311 citations · 1.1k across the 25 of their papers we have counts for
8 papers · 2 filters
Weighing the Galactic disk in sub-regions of the solar neighbourhood using Gaia DR2
Axel Widmark, Pablo Fernández de Salas, Giacomo Monari
We infer the gravitational potential of the Galactic disk by analysing the phase-space densities of 120 stellar samples in 40 spatially separate sub-regions of the solar neighbourh…
Perturbed distribution functions with accurate action estimates for the Galactic disc
H. Al Kazwini, Q. Agobert, A. Siebert +8
In the Gaia era, understanding the effects of the perturbations of the Galactic disc is of major importance in the context of dynamical modelling. In this theoretical paper we exte…
The ACTIONFINDER: An unsupervised deep learning algorithm for calculating actions and the acceleration field from a set of orbit segments
Rodrigo Ibata, Foivos Diakogiannis, Benoit Famaey +1
We introduce the "ACTIONFINDER", a deep learning algorithm designed to transform a sample of phase-space measurements along orbits in a static potential into action and angle coord…
Charting the Galactic acceleration field I. A search for stellar streams with Gaia DR2 and EDR3 with follow-up from ESPaDOnS and UVES
Rodrigo Ibata, Khyati Malhan, Nicolas Martin +10
We present maps of the stellar streams detected in the Gaia Data Release 2 (DR2) and Early Data Release 3 (EDR3) catalogs using the STREAMFINDER algorithm. We also report the spect…
The bar resonances and low angular momentum moving groups in the Galaxy revealed by stellar ages
Chervin F. P. Laporte, Benoit Famaey, Giacomo Monari +3
We use the second Gaia data release in combination with the catalog of Sanders & Das (2018) to dissect the Milky Way disc in phase-space and relative ages. We confirm and report th…
The RAdial Velocity Experiment (RAVE): Parameterisation of RAVE spectra based on convolutional neural networks
G. Guiglion, G. Matijevic, A. B. A. Queiroz +22
In the context of large spectroscopic surveys of stars, data-driven methods are key in deducing physical parameters for millions of spectra in a short time. Convolutional neural ne…