Chairs: Federica Bianco

This session will feature talks and discussion on the application of machine learning (ML) and artificial intelligence (AI) to LSST science.

Please contact the session chair with any questions, or if you'd like to volunteer to speak or help with this session.

Contributed talks (7 minutes + 3 minutes for questions):

• Machine learning bias and its potential impact on LSST data products (Lior Shamir)
• DistClassiPy: Distance-Based Techniques for Classification and Anomaly Detection in Light Curves (Siddharth Chaini)
• Automatic generation of magnification maps for lensed quasars and supernovae using deep learning (Somayeh Khakpash)
• Deep Learning in Supernova Classification: Evaluating Model Efficacies (Willow Fox Fortino)
• Enhancing Transient Classification with LSST and 7-Dimensional Telescope: A Machine Learning Approach Using Single Epoch SEDs (Gregory Sung Hak Paek)
• Discovering Sub-Second Transients with Continuous-Readout Images and Deep Learning (Shar Daniels)

Flash talks (for posters (2 minutes:

• Neural network prediction of model parameters for strong lensing samples from Hyper Suprime-Cam Survey (Priyanka Gawade)
• MADNESS: Maximum-A-posteriori solution with Deep generative NEtworks for Source Separation (Biswajit Biswas)

Discussion: what are the most pressing needs to support and facilitate ML and AI for Rubin?