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20162023
most citedPhysics Of Eclipsing Binaries. II. Towards the Increased Model Fidelity

326 citations · 476 across the 4 of their papers we have counts for

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7 papers · 1 filter

astro-ph.IM2018

Gaia Data Release 2. Short-timescale variability processing and analysis

M. Roelens, L. Eyer, N. Mowlavi +13

The Gaia DR2 sample of short-timescale variable candidates results from the investigation of the first 22 months of Gaia photometry for a subsample of sources at the Gaia faint end…

astro-ph.IM2018

Gaia Data Release 2: Properties and validation of the radial velocities

D. Katz, P. Sartoretti, M. Cropper +51

For Gaia DR2 (GDR2), 280 million spectra, collected by the RVS instrument on-board Gaia, were processed and median radial velocities were derived for 9.8 million sources brighter t…

astro-ph.IM2018

Gaia Data Release 2: Photometric content and validation

D. W. Evans, M. Riello, F. De Angeli +9

Aims. We describe the photometric content of the second data release of the Gaia project (Gaia DR2) and its validation along with the quality of the data. Methods. The validation w…

astro-ph.IM2017

Gaia eclipsing binary and multiple systems. Two-Gaussian models applied to OGLE-III eclipsing binary light curves in the Large Magellanic Cloud

N. Mowlavi, I. Lecoeur-Taïbi, B. Holl +11

The advent of large scale multi-epoch surveys raises the need for automated light curve (LC) processing. This is particularly true for eclipsing binaries (EBs), which form one of t…

astro-ph.IM2017

Gaia Eclipsing Binary and Multiple Systems. A study of detectability and classification of eclipsing binaries with Gaia

A. Kochoska, N. Mowlavi, A. Prsa +5

In the new era of large-scale astronomical surveys, automated methods of analysis and classification of bulk data are a fundamental tool for fast and efficient production of delive…

astro-ph.IM2017

Gaia eclipsing binary and multiple systems. Supervised classification and self-organizing maps

M. Süveges, F. Barblan, I. Lecoeur-Taïbi +6

Large surveys producing tera- and petabyte-scale databases require machine-learning and knowledge discovery methods to deal with the overwhelming quantity of data and the difficult…