Publication details
New magnetic chemically peculiar stars and candidates in the ATLAS first catalogue of variable stars
| Authors | |
|---|---|
| Year of publication | 2021 |
| Type | Article in Periodical |
| Magazine / Source | Monthly Notices of the Royal Astronomical Society |
| MU Faculty or unit | |
| Citation | |
| Web | https://academic.oup.com/mnras/article-abstract/506/3/4561/6324590 |
| Doi | http://dx.doi.org/10.1093/mnras/stab2065 |
| Keywords | stars: chemically peculiar; stars: variables: general |
| Description | The number of known variable stars has increased by several magnitudes over the last decade, and automated classification routines are becoming increasingly important to cope with this development. Here we show that the ‘upside-down CBH variables’, which were proposed as a potentially new class of variable stars by Heinze et al. in the ATLAS First Catalogue of Variable Stars, are, at least to a high percentage, made up of ?2 Canum Venaticorum (ACV) variables – that is, photometrically variable magnetic chemically peculiar (CP2/He-peculiar) stars – with distinct double-wave light curves. Using suitable selection criteria, we identified 264 candidate ACV variables in the ATLAS variable star catalogue. 62 of these objects were spectroscopically confirmed with spectra from the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (all new discoveries except for nine stars) and classified on the MK system. The other 202 stars are here presented as ACV star candidates that require spectroscopic confirmation. The vast majority of our sample of stars are main-sequence objects. Derived masses range from 1.4 M? to 5 M?, with half our sample stars being situated in the range from 2?M? to 2.4?M?, in good agreement with the spectral classifications. Most stars belong to the thin or thick disc; four objects, however, classify as members of the halo population. With a peak magnitude distribution at around 14th magnitude, the here presented stars are situated at the faint end of the known Galactic mCP star population. Our study highlights the need to consider rare variability classes, like ACV variables, in automated classification routines. |
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