Table 1, it is clear that visual inspection of the image results in a more complete WR survey. Whilst Morello et al (2018) successfully use a machine-learning approach to identify new WR stars in the Milky Way, it is unclear if such an approach can be successfully employed for more distant galaxies where we see changing photometric properties with changing spatial resolution (recall Fig. 3).…”

The detectability of Wolf-Rayet Stars in M33-ike spirals up to 30 Mpc

“…From Table 1 it is clear that visual inspection of the image results in a more complete WR survey. Whilst Morello et al (2018) successfully use a machine-learning approach to identify new WR stars in the Milky Way, it is unclear if such an approach can be successfully employed for more distant galaxies where we see changing photometric properties with changing spatial resolution (recall Figure 3).…”

Surveying the reach and maturity of machine learning and artificial intelligence in astronomy

“…With regards to the role of ML and AI in advancing knowledge in astronomy, there was clear evidence from the sample of recent publications that discovery tasks are being performed with all of the data types: images (Ciuca & Hernández, ; Gomez Gonzalez, Absil, & Van Droogenbroeck, ; Hartley, Flamary, Jackson, Tagore, & Metcalf, ; Jacobs et al, ; Lanusse et al, ; Morello, Morris, Van Dyk, Marston, & Mauerhan, ; Pourrahmani et al, ; Wan et al, ); spectroscopy (Bu, Lei, Zhao, Bu, & Pan, ; Li et al, ); photometry (Ostrovski et al, ; Timlin et al, ; Vida & Roettenbacher, ); light curves (Armstrong et al, ; Cohen et al, ; Giles & Walkowicz, ; Hedges, Hodgkin, & Kennedy, ; Heinze et al, ; Peña et al, ; van Roestel et al, ); time‐series (Connor & van Leeuwen, ; Farah et al, ; Michilli et al, ; Morello et al, ; Pang et al, ; Tan et al, ); catalogues (Lin et al, ; Marchetti et al, ; Nguyen, Pankratius, Eckman, & Seager, ; Yan et al, ); and simulation (Kuntzer & Courbin, ; Nadler et al, ; Xu & Offner, ).…”

“…Two key activities in stellar astronomy are spectral classification (e.g., Garcia‐Dias, Allende Prieto, Sánchez Almeida, & Ordovás‐Pascual, ; Wang, Guo, & Luo, with k ‐means clustering; Kong et al, ; classification of young stellar objects with eight different methods by Miettinen, ) and photometric classification (e.g., Ksoll et al, ; Zhang et al, with SVM, RF, and Fast Boxes). Many new examples of specific stellar classes have been discovered, such as Wolf‐Rayet stars (Morello et al, ), blue horizontal branch stars (Wan et al, ), hot sub dwarf stars (Bu et al, ), and rare hypervelocity stars (Marchetti et al, ). ML/AI have also led to the discovery of unresolved binary stars in simulated catalogues using RF and ANN algorithms (Kuntzer & Courbin, ), and new pulsars, and fewer false positives, from the LOFAR Tied‐Array All‐Sky Survey (Michilli et al, ; Tan et al, ).…”

“…With regards to the role of ML and AI in advancing knowledge in astronomy, there was clear evidence from the sample of recent publications that discovery tasks are being performed with all of the data types: images [Hartley et al, 2017, Ciuca and Hernández, 2017, Gomez Gonzalez et al, 2018, Pourrahmani et al, 2018, Lanusse et al, 2018, Wan et al, 2018, Morello et al, 2018, Jacobs et al, 2017; spectroscopy [Bu et al, 2017; photometry [Timlin et al, 2018, Vida andRoettenbacher, 2018, Ostrovski et al, Table 1: From a qualitative examination of a sample of ∼ 200 refereed publications from 2017 to February 2019, a mapping emerges between the nature of astronomical data and the way that machine learning and artificial intelligence is actively been pursued. The table presents a qualitative summary of the categories of ML/AI algorithms and the most common types of astronomical data in the sample of publications.…”