JACS 2023

Session 1: A Survival Guide to Python - From Zero to Hero

Soheb Mandhai, 26.10.2023

Abstract: In this session, I'll go through the key components of Python programming. This will consist of a beginner-friendly introduction to scripting in Python. We will cover themes such as scopes, functions and classes, best practices to use, useful coding libraries, commenting, debugging and traceback, and a brief note on environment control. Near the end of the session, I will cover the key pitfalls that other PhD students have faced in the past and pass on the wisdom gained.

Session 2: Scientific Publications and Latex

George Bendo, 02.11.2023

Abstract: This talk will review several topics related to paper writing. I will first review LaTeX, which is the mark-up language used for professional paper writing. I will then describe the typical outline of an astronomy paper. I then conclude with some writing tips and some discussion on the practical aspects of publishing a paper.

Session 3: Conferences and Networking

Fiona Porter, 09.11.2023

Abstract: Unsure how conferences work or intimidated by networking? This talk aims to give you some tips that'll help you out. In this session, I'll talk about how to find conferences that are relevant to your research, the basics of writing a conference abstract, and what you should do when you're accepted. I'll also discuss the basics of networking and how to make connections at conferences and beyond.

Session 4: An Introduction to Linux

Jacob Burba, 16.11.2023

Abstract: Do you think that 'The Terminal' is a 2004 hit film starring Tom Hanks? Does a 'super user' sound like a terrible name for a superhero? In this introductory JACS session, we will cover all you need to know to get started with Linux here at JBCA, covering the essential commands for your daily activities. Linux might feel a bit difficult and unfamiliar at first. Still, even after this short session, you should be comfortable with managing your files, dealing with some minor software issues and solving some small problems. We'll also cover what to do when you're stuck and where to ask for help.

Session 5: High Performance Computing

Anthony Holloway, 23.11.2023

Abstract: When you need more power than your desktop can provide, the next place to look is High Performance Computing. The session will look at what facilities we have available at JBCA and to what purposes they are dedicated. We will look at the methods and issues involved in moving from a serial code to a parallel code and how that can speed up your processing. There will also be a look at what options you have if your processing outgrows the JBCA facilities.

Session 6: Data Processing in Python

Soheb Mandhai, 30.11.2023

Abstract: As astrophysicists, we’re generally met with large quantities of data that we need to process and analyse. In this beginner-friendly session, I’ll cover the Pythonic methods required to load, process, manipulate, visualise, and export data. I’m aiming for this to be an interactive session, so there’ll be some exercises too. There’ll be additional emphasis on the usage of the Python package “Pandas”.

Session 7: Statistics for Astronomers

Avishek Basu, 07.12.2023

Abstract:

Session 8: The Bayesian Approach: Model Fitting and Selection

Michael Keith, 11.01.2024

Abstract: In this talk, I will give an overview of parameter estimation and model selection using a “Bayesian” methodology. The aim is to help you understand what is meant when people say they “use a Bayesian approach” and to help you see where such an approach may be helpful for your own work. I will also provide a demo for the emcee (parameter estimation) and dynesty (model selection) software packages.

Session 9: Open & Reproducible Science with Git and Containers

Alex Clarke, 18.01.2024

Abstract: In this session, I will talk about a variety of tools the community uses to make science open and reproducible. These include Git, Zenodo, Docker/Singularity containers

Session 10: An Introduction to Machine Learning

Micah Bowles, 01.02.2024

Abstract: As research becomes more data-intensive, machine learning methods have become core to many research directions within astronomy and astrophysics and beyond. I will touch on methods from decision trees to large language models and I will give an overview of machine learning in science and highlight considerations you need to make to decide what (if any) methods might be appropriate for your research. The content will be aimed at those with technical backgrounds who have not had much / any exposure to machine learning.

Title image background: Lovell Telescope, Jodrell Bank Observatory by Mike Peel; Jodrell Bank Centre for Astrophysics, University of Manchester.

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