Stellar Spectral Classification

Gray, R. O.; Corbally, C. J.; Burgasser, Adam J.

doi:10.2307/j.ctv1nxcw72

Cited by 95 publications

(146 citation statements)

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“…Spectral classification was performed in the framework of the refined MK classification system following the methodology outlined in Gray & Garrison (1987, 1989a; Garrison & Gray (1994) and Gray & Corbally (2009). For a precise classification and to identify peculiarities, the blue-violet (3800−4600 Å) spectral region was compared visually to, and overlaid with, MK standard star spectra and a set of synthetic spectra.…”

Section: Spectral Classificationmentioning

confidence: 99%

“…The standard star spectra were taken from the libr18 and libnor36 libraries that are distributed with the MKCLASS code 6 , a computer program written to classify stellar spectra on the MK system (Gray & Corbally 2014), and the liblamost library, which is a preliminary set of suitable LAMOST spectra compiled by Hümmerich et al (2020). The synthetic spectra were calculated using the program SPECTRUM 7 (Gray & Corbally 1994) and ATLAS9 model atmospheres (Castelli & Kurucz 2003), assuming log g = 4.0, [M/H] = 0.0 and a microturbulent velocity of 2 km s −1 .…”

Section: Spectral Classificationmentioning

confidence: 99%

“…mCP stars exhibit several peculiarities related to traditional classification criteria, which need to be taken into account when assigning a spectral type to these objects. They often show weak, unusually broad or otherwise peculiar Ca K line profiles, weak Mg 4481 Å lines and are markedly He deficient (Gray & Corbally 2009;Ghazaryan et al 2018). The latter characteristic resulted in the classification of many of the hotter (late B-type) mCP stars as A-type stars because of the lack of, or the occurrence of only very weak, He lines in their spectra (Gray & Corbally 2009).…”

Section: Spectral Classificationmentioning

confidence: 99%

“…They often show weak, unusually broad or otherwise peculiar Ca K line profiles, weak Mg 4481 Å lines and are markedly He deficient (Gray & Corbally 2009;Ghazaryan et al 2018). The latter characteristic resulted in the classification of many of the hotter (late B-type) mCP stars as A-type stars because of the lack of, or the occurrence of only very weak, He lines in their spectra (Gray & Corbally 2009). Furthermore, mCP stars generally show enhanced and peculiar metallic lines.…”

Section: Spectral Classificationmentioning

confidence: 99%

“…Furthermore, mCP stars generally show enhanced and peculiar metallic lines. In general, therefore, the hydrogen-line profile is the most accurate indicator of the actual temperature of these objects (Gray & Corbally 2009).…”

Section: Spectral Classificationmentioning

confidence: 99%

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New magnetic chemically peculiar stars and candidates in the ATLAS First Catalog of Variable Stars

Bernhard,

Huemmerich,

Paunzen

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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. (2018) in the ATLAS First Catalogue of Variable Stars, are, at least to a high percentage, made up of 𝛼 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 Fiber 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 disk; 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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Section: Spectral Classificationmentioning

confidence: 99%

Section: Spectral Classificationmentioning

confidence: 99%

Section: Spectral Classificationmentioning

confidence: 99%

Section: Spectral Classificationmentioning

confidence: 99%

Section: Spectral Classificationmentioning

confidence: 99%

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New magnetic chemically peculiar stars and candidates in the ATLAS First Catalog of Variable Stars

Bernhard,

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Reliability and Interpretability in Science and Deep Learning

Scorzato

2024

Minds & Machines

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In recent years, the question of the reliability of Machine Learning (ML) methods has acquired significant importance, and the analysis of the associated uncertainties has motivated a growing amount of research. However, most of these studies have applied standard error analysis to ML models—and in particular Deep Neural Network (DNN) models—which represent a rather significant departure from standard scientific modelling. It is therefore necessary to integrate the standard error analysis with a deeper epistemological analysis of the possible differences between DNN models and standard scientific modelling and the possible implications of these differences in the assessment of reliability. This article offers several contributions. First, it emphasises the ubiquitous role of model assumptions (both in ML and traditional science) against the illusion of theory-free science. Secondly, model assumptions are analysed from the point of view of their (epistemic) complexity, which is shown to be language-independent. It is argued that the high epistemic complexity of DNN models hinders the estimate of their reliability and also their prospect of long term progress. Some potential ways forward are suggested. Thirdly, this article identifies the close relation between a model’s epistemic complexity and its interpretability, as introduced in the context of responsible AI. This clarifies in which sense—and to what extent—the lack of understanding of a model (black-box problem) impacts its interpretability in a way that is independent of individual skills. It also clarifies how interpretability is a precondition for a plausible assessment of the reliability of any model, which cannot be based on statistical analysis alone. This article focuses on the comparison between traditional scientific models and DNN models. However, Random Forest (RF) and Logistic Regression (LR) models are also briefly considered.

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Solar Radiation Resource Assessment for Renewable Energy Conversion

Myers

2012

Comprehensive Renewable Energy

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Stellar Spectral Classification

Cited by 95 publications

References 0 publications

New magnetic chemically peculiar stars and candidates in the ATLAS First Catalog of Variable Stars

New magnetic chemically peculiar stars and candidates in the ATLAS First Catalog of Variable Stars

Reliability and Interpretability in Science and Deep Learning

Solar Radiation Resource Assessment for Renewable Energy Conversion

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