Physics Informed Machine Learning in Astronomy


Machine Learning had a significant success in Astronomy in recent years, and it becomes obvious that useful applications of ML require a tight connection to physical modelling. In this session, we will explore several aspects of imbuing physics as part of a ML model, from building hybrid models that merge both deep learning and physical models, to using known physical symmetries and equivariances to design dedicated neural architectures.

Organisers: Francois Lanusse (Chair) & Jean-Luc Starck (Committee member)

Session Schedule

17:30 - 17:50 Invited talk -
Combining Physics and Machine Learning in Astronomy
François Lanusse
17:50 - 18:10 Invited talk -
Data-driven analysis of strong gravitational lenses: opportunities and challenges for machine learning
Laurence Perreault-Levasseur
18:10 - 18:30 Invited talk -
Multiscale modeling of galaxy-scale strong lenses
Aymeric Galan*
18:30 - 18:50 Invited talk -
Rethinking data-driven point spread function modeling with a differentiable optical model
Tobias Liaudat*
18:50 - 19:10 Invited talk -
Learning the Galaxy-Halo Connection by Imposing Exact Physical Symmetries
Kate Storey-Fisher*
19:10 - 20:30 Invited poster -
Sampling high-dimensional inverse problem posteriors with neural score estimation
Benjamin Remy*
19:10 - 20:30 Contributed poster -
Deep Learning-based galaxy image deconvolution
Utsav Akhaury*
19:10 - 20:30 Contributed poster -
Scalable and equivariant spherical CNNs by discrete-continuous (DISCO) convolutions
Jason McEwen
19:10 - 20:30 Contributed poster -
Delensing Gravitational Lensing Effects with Physics-Informed Neural Networks
Ayoub Tajja*