Astronomy and Artificial Intelligence – Online Event

Image generated in Bing Copilot using Microsoft Designer’s Image Creator, powered by DALL-E 3. Prompt: “radio telescope with a starry night sky background.”

As part of the 2024 STFC Astronomy and AI summer school, we also ran an online event aimed at a wider cohort of students who have an interest in AI and astronomy. This event took place the week prior to the summer school, on Wednesday 3rd July, and featured panel discussions on broader questions about AI, alongside the release of a number of recorded case studies showcasing how AI is being utilised by astronomers today.

Material from the event was also made public after the summer school for those unable to attend, along with a shortened compilation video to provide an overview of the variety of AI uses in astronomical research.

This event was organised by Dr James Pearson (The Open University) and Prof Berry Billingsley (CCCU).


09.15-10.15 Introduction by Dr Marc Sarzi
10.15-10.30 Break
10.30-12.00 Panel Discussion I – What is the Future of Astronomy with AI?
12.00-13.00 Lunch Break
13.00-14.00 Talk on Generative AI by Prof Berry Billingsley
14.00-15.30 Panel Discussion II – What Relevance can Generative AI have within Astronomy?

Panel Discussions

The following lists the speakers participating in the panel discussions for the online event. These discussions tackled the broader questions of AI and its use in both astronomy and in a wider context.

Panel Discussion I – What is the Future of Astronomy with AI?

Host: Dr Marc Sarzi
Panel Discussion II – What Relevance can Generative AI have within Astronomy?

Host: Prof Berry Billingsley
Ingo Waldmann (University College London)Josh Wilde (The Open University)
Xinyue Sheng (Queen’s University Belfast)Weiguang Cui (University of Edinburgh)
Benjamin Joachimi (University College London)Kevin Walsh (Westminster School)

Case Studies of AI in Astronomy

A number of researchers and PhD students kindly provided recorded talks of how they utilise AI in their own work. These case studies are listed below, including brief descriptions for each. They cover a variety of research topics and types of AI, so are definitely worth a watch!

Click the titles by each name to view abstracts for the talks:

Mike Walmsley (University of Toronto)

Computer vision for galaxy images

Modern telescopes take far more images than humans could ever look through, and so we rely on automated methods to measure what galaxies look like. AI is an excellent tool for solving this visual recognition problem. I’ll talk about how AI models have changed over the last few years to learn efficiently and quickly adapt to new measurement tasks.

Josh Wilde (The Open University)

Using machine learning to find gravitational lenses & how it can go wrong

I will discuss the uses of CNNs to identify gravitational lenses in simulated and real data, discussing common flaws that machine learning models suffer from and what can cause these issues. I will discuss the use of interpretability methods to identify what features within the image the machine learning techniques associate with gravitational lensing. I will explain how the need of interpretability in machine learning has led me to use U-nets to help locate gravitational lenses in images.

Ruby Pearce-Casey (The Open University)

Using cGANs for Anomaly Detection: Hunting for Gravitational Lensing Systems in Euclid

Gravitational lensing is a powerful tool that directly probes all clustering components in the universe through their gravitational effect on light from distant background sources. The problem arises in finding gravitational lenses, and, with the accelerated growth in data volume and complexity in astronomy, machine-learning-aided lens searches have proven successful.

We present a proof of concept for an alternative method of strong gravitational lens finding using a conditional Generative Adversarial Network (cGAN). We use Early Release Observation (ERO) images of the Perseus Cluster from Euclid, covering 0.57 sq. degrees on the sky, and the network is based on the pix2pix architecture with an adapted U-Net generator. We train our model to predict Euclid’s NISP-H band flux (1.54-2.00µm) from a combination of the filters NISP-J, NISP-Y and VIS band (0.55-1.54µm) in 40,000 cut-outs from the Perseus Cluster which are 20×20 arcseconds in size. We test the cGAN on 5,000 cut-outs from the Perseus cluster, 10% of which contain a simulated strong gravitational lens painted into the cut-out based on a Singular Isothermal Ellipsoid model. Candidate gravitational lenses and cut-outs with a gravitational lens painted in were deliberated excluded from the model’s training data set such that gravitational lensing systems remain unknown to the network. We find that the cGAN can accurately predict the NISP-H band flux of the cut-outs from the Perseus cluster. However, the model fails to predict the NISP-H band flux of the cut-outs containing the simulated gravitational lenses, with a larger difference between the model’s prediction and ground truth for lenses with extended arcs and Einstein rings, suggesting that the cGAN can be used as an anomaly detector for an alternative method of lens finding.

Ingo Waldmann (University College London)

Machine learning in exoplanet characterisation

The upcoming decade promises remarkable progress in our grasp of exoplanet formation, evolution, and an in-depth assessment of their climates and possible habitability. With the James Webb Space Telescope now in orbit, we’re at the threshold of high-precision atmospheric studies of these distant worlds. This milestone, combined with forthcoming endeavours like the ELTs and the Ariel space mission, positions us in an era rich in high-precision exoplanetary data.
This vast influx of precise data holds immense discovery prospects but also introduces challenges in handling both model intricacy and data volume. As machine and deep learning techniques gain traction across various scientific and industrial domains, they also find their footing in exoplanetary and solar system research. Such techniques offer a refreshing edge in modelling intricate, non-linear data over conventional methods. In my talk’s initial segment, I’ll explore the use of simulation-based inference, deep surrogate models, and Explainable AI in refining time-series data and leveraging inverse modelling to extract exoplanet attributes from observations.

In the second part, I’ll delve into our machine-learning-centric data challenges. It’s noteworthy that many challenges in exoplanet data analysis resonate with issues keenly explored in the machine learning realm. Yet, interdisciplinary collaboration has often been stymied by language barriers and domain unfamiliarity. Addressing this, as part of the ESA Ariel Space mission, we have organised four machine learning challenges, hosted at prestigious AI conferences like NeurIPS and ECML, attracting hundreds of participating teams annually. I will discuss how such challenges can catalyse rapid, sturdy AI advancements in this budding domain and pave the way for more resilient AI solutions in the future.

Xinyue Sheng (Queen’s University Belfast)

NEural Engine for Discovering Luminous Events (NEEDLE): identifying rare transient candidates in real time from host galaxy images

The work is to develop a machine learning classifier (NEEDLE) for identifying superluminous-supernovae and tidal disruption events at their early stage, using a few detections and the context information. It is currently applied on Lasair broker digesting ZTF alerts, and in the future, it will deal with LSST alerts.

Weiguang Cui (University of Edinburgh)

How AI helps with constraining galaxy cluster mass

Machine learning has been widely used in different astronomy researches recently. Some of its models, especially the image to property feature, make it the perfect tool for solving some particular problems in astronomy, such as estimating the cluster mass from observation images. In this showcase, I present how the different ML models are adopted to estimate the cluster mass in different dimensions — from a signal cluster mass M_500 (0d data point, see de Andres et al. 2022), to cluster mass within different radii (1D density profile, Ferragamo et al. 2023) and projected cluster mass map (2D image, de Andres et al. 2024). With this powerful tool, we are able to probe the mystery Universe, deeply, completely and swiftly!

Benjamin Joachimi (University College London)

Forward-modelling the Universe with neural density estimation

I provide an overview of recent work constraining cosmological parameters from two of the largest galaxy imaging surveys to date, the Dark Energy Survey and the Kilo-Degree Survey. Both analyses use a novel approach to inference: machine-learning the likelihood using neural density estimators and large suites of large-scale structure simulations. I’ll include brief introductions to weak gravitational lensing, on which the analyses are based, and to the concepts of simulation-based/likelihood-free inference.

Getting Started with AI in Astronomy

With the field of AI and deep learning ever expanding and evolving, it can be daunting at first to figure out where to start and difficult to understand what might be of use to you.

The GitHub repository here provides links and information about AI, covering general concepts, how artificial neural networks work, types of deep learning, tools and packages, and examples of how AI has been used in the field of astronomy. It is non-exhaustive but can hopefully act as an initial guide.

– Repository created by Dr James Pearson (The Open University)


Registration is now closed.

If you have an interest in astronomy and AI then feel free to apply, whether you are just beginning your explorations into AI or already using AI in your research.

Eligibility. As well as those attending the summer school, those eligible to register are:
a) astronomy PhD students,
b) final year undergraduate and master’s students in physics or computer science.
You do not need to be STFC-funded to register. Female students are especially encouraged to apply.

Cost. The online event is completely free! Costs for the summer school are on the summer school page.

Summer school attendees will automatically have access to the online event – if you wish to register for the summer school, see the summer school page.

If only registering for the online event, please provide the following information:

  • Your full name & pronouns.
  • Your associated university.
  • Whether you are an astronomy PhD student, a final year undergraduate or master’s student in physics or computer science, and if you are STFC-funded.
  • Confirmation that you only wish to register for the online event.