2022A&A...657A.129A


Query : 2022A&A...657A.129A

2022A&A...657A.129A - Astronomy and Astrophysics, volume 657A, 129-129 (2022/1-1)

Youth analysis of near-infrared spectra of young low-mass stars and brown dwarfs.

ALMENDROS-ABAD V., MUZIC K., MOITINHO A., KRONE-MARTINS A. and KUBIAK K.

Abstract (from CDS):


Context. Studies of the low-mass population statistics in young clusters are the foundation for our understanding of the formation of low-mass stars and brown dwarfs. Robust low-mass populations can be obtained through near-infrared spectroscopy, which provides confirmation of the cool and young nature of member candidates. However, the spectroscopic analysis of these objects is often not performed in a uniform manner, and the assessment of youth generally relies on the visual inspection of youth features whose behavior is not well understood.
Aims. We aim at building a method that efficiently identifies young low-mass stars and brown dwarfs from low-resolution near-infrared spectra, by studying gravity-sensitive features and their evolution with age.
Methods. We have built a data set composed of all publicly available (∼2800) near-infrared spectra of dwarfs with spectral types between M0 and L3. First, we investigate methods for the derivation of the spectral type and extinction via comparison to spectral templates and various spectral indices. Then, we examine gravity-sensitive spectral indices and apply machine learning methods in order to efficiently separate young (≥10 Myr) objects from the field.
Results. Using a set of six spectral indices for spectral typing, including two newly defined ones (TLI-J and TLI-K), we are able to achieve a precision below one spectral subtype across the entire spectral type range. We define a new gravity-sensitive spectral index (TLI-g) that consistently separates young objects from field objects; it shows a performance superior to other indices from the literature. Even better separation between the two classes can be achieved through machine learning methods that use the entire near-infrared spectra as an input. Moreover, we show that the H and K bands alone are sufficient for this purpose. Finally, we evaluate the relative importance of different spectral regions for gravity classification as returned by the machine learning models. We find that the H-band broadband shape is the most relevant feature, followed by the FeH absorption bands at 1.2 µm and 1.24 µm and the KI doublet at 1.24 µm.

Abstract Copyright: © ESO 2022

Journal keyword(s): stars: pre-main sequence - stars: formation - techniques: spectroscopic - stars: low-mass

VizieR on-line data: <Available at CDS (J/A+A/657/A129): list.dat fits/*>

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

Simbad objects: 12

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Number of rows : 12
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 NGC 1333 OpC 03 29 11 +31 18.6           ~ 1371 1
2 IC 348 OpC 03 44 32 +32 09.5           ~ 1334 1
3 Cl Melotte 22 OpC 03 46 24 +24 06.8           ~ 3279 0
4 NAME Taurus Complex SFR 04 41.0 +25 52           ~ 4094 0
5 NAME AB Dor Moving Group MGr 06 38 53.4 -42 50 04           ~ 442 0
6 NAME TW Hya Association As* 11 01.9 -34 42           ~ 907 0
7 NAME Cha 1 MoC 11 06 48 -77 18.0           ~ 1095 1
8 NAME Upper Sco Association As* 16 12 -23.4           ~ 1252 1
9 NAME Upper Sco-Cen As* 16 15 -24.2           ~ 1267 1
10 NAME Ophiuchus Molecular Cloud SFR 16 28 06 -24 32.5           ~ 3446 1
11 LDN 1746 DNe 17 11.3 -27 22           ~ 127 0
12 [BHB2007] 18NE Y*? 17 11 41.7215271000 -27 25 50.248160868           ~ 3 0

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2023.01.31-00:46:05

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