International Conference on Machine Learning for Astrophysics 3rd Ed. - ML4ASTRO3

Session

Time Domain And Transients

1 Sept 2026, 13:30

Aula Prima (University of Malta)

Conveners

Time Domain And Transients: I

• Filomena Bufano (Istituto Nazionale di Astrofisica (INAF))

62. [INVITED] AI for the Sky: SkAI Research and Physics-Informed Deep Learning for Stars (remote)

Vicky Kalogera

01/09/2026, 13:30

The coming decade of sky surveys will reveal the changing universe at a depth and scale we have never had. Meeting that opportunity takes more than better analysis — it reaches into how we choose what to observe, how we draw inference from what we collect, and how we model the physics underneath. I will introduce the NSF-Simons AI Institute for the Sky (SkAI), where astrophysicists, computer...

63. Speeding-up the search for anomalies in ZTF time series with interpretable machine learning

Emmanuel Gangler

01/09/2026, 14:10

Machine learning is often viewed as a black box when it comes to understanding its output, be it a decision or a score. Automatic anomaly detection is no exception to this rule, and quite often the astronomer is left to independently analyze the data in order to understand why a given event is tagged as an anomaly. Interpretable AI on the other hand provides clues to the analyst, often in the...

64. Using Flow Matching to study the sub-burst structure of short astronomical transients

Anja Schmit

01/09/2026, 14:30

Astronomical transients, including Fast Radio Bursts and Gamma-ray Bursts, are among the most energetic known phenomena in the universe, originating mostly from extragalactic distances. To gain deeper insight into their origin and nature, it is essential to analyse their substructure. A rapidly increasing number of observations allows for data-driven analysis of these transients. In this talk...

65. Bridging Difference Imaging and Supernova Physics with TimeGANs

Maria Zampella

01/09/2026, 14:50

Uncertainties in the mass-loss history model for stellar evolution hinder the development of a comprehensive understanding of the scenarios linking different supernova types to their progenitors.
This work aims to predict the physical parameters of the progenitor directly from time sequences of synthetic multi-band images. The focus is on redshift estimation.
We propose a generative method...

83. Leveraging Transformer Architectures for Scalable Exoplanet Transit Inference

Vikash Singh

01/09/2026, 15:10

Current exoplanet detection relies heavily on iterative sampling and box-searching, methods that face significant scaling challenges with the massive data volumes expected from next-generation surveys. This presentation introduces an exploratory concept: adapting Large Language Model (LLM) architectures to treat photometric light curves as "textual" sequences. By utilizing self-attention...

66. SELDON: A Foundation Model for Transients

Jack O'Brien

Next generation transient discovery surveys require accurate real-time analysis of evolving transients for followup prioritization from other surveys. SELDON (Supernova Explosions Learned by Deep ODE Networks) is a transient foundation model designed with this capability in mind, and more. Our model not only provides classifications and forecasts for the future evolution of transient...