23 September 2021Séminaire – Jan Rybizki (MPIA, Heidelberg)

© 2021 Observatoire Astronomique de Strasbourg | Webdesign et développement Alchimy.

Galaxy Modelling Tools: From mock catalogues, spurious parallax
solutions and chemical evolution

Abstract

With each data release the Gaia mission provides us with more observational constraints of the Milky Way. What we are able to infer from these is limited by our ability to model and reproduce the observables and compare them accurately to cleaned real data, while taking into account selection effects of the Gaia instrument and the stellar tracer population. I will give a short inventory of publicly available tools and models that can be used to produce mock observations and accurately compare these to Milky Way data. A few potential use-cases and efficient work-flows will be discussed and I will highlight a multi-star chemical modelling approach that can be used to infer the stellar IMF.

The advent of the Gaia data, in particular its 6D sample, has finally allowed us to precisely characterise the kinematic space throughout a significant portion of the Milky Way disc(s). In doing so, we are now able to quantify the properties of the different kinematic substructures and follow their changes with Galactocentric radius and azimuth. I will present the latest advances in this topic, discuss its implications on the global properties of the Galaxy and its history, and show how we can use this information to constrain the Milky Way gravitational potential.

In addition: The CO2 footprint of Supercomputing in Astronomy

While we know that CO2 emissions needs to be reduced to save the
lilvelihood of future generations, we, the astro community have not
formulated reduction goals yet. From recent analysis we know that
travelling, supercomputing and observing/office infrastructure
contribute the biggest share of our greenhouse gas emissions. In this
talk I will show results from our assessment at MPIA to raise awareness
of our CO2 footprint and will present ideas how to lower computing
related emissions in the future.