ZTF runs its signature summer school for a fifth year

By: Michael Coughlin
August 5, 2025

For the 5th year in a row, we hosted the ZTF summer school from July 21-25 at the University of Minnesota, where senior undergraduates, graduate students and postdocs learned about “Data Science in the Rubin Era.” 

We welcomed 75 attendees from nearly 50 different institutions to the hybrid workshop. The format of the workshop focused on delivery of engaging Jupyter notebook based talks and tutorials covering a variety of science cases and technical topics. 

Highlights included hands-on tutorials covering development of fast transient metrics for Rubin Observatory, demonstration of our new broker BOOM (the Burst and Outburst Observations Monitor) and applications of our multi-modal classification pipeline, AppleCiDEr (Applying Multimodal Learning to Classify Transient Detections Early), making detection and simulating the evolution of light echoes from historical transients. 

But it was not all hard work. Participants enjoyed a planetarium show, “Our Expanding Universe” at the University of Minnesota’s Bell Museum on the Saint Paul Campus, which featured video from Rubin Observatory operations as well as first light images. Later in the week, the group had the workshop dinner at the Mall of America. 

The school ended with a hackathon on Friday where the students applied what they have learned to a sample survey data set from ZTF and built a multi-modal classifier.

Day 1:

  • Benny Border (Classification of transients using images):  This tutorial introduced the basics of machine learning with tabular data products and image classification. 
  • Shar Daniels / Cristina Andrade (Projecting rates of Rubin identified transients): In this tutorial, we describe the data analysis platform being developed to estimate the rates of a variety of Rubin identified transients, focusing on fast transients such as kilonovae.

Day 2:

  • Michael Coughlin / Theophile du Laz / Antoine Le Calloch (Developing brokering software for Rubin): In this tutorial, we described ZTF and the alert data products produced that can be used by the broader community to identify astrophysical objects of interest to them. We introduced BOOM (the Burst and Outburst Observations Monitor), a multi-survey broker we are developing, and the notebook demonstrated how to develop filters using BOOM to identify real transients. We also introduced the use of a mobile application to access transient candidates through SkyPortal.

Day 3: 

  • Felipe Fontinele Nunes (Classification of transients using light curves): In this tutorial, we introduce light curve classification, including the use of transformers for accounting for the changing numbers of photometry points in realistic light curves.
  • Argyro Sasli / Maojie Xu (Classification of transients using spectroscopy): We explored how astronomical spectra can be used to study transient and variable sources such as supernovae, active galactic nuclei, and tidal disruption events. Using real data accessed through the Fritz/SkyPortal API, we demonstrated how to retrieve and visualize spectra from different instruments, apply redshift corrections to shift observations into the rest frame, and identify key spectral features such as hydrogen and calcium lines. We also showed how to train a CNN-based network to classify these transients and provided exercises to reinforce key concepts related to CNNs. The final model was exported to ONNX and used to perform production-oriented classification for ZTF.

Day 4: 

  • Xiaolong Li (Historical Transients through Echoes): In this tutorial, we apply AI techniques to search light echoes, and simulate the evolution of light echoes from historical events for Rubin. We train the vgg16 model as a binary classifier and explore how the model makes predictions by analyzing the feature maps. Next, we apply a region based object detection model, FasterRCNN, to detect objects in sky survey images. In addition, we simulate the evolution of light echoes (LEs) from a historical astrophysical transient (e.g., a supernova). These simulations help us predict how Rubin LSST will detect light echoes for historical transients in our Milky Way and nearby galaxies.
  • Daniel Warshofsky (Large scale variable star classification): In this tutorial, we describe some of the challenges faced in searching for variable light curves in large datasets. Next we learned how The ZTF Sources Classification Project (SCoPe) handles these issues. Finally we explored how to programmatically search through the SCoPe database to find interesting variable sources. 

Day 5:

  • Felipe Fontinele Nunes (Building a multi-modal transient classifier): This hackathon incorporated elements from the previous tutorials, combining image, photometry, and spectral classification in a fusion model.