Training

The school is designed for researchers at all career stages who are particularly interested in the formation and evolution of galaxies. The school is ideally positioned to offer an up-to-date scientific perspective on the recent exciting findings in extragalactic research, especially from the JWST and other space- and ground-based facilities.

An exciting and bright future for observational astronomy and theoretical advancements awaits the generations of young astronomers, thanks in large part to the array of powerful facilities available currently and also in the near future. This is the second in a series of schools that provide a comprehensive overview of the state of the art in this field.

The school consistently goes beyond standard textbook knowledge, offering valuable insights into the current frontiers of research in both theory and observation. It strongly promotes personal interaction between lecturers, tutors, and participants, fostering a welcoming environment ideal for learning and sharing knowledge.

The lectures and tutorials will be highly interactive, allowing ample time for questions and discussion. To support this, several panel discussions and afternoon tutorials will be scheduled throughout the week, encouraging a collaborative atmosphere. Importantly, all participants are strongly encouraged to present their ongoing work and/or future projects, either in our dedicated speakers' corner or during the panel sessions. 

Important Dates:

• Pre-Registration Opens: November 4th, 2024
• Pre-Registration Deadline: March 16th, 2025
• Notification to Accepted Participants: before March 31st, 2025

Approximately 75% of known exoplanets have been discovered by identifying transit signals in the light curves of their host stars. These discoveries start with the acquisition of raw images of the sky, followed by the extraction of stellar time-series photometry. In this workshop, we will process ground-based observations of the star TRAPPIST-1 to replicate the detection of its first exoplanet transit using original data from the TRAPPIST telescope. Participants will learn to calibrate raw images and extract TRAPPIST-1's light curve using prose, a Python package designed for modular image processing in astronomy. Finally, we will apply nuance, a detection algorithm tailored for identifying transits in noisy datasets. 

Overall, participants will gain hands-on experience in image calibration, light curve extraction, and transit detection, along with practical skills in using Python tools for astronomical data analysis.

This course is taught by Lionel García (Center for Computational Astrophysics, Flat Iron Institute, New York)

This session is a hands-on introduction to using GitHub for scientific research collaboration. We will explore software repositories and their role in managing and sharing research work within a group. Key concepts—such as commits, forks, pushes, pulls, and pull requests—will be clarified with practical examples of their use. Participants will also receive practical guidance on repository management and effective collaboration techniques. The session includes a hands-on component where attendees will create repositories and work together to build and populate a shared repository using GitHub’s tools.

This course is taught by Manuel Parra and Javier Moldón

The Python Blob Detector and Source Finder (PyBDSF) is a CASA-based software designed for source detection in radio-interferometric images. Originally developed for the Low Frequency Array (LOFAR), PyBDSF is designed to work on a wide range of scales, cataloguing different types of sources and allowing post-extraction filtering depending on the user’s scientific purposes. PyBDSF works on images produced by any radio-interferometer. In short, PyBDSF loads the image in fits or CASA format, computes some basic image statistics (rms and mean), and calculates a threshold to separate source from noise pixels with different algorithms that can be chosen by the user. Then, the program identifies islands of emission which are fitted with one or multiple Gaussians. The resulting source catalogue, which can be written into different formats (ascii, fits, txt,…) for subsequent analysis, provides key source properties such as astrometry, integrated and peak fluxes, source geometry, etc. In addition, internally derived images (rms noise maps, Gaussian residual and models, etc) are written in commonly used formats (fits or CASA) if specified by the user. It is possible to work with PyBDSF interactively in a terminal, or via Python scripts or notebooks. PyBDSF can work with all Stokes parameters and compute the polarisation percentage and angle for each source. Also, PyBDSF can compute the spectral indices of both extended and unresolved sources provided a frequency cube is input. Finally, PyBDSF can estimate the PSF spatial variation across the image and correct it. This is of special interest for low frequency observations (such as those of LOFAR) which are more prominently affected by ionospheric effects.

This course will be taught by Miguel Cano (IAA-CSIC)

Spectral Energy Distributions (SEDs) play a vital role in understanding the processes of star formation, especially in the context of massive protostars. The sedcreator Python package has been developed to streamline the construction and fitting of SEDs. This tool is developed in the context of massive star formation theories, particularly Core Accretion. It leverages data from the SOMA survey and the Zhang&Tan model grid, based on the Turbulent Core model, to provide a comprehensive approach to SED analysis. With this package, users can easily measure the flux on their selected object and fit these fluxes to a grid of models. This process allows for the estimation of critical physical properties associated with the forming protostar, such as the mass of the central protostar. The sedcreator package represents a significant step forward in the study of SEDs in the context of star formation and massive stars.

This course will be taught by Rubén Fedriani. 

This course of our IAA SO Training programme will focus on how to impart scientific presentations. It will consist of a one hour lecture followed by up to four practical sessions every two weeks.

The participants have the opportunity to impart presentations that they have either given recently or that they are preparing for upcoming events. The SO Training team (coordinator and/or co-coordinators) as well as the audience will provide the speaker with feedback. Up to two presentations (not longer than 25 min) can be accommodated per session. The participation in the practical sessions is optional, but all participants are strongly encouraged to impart a presentation in the practical sessions and also to attend those by others to learn and help with feedback.

The sessions will take place bi-weekly on Tuesday afternoon.

Lecturer: Rainer Schödel, IAA-CSIC

Within the  Severo Ochoa Training Initiative of the IAA-CSIC we are offering the 2024 edition of "English for Academic Purposes", imparted by Ms. Marie Waltie of the University of Leipzig. The workshop will be in-person. Key aspects of the use of English for academic purposes will be highlighted and correct usage encouraged. Participants will comprehensively expand their competence in the academic use of English. The course will cover the following topics: 

• Common Errors in Academic English
• Writing up Results and Methods in English
• Pronunciation Practice in English
• Academic Discussions in English
• Presenting your Academic Project in English

Fundamental knowledge of the English language as well as high motivation of the participants is a requirement because the course will be interactive. We can accommodate up to 30 participants. In case of over-subscription, we will give preference to students and young postdocs and/or may lead selection interviews. If you are interested in participating, please register before 31 January 2024 indicating whether you are interested in an INDIVIDUAL session of 20-25 min.

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.​​