scholarly journals A survey of M stars in the field of view of Kepler space telescope

2010 ◽  
Vol 6 (S276) ◽  
pp. 448-449 ◽  
Author(s):  
Mahmoudreza Oshagh ◽  
Nader Haghighipour ◽  
Nuno C. Santos

AbstractM dwarfs constitute more than 70% of the stars in the solar neighborhood. They are cooler and smaller than Sun-like stars and have less-massive disks which suggests that planets around these stars are more likely to be Neptune-size or smaller. The transit depths and transit times of planets around M stars are large and well-matched to the Kepler temporal resolution. As a result, M stars have been of particular interest for searching for planets in both radial velocity and transit photometry surveys. We have recently started a project on searching for possible planet-hosting M stars in the publicly available data from Kepler space telescope. We have used four criteria, namely, the magnitude, proper motion, H-Ks and J-H colors, and searched for M stars in Q0 and Q1 data sets. We have been able to find 108 M stars among which 54 had not been previously identified among Kepler's targets. We discuss the details of our selection process and present the results.

2013 ◽  
Vol 9 (S298) ◽  
pp. 310-321 ◽  
Author(s):  
X.-W. Liu ◽  
H.-B. Yuan ◽  
Z.-Y. Huo ◽  
L.-C. Deng ◽  
J.-L. Hou ◽  
...  

AbstractAs a major component of the LAMOST Galactic surveys, the LAMOST Spectroscopic Survey of the Galactic Anti-center (LSS-GAC) will survey a significant volume of the Galactic thin/thick disks and halo in a contiguous sky area of ~3,400 sq.deg., centered on the Galactic anti-center (|b| ≤ 30°, 150 ≤ l ≤ 210°), and obtain λλ3800–9000 low resolution (R ~1,800) spectra for a statistically complete sample of ≳ 3 M stars of all colors, uniformly and randomly selected from (r, g - r) and (r, r - i) Hess diagrams obtained from a CCD imaging photometric survey of ~5,400 sq.deg. with the Xuyi 1.04/1.20 m Schmidt Telescope, ranging from r = 14.0 to a limiting magnitude of r = 17.8 (18.5 for limited fields). The survey will deliver spectral classification, radial velocity (Vr) and stellar parameters (effective temperature (Teff), surface gravity (log g) and metallicity [Fe/H]) for millions of Galactic stars. Together with Gaia which will provide accurate distances and tangential velocities for a billion stars, the LSS-GAC will yield a unique data set to study the stellar populations, chemical composition, kinematics and structure of the disks and their interface with the halo, identify streams of debris of tidally disrupted dwarf galaxies and clusters, probe the gravitational potential and dark matter distribution, map the 3D distribution of interstellar dust extinction, search for rare objects (e.g. extremely metal-poor or hyper-velocity stars), and ultimately advance our understanding of the assemblage of the Milky Way and other galaxies and the origin of regularity and diversity of their properties.The survey was initiated in the fall of 2012 and expected to complete in the spring of 2017. Hitherto, about 0.4 M spectra of S/N(λ7450) ≥ 10 per pixel have been accumulated. In addition, bright nights have been used to target stars brighter than 14 mag and have so far generated over 0.4 M spectra, yielding an excellent sample of local stars to probe the solar neighborhood. LSP3, a set of pipelines tailored to the need of LSS-GAC, for spectral flux-calibration, and radial velocity and stellar parameter determinations, have been developed at Peking University, based on packages developed for the SDSS and those at the National Astronomical Observatories of Chinese Academy of Sciences. Comparisons of multi-epoch observations, with the SDSS results, as well as applying the pipelines to open and globular clusters show that LSP3 has achieved a precision of 5 km s−1, 110 K, 0.15 dex and 0.15 dex for Vr, Teff, log g and [Fe/H], respectively. The data are publicly available, subject to regulations of the LAMOST data policy, and begin to yield scientific results. The potential of LSS-GAC and power of existing data are illustrated with examples of selected problems.


2012 ◽  
Vol 8 (S293) ◽  
pp. 177-182
Author(s):  
A. Quirrenbach ◽  
P. J. Amado ◽  
J. A. Caballero ◽  
H. Mandel ◽  
R. Mundt ◽  
...  

AbstractCARMENES (Calar Alto high-Resolution search for M dwarfs with Exo-earths with Near-infrared and optical Echelle Spectrographs) is a next-generation instrument under construction for the 3.5 m telescope at the Calar Alto Observatory by a consortium of eleven Spanish and German institutions. The scientific goal of the project is a five-year exoplanet survey targeting 300 M stars with the completed instrument. The CARMENES hardware consists of two separate échelle spectrographs covering the wavelength range from 0.55 to 1.7 μm at a spectral resolution of R = 82,000, fed by fibers from the Cassegrain focus of the telescope. Both spectrographs are housed in a temperature-stabilized environment in vacuum tanks, to enable a long-term radial velocity precision of 1 m s−1 employing a simultaneous calibration with Th-Ne and U-Ne emission line lamps.


2015 ◽  
Vol 11 (S317) ◽  
pp. 371-372
Author(s):  
Jing Zhong ◽  
Sébastien Lépine ◽  
Jing Li ◽  
Li Chen ◽  
Jinliang Hou

AbstractIn this work, we present a set of M-type star candidates selected from the LAMOST DR1. A discrimination method with the spectral index diagram is used to separate M giants and M dwarfs. Then, we have successfully assembled a set of M giants templates from M0 to M6, using the spectra identified from the LAMOST spectral database. After combining the M dwarf templates in Zhong et al. (2015a) and the new created M giant templates, we use the M-type spectral library to perform the template-fit method to classify and identify M-type stars in the LAMOST DR1. A catalog of M-type star candidates including 8639 M giants and 101690 M dwarfs/subdwarfs is provided. As an additional results, we also present other fundamental parameters like proper motion, photometry, radial velocity and spectroscopic distance.


2010 ◽  
Vol 6 (S276) ◽  
pp. 545-546 ◽  
Author(s):  
Andreas Quirrenbach ◽  
Pedro J. Amado ◽  
José A. Caballero ◽  
Holger Mandel ◽  
Reinhard Mundt ◽  
...  

AbstractCARMENES (Calar Alto high-Resolution search for M dwarfs with Exo-earths with Near-infrared and optical Echelle Spectrographs) is a next-generation instrument for the 3.5 m telescope at the Calar Alto Observatory. CARMENES will conduct a five-year exoplanet survey targeting ~300 M stars. The CARMENES instrument consists of two separate fiber-fed spectrographs covering the wavelength range from 0.52 to 1.7 μm at a spectral resolution of R = 85,000. The spectrographs are housed in a temperature-stabilized environment in vacuum tanks, to enable a 1 m/s radial velocity precision employing a simultaneous emission-line calibration.


1999 ◽  
Vol 170 ◽  
pp. 218-222
Author(s):  
G. Szécsényi-Nagy

AbstractUntil recently the problem of collecting high resolution spectra of flare stars has been intractable since the techniques available have not been sensitive enough to reach these extremely faint objects. Although many of the nearest stars (and practically all of the nearby variable stars) belong to this class, even the ones nearest to our sun are fainter than magnitude 8 or 10. In determining the radial velocity of nearby flare stars astronomers accepted the available accuracy of ~ 1 km/s. This may be adequate for the classification of the objects into age classes (according their kinematic properties).The other considerable group of flare stars is taken traditionally as a natural by-product of star formation processes which go on in clusters and associations. Until recently there has not been any serious attack against the widely popular hypothesis that all but a few of the flare stars discovered in the fields of stellar aggregates (their number exceeds that of the solar neighborhood flare stars) are physical members of the systems. The discovery (Szécsényi-Nagy et al. 1997, 1998) that hundreds of flare stars found in the field of M45 may not be cluster members may change the situation. Most flare stars observed there are very faint and consequently they were missing from previously published lists of Pleiades members. For one third of the objects only reliable membership probabilities have been determined, and many of them are listed as probable non-members (Haro, Chavira, & Gonzalez 1982). However, a recently published photographic proper motion survey of the Pleiades’ field (Souchay & Schilbach 1995) provided reliable membership probability values for many stars of extremely low luminosity too. Based on that about 85% of the well-documented flare stars can be – and have been – identified. Our results (Szécsényi-Nagy et al. 1997) undoubtedly prove that a substantial fraction (~ 40%) of the so called Pleiades flare stars are (more or less) definitely non-members. Since all of these new cluster membership probability calculations have been based on stellar proper motion values, in order to be able to reach a final decision, we badly need some other independent data set for the very same stars. It is to be shown that precise stellar radial velocities, an unexploited – because almost unknown – parameter for flare stars, could solve the problem by supporting or disproving these faint objects’ cluster membership. Consequently the flare stars of these two kinds (which are accidentally mixed on the photographic plates) could be classified into different age groups and their evolutionary stages and tracks could be investigated more deeply.Our intention is to persuade astronomers involved in stellar radial velocity business that developing and using a method of high precision stellar radial-velocity measurement for late dK/dM stars is not a waste of time but a really feasible job and that we can and will contribute to the success of it by identifying the best tartgets, taking part in the necessary observations and evaluating the data.


2019 ◽  
Vol 157 (6) ◽  
pp. 216 ◽  
Author(s):  
Jennifer G. Winters ◽  
Todd J. Henry ◽  
Wei-Chun Jao ◽  
John P. Subasavage ◽  
Joseph P. Chatelain ◽  
...  
Keyword(s):  

2013 ◽  
Vol 8 (S299) ◽  
pp. 64-65
Author(s):  
Julien Rameau ◽  
Gaël Chauvin ◽  
Anne-Marie Lagrange ◽  
Philippe Delorme ◽  
Justine Lannier

AbstractWe present the results of two three-year surveys of young and nearby stars to search for wide orbit giant planets. On the one hand, we focus on early-type and massive, namely β Pictoris analogs. On the other hand, we observe late type and very low mass stars, i.e., M dwarfs. We report individual detections of new planetary mass objects. According to our deep detection performances, we derive the observed frequency of giant planets between these two classes of parent stars. We find frequency between 6 to 12% but we are not able to assess a/no correlation with the host-mass.


2018 ◽  
Vol 617 ◽  
pp. A108 ◽  
Author(s):  
T. Appourchaux ◽  
P. Boumier ◽  
J. W. Leibacher ◽  
T. Corbard

Context. The recent claims of g-mode detection have restarted the search for these potentially extremely important modes. These claims can be reassessed in view of the different data sets available from the SoHO instruments and ground-based instruments. Aims. We produce a new calibration of the GOLF data with a more consistent p-mode amplitude and a more consistent time shift correction compared to the time series used in the past. Methods. The calibration of 22 yr of GOLF data is done with a simpler approach that uses only the predictive radial velocity of the SoHO spacecraft as a reference. Using p modes, we measure and correct the time shift between ground- and space-based instruments and the GOLF instrument. Results. The p-mode velocity calibration is now consistent to within a few percent with other instruments. The remaining time shifts are within ±5 s for 99.8% of the time series.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tressy Thomas ◽  
Enayat Rajabi

PurposeThe primary aim of this study is to review the studies from different dimensions including type of methods, experimentation setup and evaluation metrics used in the novel approaches proposed for data imputation, particularly in the machine learning (ML) area. This ultimately provides an understanding about how well the proposed framework is evaluated and what type and ratio of missingness are addressed in the proposals. The review questions in this study are (1) what are the ML-based imputation methods studied and proposed during 2010–2020? (2) How the experimentation setup, characteristics of data sets and missingness are employed in these studies? (3) What metrics were used for the evaluation of imputation method?Design/methodology/approachThe review process went through the standard identification, screening and selection process. The initial search on electronic databases for missing value imputation (MVI) based on ML algorithms returned a large number of papers totaling at 2,883. Most of the papers at this stage were not exactly an MVI technique relevant to this study. The literature reviews are first scanned in the title for relevancy, and 306 literature reviews were identified as appropriate. Upon reviewing the abstract text, 151 literature reviews that are not eligible for this study are dropped. This resulted in 155 research papers suitable for full-text review. From this, 117 papers are used in assessment of the review questions.FindingsThis study shows that clustering- and instance-based algorithms are the most proposed MVI methods. Percentage of correct prediction (PCP) and root mean square error (RMSE) are most used evaluation metrics in these studies. For experimentation, majority of the studies sourced the data sets from publicly available data set repositories. A common approach is that the complete data set is set as baseline to evaluate the effectiveness of imputation on the test data sets with artificially induced missingness. The data set size and missingness ratio varied across the experimentations, while missing datatype and mechanism are pertaining to the capability of imputation. Computational expense is a concern, and experimentation using large data sets appears to be a challenge.Originality/valueIt is understood from the review that there is no single universal solution to missing data problem. Variants of ML approaches work well with the missingness based on the characteristics of the data set. Most of the methods reviewed lack generalization with regard to applicability. Another concern related to applicability is the complexity of the formulation and implementation of the algorithm. Imputations based on k-nearest neighbors (kNN) and clustering algorithms which are simple and easy to implement make it popular across various domains.


2018 ◽  
Vol 11 (2) ◽  
pp. 53-67
Author(s):  
Ajay Kumar ◽  
Shishir Kumar

Several initial center selection algorithms are proposed in the literature for numerical data, but the values of the categorical data are unordered so, these methods are not applicable to a categorical data set. This article investigates the initial center selection process for the categorical data and after that present a new support based initial center selection algorithm. The proposed algorithm measures the weight of unique data points of an attribute with the help of support and then integrates these weights along the rows, to get the support of every row. Further, a data object having the largest support is chosen as an initial center followed by finding other centers that are at the greatest distance from the initially selected center. The quality of the proposed algorithm is compared with the random initial center selection method, Cao's method, Wu method and the method introduced by Khan and Ahmad. Experimental analysis on real data sets shows the effectiveness of the proposed algorithm.


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