The TESS–HERMES survey data release 1: high-resolution spectroscopy of the TESS southern continuous viewing zone

Sharma, Sanjib; Stello, Dennis; Buder, Sven; Kos, Janez; Bland-Hawthorn, Joss; Asplund, Martin; Duong, Ly; Lin, Jane; Lind, Karin; Ness, Melissa; Huber, Daniel; Zwitter, T.; Traven, G.; Hon, Marc; Kafle, Prajwal R.; Khanna, Shourya; Saddon, Hafiz; Anguiano, Borja; Casey, Andrew R.; Freeman, K. C.; Martell, Sarah L.; Silva, Gayandhi M. De; Simpson, Jeffrey D.; Wittenmyer, R. A.; Zucker, D. B.

doi:10.1093/mnras/stx2582

Cited by 149 publications

(129 citation statements)

References 54 publications

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“…Because APASS was not complete in the Southern sky at the start of observations, we use a synthetic V JK magnitude calculated from 2MASS photometry: V JK = K + 2(J − K + 0.14) + 0.382e ((J−K−0.2)/0.5) . Using PARSEC iscochrones (Marigo et al 2017) it was shown in Sharma et al (2018) that this transformation is reasonably accurate across the parameter space where the majority of GALAH stars fall. The GALAH observations are done in following three fixed magnitude ranges of V J K .…”

Section: Target Selectionmentioning

confidence: 99%

“…If available and not flagged, stellar parameters from a prior version of The Cannon (version 1.3) are chosen. This version was also used for previous data releases of GALAH (Martell et al 2017), TESS-HERMES (Sharma et al 2018), and K2-HERMES (Wittenmyer et al 2018). If these parameters are flagged or not available, the synthesis commences with the stellar parameter estimates from the reduction process.…”

Section: Analysis Step 1: the Training Set Analysis With Spectroscopymentioning

confidence: 99%

“…Asteroseismic surveys from NASA's Kepler and K2 missions are providing reliable estimates of stellar surface gravities and masses (e.g. Stello et al 2015;Sharma et al 2016) and this is expected to continue with missions like TESS (Ricker et al 2015;Sharma et al 2018) and PLATO (Rauer et al 2014;Miglio et al 2017). The ESA Gaia astrometric mission will measure accurate distances for billions of stars belonging to most components of the Galaxy (Perryman et al 2001;Lindegren et al 2016).…”

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confidence: 99%

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The GALAH Survey: second data release

Buder

Asplund

Duong

et al. 2018

Monthly Notices of the Royal Astronomical Society

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369

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The Galactic Archaeology with HERMES (GALAH) survey is a large-scale stellar spectroscopic survey of the Milky Way and designed to deliver chemical information complementary to a large number of stars covered by the Gaia mission. We present the GALAH second public data release (GALAH DR2) containing 342,682 stars. For these stars, the GALAH collaboration provides stellar parameters and abundances for up to 23 elements to the community. Here we present the target selection, observation, data reduction and detailed explanation of how the spectra were analysed to estimate stellar parameters and element abundances. For the stellar analysis, we have used a multi-step approach. We use the physics-driven spectrum synthesis of Spectroscopy Made Easy (SME) to derive stellar labels (T eff , log g, [Fe/H], [X/Fe], v mic , v sin i, A K S ) for a representative training set of stars. This information is then propagated to the whole survey with the data-driven method of The Cannon. Special care has been exercised in the spectral synthesis to only consider spectral lines that have reliable atomic input data and are little affected by blending lines. Departures from local thermodynamic equilibrium (LTE) are considered for several key elements, including Li, O, Na, Mg, Al, Si, and Fe, using 1D stellar atmosphere models. Validation tests including repeat observations, Gaia benchmark stars, open and globular clusters, and K2 asteroseismic targets lend confidence to our methods and results. Combining the GALAH DR2 catalogue with the kinematic information from Gaia will enable a wide range of Galactic Archaeology studies, with unprecedented detail, dimensionality, and scope.

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Section: Target Selectionmentioning

confidence: 99%

Section: Analysis Step 1: the Training Set Analysis With Spectroscopymentioning

confidence: 99%

mentioning

confidence: 99%

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The GALAH Survey: second data release

Buder

Asplund

Duong

et al. 2018

Monthly Notices of the Royal Astronomical Society

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369

447

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“…The mass of the stellar component is 4 × 10 8 M in either case, split among 4 × 10 4 particles. (Sharma et al 2018) programs. To maintain the survey selection function, we have applied the quality cuts summarised in Table 1, which gives a total of 465870 stars cross-matched with Gaia DR2.…”

Section: Profilementioning

confidence: 99%

The GALAH survey and Gaia DR2: Linking ridges, arches, and vertical waves in the kinematics of the Milky Way

Khanna

Sharma

Tepper-García

et al. 2019

Monthly Notices of the Royal Astronomical Society

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Gaia DR2 has revealed new small-scale and large-scale patterns in the phase-space distribution of stars in the Milky Way. In cylindrical Galactic coordinates (R, φ, z), ridge-like structures can be seen in the (R, V φ ) plane and asymmetric arch-like structures in the (V R , V φ ) plane. We show that the ridges are also clearly present when the third dimension of the (R, V φ ) plane is represented by z , V z , V R , [Fe/H] and [α/Fe] . The maps suggest that stars along the ridges lie preferentially close to the Galactic midplane (|z| < 0.2 kpc), and have metallicity and α elemental abundance similar to that of the Sun. We show that phase mixing of disrupting spiral arms can generate both the ridges and the arches. It also generates discrete groupings in orbital energy − the ridges and arches are simply surfaces of constant energy. We identify 8 distinct ridges in the Gaia DR2 data: six of them have constant energy while two have constant angular momentum. Given that the signature is strongest for stars close to the plane, the presence of ridges in z and V z suggests a coupling between planar and vertical directions. We demonstrate, using N-body simulations that such coupling can be generated both in isolated discs and in discs perturbed by an orbiting satellite like the Sagittarius dwarf galaxy.

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“…Ligi et al 2016). Masses are more difficult to measure, so stellar atmosphere models are often used -as an example, Sharma et al (2018) constrain model-dependent masses for more than 10,000 stars using high resolution spectroscopy, with a median precision of 7%. Following these examples, we draw the radius and mass of the host star from normal distributions with widths of 3% and 7%, respectively.…”

Section: Prior Assumptionsmentioning

confidence: 99%

Identifying Exo-Earth Candidates in Direct Imaging Data through Bayesian Classification

Bixel

Apai

2019

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Future space telescopes may be able to directly image ∼10 -100 planets with sizes and orbits consistent with habitable surface conditions ("exo-Earth candidates" or EECs), but observers will face difficulty in distinguishing these from the potentially hundreds of non-habitable "false positives" which will also be detected. To maximize the efficiency of follow-up observations, a prioritization scheme must be developed to determine which planets are most likely to be EECs. In this paper, we present a Bayesian method for estimating the likelihood that any directly imaged extrasolar planet is a true exo-Earth candidate by interpreting the planet's apparent magnitude and separation in light of existing exoplanet statistics. As a specific application of this general framework, we use published estimates of the discovery yield of future space-based direct imaging mission concepts to conduct "mock surveys" in which we compute the likelihood that each detected planet is an EEC. We find that it will be difficult to determine which planets are EECs with > 50% confidence using single-band photometry immediately upon their detection. The best way to reduce this ambiguity would be to constrain the planet's orbit by revisiting the system multiple times or through a radial velocity precursor survey. Astrometric or radial velocity constraints on the planet's mass would offer a lesser benefit. Finally, we show that a Bayesian approach to prioritizing targets would improve the follow-up efficiency of a direct imaging survey versus a blind approach using the same data. For example, the prioritized approach could reduce the amount of integration time required for the spectral detection (or rejection) of water absorption in most EECs by a factor of two.

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The TESS–HERMES survey data release 1: high-resolution spectroscopy of the TESS southern continuous viewing zone

Cited by 149 publications

References 54 publications

The GALAH Survey: second data release

The GALAH Survey: second data release

The GALAH survey and Gaia DR2: Linking ridges, arches, and vertical waves in the kinematics of the Milky Way

Identifying Exo-Earth Candidates in Direct Imaging Data through Bayesian Classification

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