LSST Dark Energy School

Twelve 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. 

Supported by the LSST Corporation. 

DE Schools III through IX were 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.

• DE School XII, Chicago, August 1, 2022
  • All together now: covariances for 3x2pt analysis (Danielle Leonard)
  • Artifact or science? Instrument signature removal (Merlin Fisher-Levine)
  • Robust reproducible results in python — reducing reliance on notebooks! (Eric Charles)
  • Making values-based decisions for your career (Lucianne Walkowicz)
• DE School XI, Tucson, January 20, 2020
  • What we talk about when we talk about neutrinos (Chris Walter)
  • Can machine learning solve my problem? (Emille Ishida)
  • Are we there yet? How to read the statistical signposts. (Alex Malz)
  • Extremely large optics fabrication & testing at the Richard F. Caris Mirror Lab (Dae Wook Kim)
• DE School X, Paris, July 15, 2019
  • Combining ground and space-based imaging for cosmic shear (Catherine Heymans)
  • Interpolating data using Gaussian Processes: « With great power comes great responsibility. » (Pierre-Francois Leget)
  • Multi-band and multi-resolution deblending: Why it is important to leverage all available information (Fred Moolekamp)
  • Designing libraries and APIs (Mike Jarvis and Joe Zuntz)
• DE School IX, Berkeley, February 25, 2019
  • Complementarity of dark energy and dark matter probes for LSST (Keith Bechtol)
  • Supernova cosmology with LSST and survey strategy (Renee Hlozek)
  • Why might the DESC photometric calibration requirements keep you up at night? (Eli Rykoff)
  • Collaborative software development in Python (Stéfan van der Walt)
• DE School VIII, Carnegie Mellon University, July 23, 2018
  • What are “3 x 2-point” observables for cosmology? An introduction with the Core Cosmology Library (Elisa Chisari)
  • Let there be light: What’s under the carpet in mock galaxy catalogs? (Yao-Yuan Mao)
  • Validation: Your Data, Your Analysis, and You (Michael Wood-Vasey)
  • Why, when and how to write parallelized code (in DESC). (Debbie Bard & Francois Lanusse)
• DE School VII, SLAC National Accelerator Laboratory, February 5, 2018
  • Expect the Unexpected: Lessons Learned Commissioning Astronomical Instruments (Kevin Reil)
  • Is my code good enough? Improving software through code review (Mike Jarvis)
• DE School VI, Stony Brook University, July 10, 2017
  • A Hitchhiker’s Guide to Machine Learning – applications to supernova classification (Michelle Lochner)
  • Cosmology with Galaxy Clusters; and “Now you see me, now you don’t - Effects of selection biases” (Anja von der Linden)
  • Testing Code, as You Code – unit tests and more (Mike Jarvis)
• DE School V, SLAC, February 13, 2017
  • Machine Learning in the LSST Era (David Kirkby)
  • Weak Lensing Shear Estimation Methods and Systematics (Rachel Mandelbaum)
  • Null Tests: Looking for Signal in All the Wrong Places (Mike Jarvis)
  • Cosmology with Strong Gravitational Lenses (Phil Marshall)
• DE School IV, University of Oxford, July 18, 2016
  • Cross-Correlations of Dark Energy Probes (Jo Dunkley)
  • The Era of Large Surveys: What Will LSST Deliver? (Mario Juric)
  • How Bright is that Object? (Robert Lupton)
  • Photometric Redshifts for LSST (Jeff Newman)
• DE School III, SLAC National Accelerator Laboratory, March 7, 2016
  • Robust Model Fitting with Applications to Astronomy (Andrew Connolly)
  • Which Dark Energy Models Will We Test in the LSST-WFIRST-Euclid Era? (Bhuvnesh Jain)
  • More than Just a Phase: the LSST Atmospheric PSF (Josh Meyers)
  • Primer on Wavefronts and Aberrations: the LSST Optical PSF (Aaron Roodman)
• DE School II, Argonne National Laboratory, October 26, 2015
  • Dark Energy and Lensing: From GR to Data Analysis (Scott Dodelson)
  • Survey Strategy and Dark Energy Systematics (Eric Gawiser)
  • Future Computing Architectures and Data Analysis (Salman Habib and Adrian Pope)
  • How to simulate our Universe (Katrin Heitmann)
• DE School I, SLAC National Laboratory, February 2, 2015
  • Image processing algorithms: Building science-ready catalogs (Jim Bosch)
  • LSST’s Cosmological Probes (Shirley Ho)
  • Key LSST Design Choices, and How They Were Driven by Science (Steve Kahn)
  • How to use statistics to describe the large scale structure of the Universe (David Kirkby)
  • Cosmic Co-variance (Michael Schneider)
  • How the Physics of Sensors Impacts Dark Energy Science (Chris Stubbs)

DE School XII, Chicago, August 1, 2022

Confluence page contains material accessible to DESC members only.

All together now: covariances for 3x2pt analysis (Danielle Leonard)

Watch the class on Zoom cloud here

Lesson Materials and Resources:

• Lesson slides: google slides
• Pre-lesson reading: Section 3.1 of this paper (about 2 pages). Particularly useful if you are not familiar with Bayes theorem.

Artifact or science? Instrument signature removal (Merlin Fisher-Levine)

Watch the class on Zoom cloud here

Lesson Materials and Resources:

• Lesson slides: pdf

Robust reproducible results in python — reducing reliance on notebooks! (Eric Charles)

Watch the class on Zoom cloud here

Lesson Materials and Resources:

• Lesson plan: on GitHub
• GitHub repo for package: here
• Pre-lesson reading: Jarvis and Zuntz DE School lesson on designing code libraries and application programming interfaces

Making values-based decisions for your career (Lucianne Walkowicz)

Material and recordings are available to DESC members here

DE School XI, Tucson, January 20, 2020

Confluence page contains material accessible to DESC members only.

What we talk about when we talk about neutrinos (Chris Walter)

Watch the class on Zoom cloud here

Lesson Materials and Resources:

• Lesson slides: pdf (DESC-internal link)
• Pre-lesson reading:
  • Scientific American article “Detecting Massive Neutrinos” (DESC-internal link)
• Further resources:
  • A later Scientific American article “The Neutrino Puzzle” (DESC-internal link)
  • A popular and scientific-level presentation of the discovery of neutrino oscillations by the Nobel Committee in 2015.
  • A 2019 review

Can machine learning solve my problem? (Emille Ishida)

Watch the class on Zoom cloud here

Lesson Materials and Resources:

• Lesson slides: Google slides
• Pre-lesson reading:
  • Understanding Machine Learning: From Theory to Algorithms: Introduction up to section 1.4 (7 pages)
• Further resources:
  • Carleo et al., 2019 - Machine learning and the physical sciences
  • Book: Understanding Machine Learning, Shai & Shai, 2014
  • YouTube playlist with lectures given by the author of the above book

Are we there yet? How to read the statistical signposts. (Alex Malz)

Lesson Materials and Resources:

• Lesson slides: Google slides
• Pre-lesson reading:
  • Feigelson & Babu. Statistical Methods for Astronomy (2012): Section 2 (<15 pages)
• Further resources:
  • Feigelson & Babu. Modern Statistical Methods for Astronomy, Cambridge Press 2012

Extremely large optics fabrication & testing at the Richard F. Caris Mirror Lab (Dae Wook Kim)

Watch the class on Zoom cloud here

Lesson Materials and Resources:

• Lesson slides: pdf (DESC-internal link)

DE School X, Paris, July 15, 2019

Confluence page contains material accessible to DESC members only.

Combining ground and space-based imaging for cosmic shear (Catherine Heymans)

Watch the class on Youtube here

Lesson Materials and Resources:

• Lesson slides: pdf (DESC-internal link)
• Pre-lesson reading:
  • Sec. 1 (Introduction) of “Scientific Synergy Between LSST and EUCLID”, J. Rhodes et al. (arXiv:1710.08489)
• Further resources:
  • Remainder of above paper.

Interpolating data using Gaussian Processes: « With great power comes great responsibility. » (Pierre-Francois Leget)

Watch the class on Youtube here

Lesson Materials and Resources:

• Lesson slides: pdf, keynote (DESC-internal links)
• Jupyter notebooks to play with Gaussian processes realization & prediction, hyperparameter search, and mean function
• Pre-lesson reading:
  • Chapter 1 (pdf, only six pages) of “Gaussian Processes for Machine Learning”, by Rasmussen & Williams. Entire book is available online through the “official” website
• Further resources:
  • Remainder of above book.

Multi-band and multi-resolution deblending: Why it is important to leverage all available information (Fred Moolekamp)

Watch the class on Youtube here

Lesson Materials and Resources:

• Lesson slides: pdf (DESC-internal link), google slides (public)
• Pre-lesson reading:
  • Sec. 1 (Introduction) of “SCARLET: Source separation in multi-band images by Constrained Matrix Factorization”, P. Melchior, F. Moolekamp et al. (arXiv:1802.10157).
• Further resources:
  • Remainder of above paper.
  • “Block-Simultaneous Direction Method of Multipliers: A proximal primal-dual splitting algorithm for nonconvex problems with multiple constraints”, F. Moolekamp & P. Melchior (arXiv:1708.09066).

Designing libraries and APIs (Mike Jarvis and Joe Zuntz)

Watch the class on Youtube here

Lesson Materials and Resources:

• Lesson slides: pdf
• Google slides to assist in activities:
  • Activity 2
  • Activity 3

DE School IX, Berkeley, February 25, 2019

Confluence page contains material accessible to DESC members only.

Complementarity of dark energy and dark matter probes for LSST (Keith Bechtol)

Zoom recording: slides, audio

Lesson Materials and Resources:

• Lesson slides: pdf - DESC-internal link
• Pre-lesson reading:
  • Probing the Fundamental Nature of Dark Matter with LSST arxiv:1902.01055 – start with the short “Executive Summary” and “Introduction” sections and then look at any of the more detailed sections that are of interest.
• Further resources:
  • Gravitational probes of dark matter physics arXiv:1712.06615

Supernova cosmology with LSST and survey strategy (Renee Hlozek)

Zoom recording: slides, audio

Lesson Materials and Resources:

• Lesson slides: DESchoolFeb2019_Hlozek.pdf (DESC-internal link)
• Pre-lesson reading:
  • Supernova cosmology: legacy and future arXiv:1102.1431 – start with the introduction, and then also glance at other sections of interest, like the cosmology and systematics sections.
• Oxford Galaxy Worksheets from Joe Zuntz to compute H0 from SDSS galaxies. During class, we will each take one (randomly) from the pdf and compute H0 through this spreadsheet.
• Further resources: Sections related to SNe in the following DESC documents:
  • Optimizing the LSST Observing Strategy for Dark Energy Science: DESC Recommendations for the Wide-Fast-Deep Survey arxiv:1812.00515
  • LSST DESC Science Requirements Document arxiv:1809.01669

Why might the DESC photometric calibration requirements keep you up at night? (Eli Rykoff)

Lesson Materials and Resources:

• Lesson slides: deschool_photometry.pdf (DESC-internal link)
• Pre-lesson reading:
  • Forward Global Photometric Calibration of the Dark Energy Survey – in particular Sections 1 and 2; also interesting to peruse the figures, especially figures 1, 2, 3, 9, and 22.

Collaborative software development in Python (Stéfan van der Walt)

Lesson Materials and Resources:

• Lesson slides
• Pre-lesson reading:
  • How to write good scientific software (pdf - DESC-internal link, or more recent public blog post)

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

Confluence page contains material accessible to DESC members only.

What are “3 x 2-point” observables for cosmology? An introduction with the Core Cosmology Library (Elisa Chisari)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Lesson slides: CCL_DE_School_CMU18_slides.pdf, plus slides for your results. Notebooks with full solutions are here:
  • Notebook 1: Growth and expansion CCL3x2_DESchool_Ex1_complete.ipynb
  • Notebook 2: Making a sample of galaxies CCL3x2_DESchool_Ex2_complete.ipynb
  • Notebook 3: Observables for 3x2pt CCL3x2_DESchool_Ex3_complete.ipynb
• Pre-lesson reading:
  • Multi-probe methodology for DES, E. Krause+, up to and including section B, https://arxiv.org/pdf/1706.09359.pdf
• Software: We will illustrate the lesson with the Core Cosmology Library, a public DESC software tool. 
  • Installation on your personal computer is via “pip” and instructions are available here. Scroll down to “Install the pyccl Python module” after making sure you satisfy the cmake requirement. For a more ready-to-use option, we also have a working NERSC installation you can use through the Jupyter-dev  environment.
  • Make sure you can open and run one of our CCL example Jupyter notebooks. For example, you can try out this one.

Let there be light: What’s under the carpet in mock galaxy catalogs? (Yao-Yuan Mao)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Lesson slides (google pres’n)
• Pre-lesson reading:
  • Katrin Heitmann’s DE School lesson on How to Simulate our Universe (Yao’s lesson will help you deconstruct slides 25 and 26)
  • Section 1 of Wechsler & Tinker 2018: https://arxiv.org/abs/1804.03097
• Post-lesson resources
  • The rest of Wechsler & Tinker 2018: https://arxiv.org/abs/1804.03097
  • Moster+ 2017 https://arxiv.org/abs/1705.05373
  • Behroozi+ 2018 https://arxiv.org/abs/1806.07893
  • KITP Conference: Quantifying and Understanding the Galaxy—Halo Connection: http://online.kitp.ucsb.edu/online/galhalo_c17/

Validation: Your Data, Your Analysis, and You (Michael Wood-Vasey)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Lesson slides (pdf - DESC-internal link)
• Pre-lesson reading:
  • LSST validate_drp SurveySim presentation - DESC-internal link
• Further resources -
  • LSST validate_drp Data Management Technical Note
  • The Hyper Suprime-Cam Software Pipeline (Bosch et al. 2017)
  • The DES Science Verification Weak Lensing Shear Catalogues (Jarvis et al. 2015)
  • LSST Science Requirements Document (LPM-17)
  • Gaia Data Release 2: Catalogue validation

Why, when and how to write parallelized code (in DESC). (Debbie Bard & Francois Lanusse)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Lesson slides (google pres’n)
• Pre-lesson reading:
  • A good article about the future of computing: https://www.technologyreview.com/s/611574/the-us-may-have-just-pulled-even-with-china-in-the-race-to-build-supercomputings-next-big/
  • TOAST: a python-based CMB data simulation and reduction framework, scaled to 658,784 KNL cores on Cori (using containers): https://cs.lbl.gov/news-media/news/2017/a-toast-for-next-generation-cmb-experiments/
  • Galactos: calculating the three-point correlation function on two billion galaxies on 655,248 KNL cores on Cori (winner of the 2017 HPC Innovation Excellence prize): https://arxiv.org/abs/1709.00086
• Further resources -
  • http://www.nersc.gov/users/getting-started/
  • http://www.nersc.gov/users/computational-systems/cori/running-jobs/
  • http://www.nersc.gov/users/data-analytics/data-analytics-2/python/ Some tutorials:
  • http://www.nersc.gov/users/training/online-tutorials/introduction-to-scientific-i-o/
  • http://www.nersc.gov/assets/Training-Materials/hybridTalk.pdf

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

Confluence page contains material accessible to DESC members only.

Expect the Unexpected: Lessons Learned Commissioning Astronomical Instruments (Kevin Reil)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Lesson slides (pptx, pdf)
• Pre-lesson reading: Examples from DECam commissioning – search for words like “commissioning”, “first light”, “challenge”, “difficult”… in Sec. 9 of this paper on the Dark Energy Camera: https://arxiv.org/abs/1504.02900

Is my code good enough? Improving software through code review (Mike Jarvis)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Lesson slides
• Pre-lesson reading:
  • Practical Lessons in Peer Code Review - Salsita Software blog
  • Better Learning through Code Reviews - Capgemini Engineering blog
  • Why code reviews matter (and actually save time!) - Atlassian Agile Coach
  • 7 ways to uplevel your code review skills - Asana blog

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

Confluence page contains material accessible to DESC members only.

A Hitchhiker’s Guide to Machine Learning – applications to supernova classification (Michelle Lochner)

Lesson Materials and Resources:

• Lesson slides.
• Pre-lesson reading - David Kirkby’s DE School lesson on Machine Learning in the LSST Era
• Further resources - Pattern Recognition and Machine Learning (Bishop 2006), Andrew Ng’s Coursera course, Scikit-learn tutorial (Jake VanderPlas)

Cosmology with Galaxy Clusters; and “Now you see me, now you don’t - Effects of selection biases” (Anja von der Linden)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Lesson slides.
• Pre-lesson reading - Introduction (Chapter 1) of Cosmological Parameters from Observations of Galaxy Clusters (Allen et al. 2011);  Introduction (Section 1 until ~halfway through 1.2) of Weighing the Giants I.
• Further resources - All of Allen et al. 2011;  Weighing the Giants IV;  Observational probes of cosmic acceleration (Weinberg et al. 2013).

Testing Code, as You Code – unit tests and more (Mike Jarvis)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Lesson slides.
• Pre-lesson homework and in-class exercise.
• Pre-lesson reading - Software Testing - Many of the terms will be introduced in the lesson, but it may help to have seen some in advance.
• Further resources - The Art of Unit Testing, by Roy Osherove

DE School V, SLAC, February 13, 2017

Confluence page contains material accessible to DESC members only.

Machine Learning in the LSST Era (David Kirkby)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Lesson slides
• A la carte appetizers (pre-lesson reading):
  • xkcd: Linear Regression [explained]
  • A Digital Copy of the Universe, Encrypted
  • The Great A.I. Awakening
• For further resources, see the list at the end of the slides.

Weak Lensing Shear Estimation Methods and Systematics (Rachel Mandelbaum)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Lesson slides
• Pre-lesson reading - Introduction (section 1) of Jarvis et al., 2016.
• Further resources -
  • GREAT3 results paper - Various WL systematics including noise bias and the impact of realistic galaxy morphology.
  • Refregier et al 2012 - Clear explanation of noise bias.
  • Hirata et al 2004 - Supposedly about a totally different topic (intrinsic alignments), but Chris Hirata derived a whole bunch of stuff about various WL systematics using a nice clear formalism in section 3.
  • The rest of Jarvis et al 2016.

Null Tests: Looking for Signal in All the Wrong Places (Mike Jarvis)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Lesson slides
• Pre-lesson reading - Jarvis et al, 2016 (section 8)
• Further resources -
  • More DES papers with good null test sections:
    • Clampitt et al, 2017 (section 5) galaxy-galaxy lensing
    • Bonnet et al, 2016 (section 6.1) photometric redshifts
    • Becker et al, 2016 (section VI) cosmic shear
    • Rykoff et al, 2016 (section 5) redMaPPer
    • Leistedt et al, 2016 (section 3, but really the whole paper) photometry and source detection
  • An incomplete list of some non-DES papers with good null test sections:
    • Sherwin et al, 2016 (section V) ACTPol
    • BICEP2 Collaboration, 2016 (section 5)
    • Planck Collaboration, 2016 paper VIII
    • Kuijken et al, 2015 (section 5) KIDS weak lensing
    • Huff et al, 2014 (sections 3,4) SDSS cosmic shear
    • … more.

Cosmology with Strong Gravitational Lenses (Phil Marshall)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Lesson slides
• Pre-lesson reading - “Cosmic lenses support finding on faster than expected expansion of the Universe”, joint STScI/ESA H0LiCOW press release, January 2017.
• Further resources - “Time Delay Cosmography,” from The Astronomy And Astrophysics Review, 2016, by Treu & Marshall.

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

Confluence page contains material accessible to DESC members only.

Cross-Correlations of Dark Energy Probes (Jo Dunkley)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Lesson slides (with notes from talk embedded)
• Exploiting Cross Correlations and Joint Analyses, Report from the “Dark Energy and CMB” working group for the APS DPF longterm planning exercise (“Snowmass”) 2013.
• ‘Looking through the lens’, Schaan et al.
• For a small part of this lesson, install the CLASS code (c and optional python wrapper) on your laptop. Find it here: http://class-code.net. As the instructions say, either download the tarball, or grab it from git. It takes less than 5 minutes to install and run, just get the c code for this lesson and check it runs by doing ‘make clean’, ‘make class’, and then ‘./class explanatory.ini’. We just need the c code this time. As the instructions say, you might need to comment out the -fopenmp flag to compile without openMP. Then also copy in this ini file: test_mpk.ini. For plotting, you need either gnuplot or other quick plotter. There is also a python plotter that comes with CLASS but we don’t need this today.

The Era of Large Surveys: What Will LSST Deliver? (Mario Juric)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Lesson slides
• The LSST Data Management System
• The Data Products Definition Document
• Some general familiarity with LSST will be assumed; suggested reading – LSST: from Science Drivers to Reference Design and Anticipated Data Products

How Bright is that Object? (Robert Lupton)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Lesson slides

Photometric Redshifts for LSST (Jeff Newman)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Lesson slides
• Section 1 of Newman et al., Spectroscopic Needs for Imaging Dark Energy Experiments: Photometric Redshift Training and Calibration
• Sections 1, 4, 5, and appendices of Bonnett et al., Redshift distributions of galaxies in the DES Science Verification shear catalogue and implications for weak lensing
• Sections 1, 3, and 4 of Dahlen et al., A Critical Assessment of Photometric Redshift Methods: A CANDELS Investigation
• iPython notebooks for lesson: you will need Python 2.7, numpy, matplotlib, astropy, scikit-learn, and jupyter installed on your laptop.  This will be easiest if you use the anaconda python distribution (https://www.continuum.io/downloads), in which case you can use the pip install command, or enthought canopy (free for academic use, https://store.enthought.com/licenses/academic/), in which case you can use the graphical package manager interface.  You can find more detailed instructions for these installations (with the exception of astropy, which works similarly to the rest) at http://cadence.lsst.org/DESC_school/#instructions. Once you have Python working, download these 2 files to a working directory: Photo-z Lessons.ipynb and data_trim.fits.gz.

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

Confluence page contains material accessible to DESC members only.

Robust Model Fitting with Applications to Astronomy (Andrew Connolly)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Chapter 8 (Regression and Model Fitting) in  “Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data” by Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas & Alexander Gray (2014). Errata for Chapter 8 can be found here.
• This lesson uses an iPython notebook. See DESC_school for installation instructions for python and associated packages. Example notebooks are available under the notebook tab. A tutorial for ipython is available for those new to notebooks. To download a notebook click on the title of the notebook and then on “Source” (in the top right corner). Download and test the “Histogram” notebook to ensure that you have the right packages installed. 
• Python 2.7 is assumed for the class with the astroml, scipy, scikit-learn, and pymc packages installed. Details for installing these packages are available here.

OR

• To run the notebooks in the cloud simply go to http://54.84.61.202:8000 and launch the RobustFittingClass.ipynb

Which Dark Energy Models Will We Test in the LSST-WFIRST-Euclid Era? (Bhuvnesh Jain)

Watch the class on YouTube here!

Lesson Resources:

• Dark Energy (21-page pdf) from PDG 2015 Review of Particle Physics (Astrophysics and Cosmology)

More than Just a Phase: the LSST Atmospheric PSF (Josh Meyers)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Lesson slides
• “Introduction to Fourier Optics” by Joseph W. Goodman; see notes under above lesson.
• Also see this concise lecture from Observational Astronomy at U of Heidelberg: Atmospheric Turbulence: “Seeing” (12-page pdf)

Primer on Wavefronts and Aberrations: the LSST Optical PSF (Aaron Roodman)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Lesson slides
• For both this lesson and the following lesson on PSFs and the atmosphere, the recommended resource is “Introduction to Fourier Optics” by Joseph W. Goodman, 3rd edition (2005), Chapters 1-4, but especially Chapter 2 (Analysis of Two-Dimensional Signals and Systems).
  • Available new/used books online, or Google Books online, or 457-page pdf of 2nd edition (1996).
• Also see this concise lecture from Observational Astronomy at U of Heidelberg:  Diffraction Theory (12-page pdf)
• This lesson uses an iPython notebook. In addition to the usual packages (numpy, scipy, astropy), you need to have cython installed.  Cython is included in Anaconda; otherwise “pip install cython”.
• Download and unpack this tar file  into your working area; then issue the setup command below for the cython code:
  • tar -xvf wavetoimage.tar
  • python adaptive_moments_setup.py build_ext –inplace
• Download this python notebook to get started: WaveToImage Dark Energy School.ipynb

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

Confluence page contains material accessible to DESC members only.

Dark Energy and Lensing: From GR to Data Analysis (Scott Dodelson)

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:

• Understand why it is difficult to understand cosmic acceleration 
• Discuss how modified gravity models differ from dark energy models 
• Explain gravitational lensing and how it probes the growth of structure
• Learn how to code in cosmosis

Lesson Materials and Resources:

• Cosmosis bitbucket repository
• models for cosmic acceleration: http://arxiv.org/abs/0903.0866
• lensing: https://arxiv.org/abs/0805.0139
• lensing and acceleration: http://arxiv.org/abs/1309.5385

Survey Strategy and Dark Energy Systematics (Eric Gawiser)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Lesson Slides
• LSST Overview paper [http://arxiv.org/abs/0805.2366] (particularly sections 2.1.5, 2.2.2, 2.4, 2.7.1, 3.1, 4.1)
• Initial DESC study of dither patterns and cadence [http://arxiv.org/abs/1501.04733]

Future Computing Architectures and Data Analysis (Salman Habib and Adrian Pope)

Watch the class on YouTube here!

Lesson Materials and Resources:

• Lesson Slides: part one (Habib)
• Lesson Slides: part two (Pope)
• OpenMP code

How to simulate our Universe (Katrin Heitmann)

Watch the class on YouTube here! 

Lesson Materials and Resources:

• Lesson Slides
• Exercise material tarball
• How to interpret results of exercise
• Results of exercise

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

Confluence page contains material accessible to DESC members only.

Image processing algorithms: Building science-ready catalogs (Jim Bosch)

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:

• Describe the LSST DM image processing pipelines
• Explain the algorithms used for object detection in images
• Understand how single-epoch detection extends to multiple epochs
• Discuss detection algorithms for different science cases

Lesson Materials and Resources:

• Lesson slides
• LSST Data Products Definition Document
• LSST DM Design, Long-Term To-Do (WIP): Visit Processing, Deep Processing,
• LSST DM Short-Term To-Do and Planning: Science Pipelines on JIRA

LSST’s Cosmological Probes (Shirley Ho)

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:

• Describe how different types of cosmological measurements are made.
• Explain how these measurements are used to infer something about our cosmological model.

Lesson Materials and Resources:

• Resource: “Observational Probes of Cosmological Acceleration”, Weinberg et al., arXiv:1201.2434 (2013)
• This course is based on Shirley’s 2013 class at CMU, Observational Cosmology

Key LSST Design Choices, and How They Were Driven by Science (Steve Kahn)

Watch the class on YouTube here!

Learning Objectives:

After completing this class, students will be able to:

• Identify how LSST science goals impacted design choices.
• Explain the choice of limiting magnitude of 27.5 for the LSST survey, and the interplay between survey depth and breadth.
• Describe how the cadence choices for LSST impact solar system science and the transient optical sky.

Lesson Materials and Resources:

• “LSST: from Science Drivers to Reference Design and Anticipated Data Products”, Ivezic et al., arXiv:0805.2366 (2014)

How to use statistics to describe the large scale structure of the Universe (David Kirkby)

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:

• Describe what we mean by “Large Scale Structure”
• Sketch the relative sizes of benchmarks such as the moon and an LSST chip
• Estimate sigma8 using a coin and a scatter plot of galaxies
• Discuss the limitations of cosmic variance and the assumptions of homogeneity and isotropy
• Explain how r- and k-space basis functions represent the same two-point correlations in different ways
• Match a power spectrum to its corresponding correlation function and Gaussian random field

Lesson Materials:

• Lesson slides
• Red handout
• Blue handout
• Interactive spreadsheet for sigma8 exercise: final version generated during the school (read only) and an empty interactive version to play with.
• github repository and  ipython notebook of support material used to prepare the lecture

Cosmic Co-variance (Michael Schneider)

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:

• Calculate the error ellipse for cosmological parameter constraints, given a covariance matrix.
• Describe the covariance matrix for the Fourier transform and power spectrum of  a Gaussian random field.
• Describe how to obtain the covariance matrix for the galaxy power spectrum, using Nbody simulations.
• Evaluate different approaches to covariance estimation.

Lesson Materials:

• Lesson slides 
• Widget for exercise 1

How the Physics of Sensors Impacts Dark Energy Science (Chris Stubbs)

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:

• Identify the differences between ideal and real CCDs.
• Describe how CCD features (such as tree rings, charge transport anomolies) impact LSST images, and evaluate their impact on weak lensing measurements.
• Explain what measurements we can make in the lab to identify and correct for CCD effects.

Lesson Materials and Resources:

• Lesson slides
• 28-page overview of CCDs in astronomy. Good introduction if this is a new subject for you. 
• Conference proceedings paper that reviews the practical realities of using real sensors. 
• More recent overview of sensor effects that are specific to deep depletion CCDs, the kind that LSST will use.