Overview

We are delighted to announce that due to continued funding from the National Science Foundation and support from our partners at the University of Washington, Seattle, we will hold a fourth edition of the ZTF summer school. As in previous years, we continue with the same successful hybrid format, with both in-person and virtual attendance possible. We expect to accept up to 20 in-person and 40 online participants. The school will take place on Jul 29- Aug 2 and will be hosted by the University of Minnesota, USA.

This year's theme for the ZTF summer school is AI and machine learning in time-domain astronomy. The program will have a special focus on unsupervised and supervised learning, deep learning, simulation based inference and more. Best suited for graduate students and above, the school offers a week of hands-on training with Python Jupiter notebooks under the guidance of experts in the field. This year, we will wrap up with a special data challenge to apply the newly learned skills. Students will mainly work with astronomical data from the ZTF survey and other transient survey data.

Resources

Materials from the ZTF summer school are made freely available to students and educators. You can watch recordings of the lectures on Youtube and download the Python notebooks from Github.

 

Watch Lectures Download Python Notebooks

 

Application & Funding

Funding

The venue and meals during the school will be covered by our NSF grant. Thanks to a Heising-Simons grant, we will be able to offer some financial support for lodging. If you want to be considered for it, please indicate so in the application form. Note that participants who want to attend in-person will have to cover their travel expenses.

Application

The deadline for applying to the ZTF summer school has passed.

 

Program

The theme for this year is machine learning and AI in time-domain astronomy. With hundreds of thousands of detections per day, the ability to filter and classify real transients in a reliable and automated fashion is critical to the advancement of astronomical science. You will learn how astronomers take full advantage of as well as drive innovation in machine learning and AI. We will demostrate and guide you through the first fully automated detection, classification and reporting of a transient achieved by our ZTF astronomers last year.

The school format includes short lectures and interactive hands-on sessions when students will work with Python Jupyter notebooks to complete data processing assignments under the guidance of tutors. The school will run approximately between 9 am and 4 pm CDT (UTC-5) each day. During that time, students who participate online will be able to submit questions to tutors via Slack. Students whose time zones make it inconvenient to join the school can work on their own. They are welcome to submit questions on Slack and our tutors will respond as soon as possible.

Accommodation

The school is hosted by our ZTF partner institution the University of Minnesota, Twin Cities. The school sessions will take place on campus. Participants who are attending the school in person, will stay at Days Hotel . Accommodation is covered by a Heising-Simons grant from our ZTF partner, the University of Washington, Seattle.

Organizer

The lead organizer of the ZTF summer schools is Michael Coughlin at the University of Minnesota. He is assisted by members of the ZTF partnership.

• Matthew Graham - ZTF Project Scientist (Caltech)
• Ivona Kostadinova - ZTF Program Coordinator (Caltech)

Contact

If you have questions, please get in touch with us at ztf dot summer dot school @ gmail.com