The XXVI Canary Islands Winter School of Astrophysics, organized by the Instituto de Astrofisica de Canarias (IAC), focuses on the application of Bayesian methods to inference in Astrophysics. The WS welcomes a maximum of 50 PhD students and young Post-Docs, and provides a unique opportunity for the participants to broaden their knowledge in this field of Astrophysics.
Our goal as scientists is to decipher the secrets of nature. We do this by developing physical models based on our current knowledge of the Universe. These models are used to make testable predictions, which are then compared with all kinds of observations. Due to the inherent specificities, a large fraction of the work in Astronomy and Astrophysics occurs just at this border, where one has to carry out the comparison between the observations and the models. Observations are always limited in number and accuracy, and the comparison has to be done very carefully. Fortunately, a set of mathematical tools, currently known as “Bayesian inference” have been developed in the last centuries to facilitate and formalize this work at the interface between data and models.
Our view of the Universe is imperfect, because information from observations is always incomplete and uncertain. The presence of noise and limitations in our measurement apparatus, together with our limited knowledge of the phenomena under study produce that comparing models and observations is not an easy task. Uncertainty, degeneracies and ambiguities plague our results and one has to be fully aware of them to give reliable results that help advance the science. The most important consequence of the previous points is that inference has to be carried out probabilistically: we will not be able to be sure of a conclusion with 100% certainty. Additionally, we are not completely blind during inference: we always have some a-priori knowledge about the parameters of the model that we need to make explicit in our line of reasoning. All the previous points are natural part of Bayesian inference and its extreme power has been and is being put in operation in the latest years.
This Winter School aims to bring together in a relaxed working atmosphere a number of the leading scientists working in this field and PhD students and recent postdocs. It will tackle many aspects of Bayesian Astrophysics, covering the fundamentals, the tools and applications to observations in different fields. The School is particularly designed to introduce young researchers in the use of Bayesian inference as a tool in their current work. According to this objective, several laboratory sessions are planned for students.
- Andrés Asensio Ramos
- Íñigo Arregui
- Antonio Aparicio
- Rafael Rebolo