Course materials for ZTF Summer School 2024
This course will introduce key concepts and techniques used to work with large datasets, in the context of the field of astrophysics. The school will focus on modern approaches to creating and manipulating large data sets. We will introduce a range of machine learning techniques for processing data: classification algorithms (supervised and unsupervised learning), clustering algorithms, regression problems, recommender systems, graphic models and others. The algorithms will first be introduced in lectures, and the emphasis will then be placed on homework worked as teams in which the students will apply the algorithms (and already available packages) to astrophysical data sets to answer specific astrophysics questions. The course will assume familiarity with basic concepts in astrophysics, but it will include brief reviews as needed to demonstrate the use of modern data analysis techniques.
Day 1 (Monday, July 29th):
- Talks: ZTF Introduction (Michael Coughlin / Theophile Jegou Du Laz), Supervised Learning (Jie Ding)
- Tutorials: Alert Stream Access, Training a BTS-like model
Day 2 (Tuesday, July 30th):
- Talks: Supervised Learning continued (William Benoit)
- Tutorials: Compact binary coalescences
- Outing: Bell Museum / Planetarium
Day 3 (Wednesday, July 31st):
- Talks: Simulation Based Inference (Deep Chatterjee / Ethan Marx), Mixed Integer Linear Programming (Leo Singer)
- Tutorials: Kilonova light curves, Telescope Scheduling with MILP
Day 4 (Thursday, August 1st):
- Talks: Anomaly Detection (Xialong Li / Daniel Warshofsky)
- Tutorials: Echoes case study, transient case study
- Outing: Mall of America
Day 5 (Friday, August 2nd):
- Hackathon with BTS data set
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Fork this repo
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The topics of each day's lectures are described in the schools' program
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Lectures are in directories named XY/ where XY/ is the number of the day
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Various help cheat sheets are included in help/.
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You should also review in-class notebooks to make sure you understand what is happening
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The lecture notebooks have in-class exercises for you to work on
- UIUC Fundamentals of Data science: https://github.com/gnarayan/ast596_2023_Spring
- Caltech Astroinformatics: https://sites.astro.caltech.edu/ay119/
- GROWTH summer school: http://growth.caltech.edu/growth-school-2019.html
- AURA winter school: http://www.aura-o.aura-astronomy.org/winter_school/ - go to Past Years.
- YouTube Neural Networks: https://www.youtube.com/watch?v=aircAruvnKk
- UMN Big Data Astronomy: https://github.com/mcoughlin/ast8581_2024_Spring
If you are new to github, we recommend watching:
- https://www.youtube.com/watch?v=USjZcfj8yxE&ab_channel=ColtSteele
- https://www.youtube.com/watch?v=nhNq2kIvi9s&ab_channel=ColtSteele
| Question | Answer |
|---|---|
| ztfquery documentation | https://github.com/MickaelRigault/ztfquery |
| ZTF table content | https://irsa.ipac.caltech.edu/onlinehelp/ztf/overview.html |
| Alerts content | https://zwickytransientfacility.github.io/ztf-avro-alert/schema.html |
| What is the "Redshift Completeness Factor" program? | It's a program dedicated to determine the number of SN host galaxies with known spectroscopic redshift prior to the SN discovery divided by the total number of SN hosts. See https://arxiv.org/abs/1910.12973 |
| What is the "Census of the Local Unverse" program? | The Census of the Local Universe (CLU) aims to provide a galaxy catalog out to 200 Mpc that is as complete as possible. See https://arxiv.org/abs/1710.05016 |
| How is the time allocation for each filter decided? | ZTF uses a Integer Linear Programming approach, that optimizes an observing plan for an entire night by assigning targets to temporal blocks, enables strict control of the number of exposures obtained per field and minimizes filter changes. See https://arxiv.org/abs/1905.02209 |