UV Spectroscopy of Metal-Poor Massive Stars in the Small Magellanic Cloud

Bibcode
2010ihea.book...19L
DOI
10.1007/978-90-481-3400-7_4
Lennon, D. J.
Bibliographical reference

The Impact of HST on European Astronomy, Astrophysics and Space Science Proceedings, Volume . ISBN 978-90-481-3399-4. Springer Science+Business Media B.V., 2010, p. 19

Advertised on:
2010
Number of authors
1
IAC number of authors
1
Citations
0
Refereed citations
0
Description
The Hubble Space Telescope has provided the first clear evidence for weaker winds of metal-poor massive stars in the Small Magellanic Cloud, confirming theoretical predictions of the metallicity dependence of mass-loss rates and wind terminal velocities. For lower luminosity O-type stars however, derived mass-loss rates are orders of magnitude lower than predicted, and are at present unexplained.
Type
Non-refereed

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