2022A&A...657A..62K


Query : 2022A&A...657A..62K

2022A&A...657A..62K - Astronomy and Astrophysics, volume 657A, 62-62 (2022/1-1)

A new automated tool for the spectral classification of OB stars.

KYRITSIS E., MARAVELIAS G., ZEZAS A., BONFINI P., KOVLAKAS K. and REIG P.

Abstract (from CDS):


Context. As an increasing number of spectroscopic surveys become available, an automated approach to spectral classification becomes necessary. Due to the significance of the massive stars, it is of great importance to identify the phenomenological parameters of these stars (e.g., the spectral type), which can be used as proxies to their physical parameters (e.g., mass and temperature).
Aims. In this work, we aim to use the random forest (RF) algorithm to develop a tool for the automated spectral classification of OB-type stars according to their sub-types.
Methods. We used the regular RF algorithm, the probabilistic RF, which is an extension of RF that incorporates uncertainties, and we introduced the KDE - RF method which is a combination of the kernel-density estimation and the RF algorithm. We trained the algorithms on the equivalent width (EW) of characteristic absorption lines measured in high-quality spectra (signal-to-noise (S/N)≥50) from large Galactic (LAMOST, GOSSS) and extragalactic surveys (2dF, VFTS) with available spectral types and luminosity classes. By following an adaptive binning approach, we grouped the labels of these data in 11 spectral classes within the O2-B9 range. We examined which of the characteristic spectral lines (features) are more important for the classification based on a number of feature selection methods, and we searched for the optimal hyperparameters of the classifiers to achieve the best performance.
Results. From the feature-screening process, we find that the full set of 17 spectral lines is needed to reach the maximum performance per spectral class. We find that the overall accuracy score is ∼70%, with similar results across all approaches. We apply our model in other observational data sets providing examples of the potential application of our classifier to real science cases. We find that it performs well for both single massive stars and for the companion massive stars in Be X-ray binaries, especially for data of similar quality to the training sample. In addition, we propose a reduced ten-features scheme that can be applied to large data sets with lower S/N ∼ 20-50.
Conclusions. The similarity in the performances of our models indicates the robustness and the reliability of the RF algorithm when it is used for the spectral classification of early-type stars. The score of ∼70% is high if we consider (a) the complexity of such multiclass classification problems (i.e., 11 classes), (b) the intrinsic scatter of the EW distributions within the examined spectral classes, and (c) the diversity of the training set since we use data obtained from different surveys with different observing strategies. In addition, the approach presented in this work is applicable to products from different surveys in terms of quality (e.g., different resolution) and different formats (e.g., absolute or normalized flux), while our classifier is agnostic to the luminosity class of a star, and, as much as possible, it is metallicity independent.

Abstract Copyright: © ESO 2022

Journal keyword(s): stars: early-type - stars: massive - X-rays: binaries - methods: statistical - stars: emission-line, Be

Status at CDS : Tables of objects will be appraised for possible ingestion in SIMBAD.

Simbad objects: 34

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Number of rows : 34
N Identifier Otype ICRS (J2000)
RA
ICRS (J2000)
DEC
Mag U Mag B Mag V Mag R Mag I Sp type #ref
1850 - 2023
#notes
1 HD 1279 * 00 17 09.0440331312 +47 56 50.661966660 5.36 5.764 5.852     B8III 59 0
2 V* AO Cas SB* 00 17 43.0633133256 +51 25 59.116450728 5.04 6.01 6.14     O9.2II+O8V((f)) 323 0
3 * 26 And * 00 18 42.1688838792 +43 47 28.108208148   6.03 6.11     B8V 59 1
4 HD 1976 SB* 00 24 15.6540035976 +52 01 11.703208404 4.84 5.459 5.580     B5IV 141 1
5 HD 2626 ** 00 30 19.9344223392 +59 58 39.189476856 5.59 5.95 5.94     B7IV 57 0
6 * kap Cas s*b 00 32 59.9911963 +62 55 54.417374 3.50 4.30 4.16 4.02 3.96 B1Ia 457 0
7 HD 3240 * 00 36 08.3101957104 +54 10 06.419675676 4.58 4.967 5.076     B7III 65 0
8 * pi. And SB* 00 36 52.8492565 +33 43 09.638398 3.65 4.20 4.36 4.40 4.52 B5V 194 0
9 * 68 Cas * 00 44 26.1915514056 +47 51 50.342883408   5.520 5.648     B5V 104 0
10 * 23 Cas SB* 00 47 46.0538925192 +74 50 51.251241300   5.338 5.413     B8III 76 0
11 * nu. Cas V* 00 48 50.0214949536 +50 58 05.394457956   4.789 4.891     B9III 53 0
12 * nu. And SB* 00 49 48.8473652 +41 04 44.076380 3.80 4.38 4.53 4.56 4.71 B5V 209 0
13 NAME SMC G 00 52 38.0 -72 48 01   2.79 2.2     ~ 10530 1
14 V* V662 Cas HXB 01 18 02.6976641952 +65 17 29.829200388 12.27 11.99 11.14 10.33 9.58 B1Iae 256 0
15 EM* GGA 104 HXB 01 47 00.2124077208 +61 21 23.663788056 11.76 12.09 11.366 11.00 10.52 B1IIIe 181 0
16 HD 14272 * 02 19 37.2796688448 +39 50 05.798899932   6.534 6.628     B8V 38 0
17 * 10 Per s*b 02 25 16.0283450208 +56 36 35.354429280 5.95 6.56 6.26 5.93 5.74 B2Ia 238 0
18 HD 14947 s*b 02 26 46.9899705528 +58 52 33.118732380 7.83 8.44 7.98     O4.5If 249 0
19 HD 15137 SB* 02 27 59.8113542520 +52 32 57.589857672 7 7.92 7.86     O9.5II-IIIn 123 0
20 * ksi02 Cet * 02 28 09.5570589539 +08 27 36.216809306 4.14 4.25 4.30 4.29 4.34 B9III 326 0
21 HD 15558 Y*O 02 32 42.5366901168 +61 27 21.572901360 7.82 8.37 7.87 7.34 6.94 O4.5III(f) 309 0
22 HD 15570 Em* 02 32 49.4207394600 +61 22 42.089384892 8.40 8.80 8.11 7.41 6.86 O4If 298 0
23 * ome For * 02 33 50.7014978880 -28 13 56.415570096 4.7 4.90 4.96     B9Va 64 0
24 LS I +61 303 HXB 02 40 31.6644419688 +61 13 45.593918580 11.27 11.61 10.75 10.19 9.55 B0Ve 817 2
25 NAME Magellanic Clouds GrG 03 00 -71.0           ~ 6555 1
26 LS V +44 17 HXB 04 40 59.3296237272 +44 31 49.258049376 11.02 11.42 10.73 10.28 9.76 B0.2Ve 114 0
27 NAME LMC G 05 23 34.6 -69 45 22     0.4     ~ 16507 1
28 RMC 136 Cl* 05 38 42.396 -69 06 03.36           ~ 1908 1
29 HD 245770 HXB 05 38 54.5748918624 +26 18 56.836952784 9.30 9.84 9.39 8.77 8.30 O9/B0III/Ve 944 0
30 IGR J06074+2205 HXB 06 07 26.6120621496 +22 05 47.759865432   12.85 12.21 11.80 11.32 B0.5Ve 42 1
31 GSC 03588-00834 HXB 21 03 35.7100848936 +45 45 05.568751692   15.34 14.20 13.49 12.75 B0Ve 170 0
32 2MASS J21342037+4738002 HXB 21 34 20.3718881736 +47 38 00.205959228   14.68 14.16 13.80 13.42 ~ 20 0
33 BD+53 2790 HXB 22 07 56.2366802328 +54 31 06.408879888 9.4 10.11 9.84 9.64 9.43 O9.5Vep 197 0
34 NAME Local Group GrG ~ ~           ~ 7892 0

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2023.01.27-19:23:59

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