CaII triplet of RGB from VLT/FLAMES obs. (Battaglia+, 2008)

Bibcode
2008yCat..73830183B
Battaglia, G.; Irwin, M.; Tolstoy, E.; Hill, V.; Helmi, A.; Letarte, B.; Jablonka, P.
Bibliographical reference

VizieR On-line Data Catalog: J/MNRAS/383/183. Originally published in: 2008MNRAS.383..183B

Advertised on:
4
2008
Number of authors
7
IAC number of authors
0
Citations
0
Refereed citations
0
Description
The data sets presented here were collected between 2003 August and 2005 November. They consist of 15 pointings in the Sculptor dSph and 11 in the Fornax dSph spread over the galaxies. Some fields were observed with 1h exposure time, whilst other fields have repeated exposures of shorter integration time and two different plate set-ups, with the aim of testing the reliability of the derived velocities, EWs and the stability of the instrument. All the low-resolution CaT observations were made using VLT/FLAMES in Medusa mode. (2 data files).
Type
Non-refereed
Galaxies
Spectroscopy

It may interest you

  • Non-refereed
    CONCERTO: Instrument and status
    CONCERTO (CarbON CII line in post-rEionization and ReionizaTiOn) is a low-resolution Fourier transform spectrometer dedicated to the study of star-forming galaxies and clusters of galaxies in the transparent millimeter windows from the ground. It is characterized by a wide instantaneous 18.6 arcmin field of view, operates at 130-310 GHz, and was
    Fasano, Alessandro et al.

    Advertised on:

    6
    2024
    Bibcode
    2024EPJWC.29300018F
    Citations
    0
  • Non-refereed
    Generating galaxy clusters mass density maps from mock multiview images via deep learning
    Galaxy clusters are composed of dark matter, gas and stars. Their dark matter component, which amounts to around 80% of the total mass, cannot be directly observed but traced by the distribution of diffused gas and galaxy members. In this work, we aim to infer the cluster's projected total mass distribution from mock observational data, i.e. stars
    de Andres, Daniel et al.

    Advertised on:

    6
    2024
    Bibcode
    2024EPJWC.29300013D
    Citations
    0
  • Non-refereed
    A machine learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel'dovich maps with The Three Hundred clusters
    Our study introduces a new machine learning algorithm for estimating 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel'dovich (SZ) effect maps. We generate mock images from 2522 simulated clusters, employing an autoencoder and random forest in our approach. Notably, our model makes no prior assumptions
    Ferragamo, A. et al.

    Advertised on:

    6
    2024
    Bibcode
    2024EPJWC.29300019F
    Citations
    0