The Gaia mission

doi:10.48550/arxiv.1609.04153

Cited by 9 publications

(19 citation statements)

References 31 publications

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“…Furthermore, all targets are expected to be young based on their activity, YMG membership or low surface gravity measurements, most of them with expected ages ranging from 20 -40 Myr, with the exception of J1036 which has been suggested to be part of the ∼ 400 Myr Ursa Majoris moving group (Mamajek et al 2010;Jones et al 2015). Four of our targets, J0459, J0611, J1036 and J2349 have had their parallax and proper motions measured by Gaia DR2 (Collaboration 2016;Collaboration et al 2018), but no data for J0111, J0332 or J1014 exist in the current release.…”

Section: Sample Selection and Observationsmentioning

confidence: 97%

Characterising Young Visual M-dwarf Binaries with Near-Infrared Integral Field Spectra

Calissendorff,

Janson,

Bonnefoy

2020

Preprint

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We present the results from an integral field spectroscopy study of seven close visual binary pairs of young M-dwarf multiple systems. The target systems are part of the astrometric monitoring AstraLux program, surveying hundreds of M-dwarf systems for multiplicity and obtaining astrometric epochs for orbital constraints. Our new VLT/SINFONI data provides resolved spectral type classification in the J, H and K bands for seven of these low-mass M-dwarf binaries, which we determine by comparing them to empirical templates and examining the strength of water absorption in the K-band. The medium resolution K-band spectra also allows us to derive effective temperatures for the individual components. All targets in the survey display several signs of youth, and some have kinematics similar to young moving groups, or low surface gravities which we determine from measuring equivalent widths of gravity sensitive alkali lines in the J-band. Resolved photometry from our targets is also compared with isochrones from theoretical evolutionary models, further implying young ages. Dynamical masses will be provided from ongoing monitoring of these systems, which can be seen as emblematic binary benchmarks that may be used to calibrate evolutionary models for low-mass stars in the future.

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Section: Sample Selection and Observationsmentioning

confidence: 97%

Characterising Young Visual M-dwarf Binaries with Near-Infrared Integral Field Spectra

Calissendorff,

Janson,

Bonnefoy

2020

Preprint

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“…In 2013, a release including the whole dataset was published with their corresponding catalog and other tabular data. In this work, we used The Gaia mission (Collaboration et al 2016), promoted by the European Space Agency, is a collaboration to chart a three-dimensional map of the galaxy to study the structure, composition, and evolution of the Milky Way. The second data release (DR2) (Gaia et al 2018) includes observations for more than 1.3 billion objects in three photometric bands of which we only use the G-passband.…”

Section: Datamentioning

confidence: 99%

“…In this work, we explore the effectiveness of classifying astronomical time series using both the PLSTM and traditional methods, such as the Long Short Term Memory (LSTM), and a Random Forest trained on features. We test our models using OGLE (Udalski et al 1997), MACHO (Alcock et al 1995), ASAS (Pojmanski & Maciejewski 2005), LINEAR (Stokes et al 2000), Catalina Sky Survey (Drake et al 2009), Gaia (Collaboration et al 2016), andWISE (Wright et al 2010), which are widely-used catalogs containing periodic and non-periodic variable stars. We do not fold the lightcurves for periodical classes because it requires period calculation, which is time-consuming and limits nonperiodical objects' classification.…”

Section: Introductionmentioning

confidence: 99%

The effect of phased recurrent units in the classification of multiple catalogs of astronomical lightcurves

Donoso-Oliva,

Cabrera-Vives,

Protopapas

et al. 2021

Preprint

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In the new era of very large telescopes, where data is crucial to expand scientific knowledge, we have witnessed many deep learning applications for the automatic classification of lightcurves. Recurrent neural networks (RNNs) are one of the models used for these applications, and the LSTM unit stands out for being an excellent choice for the representation of long time series. In general, RNNs assume observations at discrete times, which may not suit the irregular sampling of lightcurves. A traditional technique to address irregular sequences consists of adding the sampling time to the network's input, but this is not guaranteed to capture sampling irregularities during training. Alternatively, the Phased LSTM unit has been created to address this problem by updating its state using the sampling times explicitly. In this work, we study the effectiveness of the LSTM and Phased LSTM based architectures for the classification of astronomical lightcurves. We use seven catalogs containing periodic and nonperiodic astronomical objects. Our findings show that LSTM outperformed PLSTM on 6/7 datasets. However, the combination of both units enhances the results in all datasets.

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“…These make RC become an important probe to explore the structure and evolution of the Galaxy (López-Corredoira et al 2002). There have been many previous efforts to identify RC in large spectral and asteroseismic survey data, such as the Gaia space mission (Collaboration et al 2016(Collaboration et al , 2018, the LAMOST Experiment for Galactic Understanding and Exploration (LEGUE) (Deng et al 2012;Zhao et al 2012), the Apache Point Observatory Galactic Evolution Experiment (APOGEE) (Majewski et al 2017), the GALAH (De Silva et al 2015) and Kepler mission.…”

Section: Introductionmentioning

confidence: 99%

Identification, mass and age of primary red clump stars from spectral features derived with the LAMOST DR7

He,

Luo,

Chen

2022

Preprint

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Although red clump (RC) stars are easy to identify due to their stability of luminosity and color, about 20-50% are actually red giant branch (RGB) stars in the same location on the HR diagram. In this paper, a sample of 210,504 spectra for 184,318 primary RC (PRC) stars from the LAMOST DR7 is identified, which has a purity of higher than 90 percent. The RC and the RGB stars are successfully distinguished through LAMOST spectra(R∼1800 and SNR>10) by adopting the XGBoost ensemble learning algorithm, and the secondary RC stars are also removed. The SHapley Additive exPlanations (SHAP) value is used to explain the top features that the XGBoost model selected. The features are around Fe5270, MgH & Mg Ib, Fe4957, Fe4207, Cr5208, and CN, which can successfully distinguish RGB and RC stars. The XGBoost is also used to estimate the ages and masses of PRC stars by training their spectra with Kepler labeled asteroseismic parameters. The uncertainties of mass and age are 13 and 31 percent, respectively. Verifying the feature attribution model, we find the age-sensitive elements XGBoost gets are consistent with the literature. Distances of the PRC stars are derived by K S absolute magnitude calibrated by Gaia EDR3, which has an uncertainty of about 6 percent and shows the stars mainly locate at the Galactic disk. We also test the XGBoost with R∼250, which is the resolution of the Chinese Space Station Telescope(CSST) under construction, it is still capable of finding sensitive features to distinguish RC and RGB.

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The Gaia mission

Cited by 9 publications

References 31 publications

Characterising Young Visual M-dwarf Binaries with Near-Infrared Integral Field Spectra

Characterising Young Visual M-dwarf Binaries with Near-Infrared Integral Field Spectra

The effect of phased recurrent units in the classification of multiple catalogs of astronomical lightcurves

Identification, mass and age of primary red clump stars from spectral features derived with the LAMOST DR7

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