Determination of the range of masses for the compact object in binary r-X 2S 0921-630
Distribution of the masses of compact objects in X ray binaries. Neutron stars have a mass of around 1.4 solar masses, with very slight dispersion. The average mass of a black hole is around 10 solar masses, and all have a mass greater than 4 solar masses
The range of masses for the compact object in binary r-X 2S 0921-630 has been determined (between 2.0-4.3 solar masses), consistent with a massive neutron star or a low-mass black hole.
H II regions are ionized nebulae associated with the formation of massive stars. They exhibit a wealth of emission lines in their spectra that form the basis for estimation of chemical composition. The amount of heavy chemical elements is essential to the understanding of important phenomena such as nucleosynthesis, star formation and chemical evolution of galaxies. For over 80 years, however, a discrepancy exists of a factor of around two between heavy-element abundances (the so-called metallicity) derived from the two main kinds of emission lines that can be measured in nebular spectra
The transient Swift J1727.8-162 is the latest member of the X-ray binary black hole family to be discovered. They are formed by a black hole and a low-mass star whose gas is stripped off and accreted to the black hole via an accretion disc. The high temperature of the accretion disc makes it shine in all energy bands up to X-rays, and is particularly bright during epochs known as outbursts. In this novel study, published just a few months after the discovery of the system, we present 20 epochs of optical spectroscopy obtained with the GTC-10.4m telescope. The spectra cover the main accretion
The amount and complexity of data delivered by modern galaxy surveys has been steadily increasing over the past years. New facilities will soon provide imaging and spectra of hundreds of millions of galaxies. Extracting coherent scientific information from these large and multi-modal data sets remains an open issue for the community and data-driven approaches such as deep learning have rapidly emerged as a potentially powerful solution to some long lasting challenges. This enthusiasm is reflected in an unprecedented exponential growth of publications using neural networks, which have gone