LSST Dark Energy School

Eleven Dark Energy Schools have been held to date as part of LSST DESC meetings. Below you will find (most of) the materials used for the lessons presented at these schools – slides, activities, resources – as well as videos of the lectures on YouTube or Zoom cloud. 

LSST Corporation Logo Supported by the LSST Corporation. 

DE School I, SLAC National Laboratory, February 2, 2015

DE School II, Argonne National Laboratory, October 26, 2015

DE School III, SLAC National Accelerator Laboratory, March 7, 2016

DE School IV, University of Oxford, July 18, 2016

DE School V, SLAC, February 13, 2017

DE School VI, Stony Brook University, July 10, 2017

DE School VII, SLAC National Accelerator Laboratory, February 5, 2018

DE School VIII, Carnegie Mellon University, July 23, 2018

DE School IX, Berkeley, February 25, 2019

DE School X, Paris, July 15, 2019

DE School XI, Tucson, January 20, 2020

DESC members can view the attendance lists and provide feedback at the school wiki pages:

DE Schools III through IX have been supported by the LSSTC Enabling Science effort, including matching funds from the Simonyi-Gates Challenge. All DE Schools have also received support through the host institutions.

The DE Schools promote learning through peer interactions; see A Quick Guide to Active Learning in Lectures for more details.

Lessons in reverse chronological order:

What we talk about when we talk about neutrinos (January 20, 2020)

Watch the class on Zoom cloud here

Teacher: Chris Walter

Lesson Materials and Resources:

Can machine learning solve my problem? (January 20, 2020)

Watch the class on Zoom cloud here

Teacher: Emille Ishida

Lesson Materials and Resources:

Are we there yet? How to read the statistical signposts. (January 20, 2020)

Teacher: Alex Malz

Lesson Materials and Resources:

Extremely large optics fabrication & testing at the Richard F. Caris Mirror Lab (January 20, 2020)

Watch the class on Zoom cloud here

Teacher: Dae Wook Kim

Lesson Materials and Resources:

Combining ground and space-based imaging for cosmic shear (July 15, 2019)

Watch the class on Youtube here

Teacher: Catherine Heymans

Lesson Materials and Resources:

Interpolating data using Gaussian Processes: « With great power comes great responsibility. » (July 15, 2019)

Watch the class on Youtube here

Teacher: Pierre-Francois Leget

Lesson Materials and Resources:

Multi-band and multi-resolution deblending: Why it is important to leverage all available information (July 15, 2019)

Watch the class on Youtube here

Teacher: Fred Moolekamp

Lesson Materials and Resources:

Designing libraries and APIs (July 15, 2019)

Watch the class on Youtube here

Teachers: Mike Jarvis and Joe Zuntz

Lesson Materials and Resources:

Collaborative software development in Python (February 25, 2019)

Teacher: Stéfan van der Walt

Lesson Materials and Resources:

Why might the DESC photometric calibration requirements keep you up at night? (February 25, 2019)

Teacher: Eli Rykoff

Lesson Materials and Resources:

Supernova cosmology with LSST and survey strategy (February 25, 2019)

Zoom recording: slides, audio

Teacher: Renee Hlozek

Lesson Materials and Resources:

Complementarity of dark energy and dark matter probes for LSST (February 25, 2019)

Zoom recording: slides, audio

Teacher: Keith Bechtol

Lesson Materials and Resources:

What are “3 x 2-point” observables for cosmology? An introduction with the Core Cosmology Library (July 23, 2018)

Watch the class on YouTube here!

Teacher: Elisa Chisari

Lesson Materials and Resources:

Let there be light: What’s under the carpet in mock galaxy catalogs? (July 23, 2018)

Watch the class on YouTube here!

Teacher: Yao-Yuan Mao

Lesson Materials and Resources:

Validation: Your Data, Your Analysis, and You. (July 23, 2018)

Watch the class on YouTube here!

Teacher: Michael Wood-Vasey

Lesson Materials and Resources:

Why, when and how to write parallelized code (in DESC). (July 23, 2018)

Watch the class on YouTube here!

Teacher: Debbie Bard & Francois Lanusse

Lesson Materials and Resources:

Expect the Unexpected: Lessons Learned Commissioning Astronomical Instruments (Feb 5, 2018)

Watch the class on YouTube here!

Teacher: Kevin Reil

Lesson Materials and Resources:

Is my code good enough? Improving software through code review (Feb 5, 2018)

Watch the class on YouTube here!

Teacher: Mike Jarvis

Lesson Materials and Resources:

A Hitchhiker’s Guide to Machine Learning – applications to supernova classification (July 10, 2017)

Watch the class on YouTube here! (coming soon…)

Teacher: Michelle Lochner

Lesson Materials and Resources:

Cosmology with Galaxy Clusters; and “Now you see me, now you don’t - Effects of selection biases” (July 10, 2017)

Watch the class on YouTube here!

Teacher: Anja von der Linden

Lesson Materials and Resources:

Testing Code, as You Code – unit tests and more (July 10, 2017)

Watch the class on YouTube here!

Teacher: Mike Jarvis

Lesson Materials and Resources:

Cosmology with Strong Gravitational Lenses (Feb 13, 2017)

Watch the class on YouTube here!

Teacher: Phil Marshall, SLAC

Lesson Materials and Resources:

Machine Learning in the LSST Era (Feb 13, 2017)

Teacher: David Kirbky, UC Irvine

Watch the class on YouTube here!

Lesson Materials and Resources:

Weak Lensing Shear Estimation Methods and Systematics (Feb 13, 2017)

Teacher: Rachel Mandelbaum, Carnegie Mellon University

Watch the class on YouTube here!

Lesson Materials and Resources:

Null Tests: Looking for Signal in All the Wrong Places (Feb 13, 2017)

Teacher: Mike Jarvis, U of Pennsylvania

Watch the class on YouTube here!

Lesson Materials and Resources:

The Era of Large Surveys: What Will LSST Deliver? (July 18, 2016)

Teacher: Mario Juric, U of Washington

Watch the class on YouTube here!

Lesson Materials and Resources:

How Bright is that Object? (July 18, 2016)

Teacher: Robert Lupton, Princeton University

Watch the class on YouTube here!

Lesson Materials and Resources:

Photometric Redshifts for LSST (July 18, 2016)

Teacher: Jeff Newman, University of Pittsburgh

Watch the class on YouTube here!

Lesson Materials and Resources:

Cross-Correlations of Dark Energy Probes (July 18, 2016)

Teacher: Jo Dunkley, University of Oxford

Watch the class on YouTube here!

Lesson Materials and Resources:


Robust Model Fitting with Applications to Astronomy (March 7th, 2016)

Teacher: Andrew Connolly, U of Washington

Watch the class on YouTube here!

Lesson Materials and Resources:

OR

Primer on Wavefronts and Aberrations: the LSST Optical PSF (March 7th, 2016)

Teacher: Aaron Roodman, SLAC

Watch the class on YouTube here!

Lesson Materials and Resources:

More than Just a Phase: the LSST Atmospheric PSF (March 7th, 2016)

Teacher: Josh Meyers, Stanford University

Watch the class on YouTube here!

Lesson Materials and Resources:

Which Dark Energy Models Will We Test in the LSST-WFIRST-Euclid Era? (March 7th, 2016)

Teacher: Bhuv Jain, U of Pennsylvania

Watch the class on YouTube here!

Lesson Resources:


Dark Energy and Lensing: From GR to Data Analysis (Oct 26th, 2015)

Teacher: Scott Dodelson, Fermilab

Watch the class on YouTube here!

Overview of modified gravity models for acceleration and how they might be detected using weak lensing in LSST.

Learning Objectives:

Lesson Materials and Resources:

How to simulate our Universe (Oct 26th, 2015)

Teacher: Katrin Heitmann, Argonne National Laboratory

Watch the class on YouTube here! 

Lesson Materials and Resources:

Future Computing Architectures and Data Analysis (Oct 26th, 2015)

Teacher: Salman Habib and Adrian Pope, Argonne National Laboratory

Watch the class on YouTube here!

Lesson Materials and Resources:

Survey Strategy and Dark Energy Systematics (Oct 26th, 2015)

Teacher: Eric Gawiser, Rutgers

Watch the class on YouTube here!

Lesson Materials and Resources:


How to use statistics to describe the large scale structure of the Universe (Feb 2, 2015)

Teacher: David Kirkby, U.C. Irvine

Watch the class on YouTube here!

An introduction to large scale structure in the Universe and techniques used to measure its statistics, including the topics of co-variance, random fields and the power spectrum. 

Learning Objectives:

After completing this class, students will be able to:

Lesson Materials:

Cosmic Co-variance (Feb 2, 2015)

Teacher: Michael Schneider, Lawrence Livermore National Laboratory

Watch the class on YouTube here!

In this lesson we will cover how we infer cosmological parameter constraints in the presence of correlated errors, including how we infer cosmological parameters from large-scale structure probes and how we combine parameter constraints from different surveys. 

Learning Objectives:

After completing this class, students will be able to:

Lesson Materials:

How the Physics of Sensors Impacts Dark Energy Science (Feb 2, 2015)

Teacher: Chris Stubbs, Harvard

Watch the class on YouTube here!

This lesson deals with the non-idealities encountered in real-world CCDs. We illustrate how to assess the impact of one particular gremlin on weak lensing measurements, lateral electric fields arising from radially symmetrical impurities in the Silicon wafer. 

Learning Objectives:

After completing this class, students will be able to:

Lesson Materials and Resources:

Key LSST Design Choices, and How They Were Driven by Science (Feb 2, 2015)

Teacher: Steve Kahn (LSST/SLAC)

Watch the class on YouTube here!

Learning Objectives:

After completing this class, students will be able to:

Lesson Materials and Resources:

Image processing algorithms: Building science-ready catalogs (Feb 2, 2015)

Teacher: Jim Bosch, Princeton

Watch the class on YouTube here!

A very high-level view of the LSST Data Management (DM) pipelines, and a closer look at the details of algorithms for object detection. 

Learning Objectives:

After completing this class, students will be able to:

Lesson Materials and Resources:

LSST’s Cosmological Probes (Feb 2, 2015)

Teacher: Shirley Ho, Carnegie Mellon University

Watch the class on YouTube here!

This lesson is designed to introduce several of the dark energy probes in LSST. It assumes basic knowledge of cosmology and statistics. 

Learning objectives:

After this class, students will be able to:

Lesson Materials and Resources: