Title: Student intro training on Python for data processing of AGN variability within the Large Survey of Space and Time (LSST)

Time: March 26, 2022

Lecturers: Andjelka Kovacevic, Dragana Ilic, Robert Nikutta, Paula Sanchez

Summary and Topics:

The Rubin Observatory Legacy Survey of Space and Time (LSST) will peer into space like no experiment has done before. LSST will be an unparalleled wide-field astronomical survey of our universe—wider and deeper in volume than all previous surveys combined.

The Legacy Survey of Space and Time (LSST) is a 10-year long project of the Vera C. Rubin Observatory, which will open the window of dynamical Universe and deliver a 500 petabyte set of images and data (for details see https://www.lsst.org/)

It is expected that the LSST will observe more than six million of active galactic nuclei (AGN). AGN are powered by the accretion onto the most compact objects – supermassive black holes (SMBH). Studying these objects will help us to understand the physical process in the vicinity of SMBH, probing general relativity as well as the formation and evolution of galaxies, which are the main building blocks of the Universe.

This workshop is aimed at students in physics and astronomy majors at all levels of software development skills (e.g., Python, or other programming languages). Within this workshop we aim to train students in using Python-based tools to investigate the variability of AGN and prepare for the coming most important sky survey, the LSST. The tutorial will also touch on the relevant mathematical foundations, for instance Fourier transform and cross-correlation analysis.

The workshop will be organized in two sessions covering the general introduction to the LSST and to AGN variability, followed by the presentation of NOIRLab’s Astro Data Lab science platform, which provides open access to remote compute capabilities and to large datasets, including in the future to LSST data products. Each presentation will be accompanied with several tutorials, based on Python and Jupyter notebooks.

Format: Online, through Zoom Platform

Material: All preparation materials and connection details will be circulated to registered participants well in advance.

Registration: Please contact (email of seblu1557@gmail.com)

Credits:

This student training is part of the activities of “Building Deep Learning Engine (DLE) for AGN light-curves” project, which is graciously supported by a grant from the 2021 LSST Corporation Enabling Science Call for

Proposals. https://github.com/LSST-sersag/dle