Training

Deep learning (DL) is a family of techniques widely used in multiple fields with excellent results. Unfortunately, due to its steep learning curve, its use is not as extended as desirable. Several domain-specific libraries have been developed to facilitate the use of these DL models with modest success. For the widespread adoption of these techniques, researchers should be able to design and use their own DL models. Image classification is one of the main problems in astrophysics. The de facto standard to tackle this problem is Convolutional Neural Networks (CNN), a concrete deep learning architecture. A good knowledge of this technique is essential for its proper application by researchers. These three sessions have been designed with a goal in mind: to gain confidence in using CNNs for image classification tasks. The tutor of this school is Dr Francisco Eduardo Sanchez Karhunen (Universidad de Sevilla).

This event is a discussion session on applications of near-infrared (NIR) interferometry. It will be given by Maria Koutoulaki.

Maria Koutoulaki is an expert in near-infrared (NIR) interferometry and her main research interests are in star formation and protoplanetary disks. We think it may be interesting for the IAA community, especially for PhD students, to learn more about this type of observations and see if one could apply it to their own projects.

We therefore invite you to this informal session on the 2nd of October at 10:00 at the sala polivalente next to the kitchen. The session should last about 1h. We encourage the students to attend and ask lots of questions!

Content

The main topics will be:

1) What is near-infrared interferometry?

2) What are the facilities out there?

3) Young Stellar Objects as seen through the eye of VLTI?

4) What can I do with it?

5) Discussion

SKA will be a world-leading facility and as such it aims to follow and lead best practices in scientific integrity such as those promoted by the Open Science movement. The concept of Open Science facilitates reproducibility of scientific studies by making data and methods more accessible, understandable and reusable. Furthermore, Open Science contributes to democratising information and to reducing inequalities in the access to infrastructures, hence impacting areas related to some of the United Nations Sustainable Development Goals. Open Science is rooted in SKA’s foundational principles and the reproducibility of SKA science data products is one of the Observatory metrics for scientific success. The SKA Regional Center Network (SRCNet) will play a key role enabling big scientific collaborations and it will provide the framework to implement SKA Open Science policies. The school will provide a general overview of different aspects of Open Science and how it is connected to collaborative and sharing practices that are encouraged by the SKA Observatory. This school brings an opportunity to anyone from the astronomy and the SRCNet community to have a complete and detailed view of Open Science policies, tools, as well as the ongoing activities related to Open Science at the SKAO and the SRCNet. International experts will provide a detailed and updated view on each of the school topics, and lectures as well as tutorials and hands-on sessions will facilitate our understanding from a practical point of view, encouraging questions and discussion.​​