Found 2 talks width keyword ISM atoms

Artificial intelligence techniques are increasingly used in our daily lives. They also play an important role in science, including astrophysics. I am particularly interested in the use of machine learning regressors. I will present an overview of the current situation and some recent uses of these methods in the study of planetary nebulae or HII regions.

A simple model using the balance of photodissociation assuming a one-dimensional plane-parallel model yields total hydrogen volume densities for a column of atomic hydrogen under the influence of a far-ultraviolet radiation field. This can be applied wherever atomic hydrogen can be assumed to be the product of photodissociation, or perhaps where it is being kept in its atomic state because of the local radiation field. I have previously applied this model to the nearby spiral galaxies M33, M81 and M83 in the past, but the application is mostly manual and cumbersome. In order to make this method suitable to apply to larger samples of galaxies, we developed an automated procedure that identifies candidate PDRs, calculates the balance of photodissociation at locations where PDR-produced HI can be expected and provides total hydrogen volume densities. We applied the procedure to M83 as a consistency check. It is also ready to take advantage of the latest integral field spectroscopy data (metallicity), which we did in the case of M74. In principle this procedure is most suitable to probe the diffuse interstellar medium at the edges of HII regions in other galaxies than our own. However, if detailed morphological information is already available, we can improve our understanding of the method by applying it to very specific cases, such as parts of the Taurus molecular cloud. While the results are highly sensitive to the local morphology, they can potentially be used as an independent probe of the molecular gas.