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Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers
D. Huppenkothen, M. Ntampaka, M. Ho +19
Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of…
Gaia Data Release 3: External calibration of BP/RP low-resolution spectroscopic data
P. Montegriffo, F. De Angeli, R. Andrae +46
Context. Gaia Data Release 3 contains astrometry and photometry results for about 1.8 billion sources based on observations collected by the European Space Agency (ESA) Gaia satell…
Gaia Data Release 3: Processing and validation of BP/RP low-resolution spectral data
F. De Angeli, M. Weiler, P. Montegriffo +51
(Abridged) Blue (BP) and Red (RP) Photometer low-resolution spectral data is one of the exciting new products in Gaia Data Release 3 (Gaia DR3). We calibrate about 65 billion indiv…
Selection Functions in Astronomical Data Modeling, with the Space Density of White Dwarfs as Worked Example
Hans-Walter Rix, David W. Hogg, Douglas Boubert +6
Statistical studies of astronomical data sets, in particular of cataloged properties for discrete objects, are central to astrophysics. One cannot model those objects' population p…
An astronomical institute's perspective on meeting the challenges of the climate crisis
Knud Jahnke, Christian Fendt, Morgan Fouesneau +10
Analysing greenhouse gas emissions of an astronomical institute is a first step in reducing its environmental impact. Here, we break down the emissions of the Max Planck Institute…
A Gaia early DR3 mock stellar catalog: Galactic prior and selection function
Jan Rybizki, Markus Demleitner, Coryn Bailer-Jones +7
We present a mock stellar catalog, matching in volume, depth and data model the content of the planned Gaia early data release 3 (Gaia EDR3). We have generated our catalog (GeDR3mo…