Astronomy 8824 (Prof. Martini, Autumn 2019) skip navigation

Astronomy 8824
Numerical and Statistical Methods in Astrophysics

Autumn Semester 2019

Prof. Paul Martini
TTh 8:50-10:20am
4054 McPherson Laboratory

[Contact | Syllabus | Evaluation | Books and References | Schedule and Readings ]


Office: 4021 McPherson Lab
Office Phone: 614-292-8632
Office Hours: by appointment or whenever my door is open


The class syllabus and course outline are in this PDF File.


The course grade will be based on problem sets (70%), and class participation and presentations (30%). There will not be a final exam. Here are the problem sets and due dates:
Problem Set 1 Due Sept 3
Problem Set 2 Due Sept 17 (Starter Notebook)
Problem Set 3 Due Oct 1
Problem Set 4 Due Oct 29 (Starter Notebook)
Problem Set 5 Due Nov 12
Problem Set 6 Due Nov 26
Problem Set 7 Due Dec 6 (ps7data.tgz)

The Schedule and Readings section below includes the in-class presentations.

Books and References

I recommend the following two books:

Numerical Recipes by Press, Teukolsky, Vetterling, and Flannery.

This book is a good introduction to many numerical methods, and is available for a number of programming languages (although unfortunately not python). It should be more useful for descriptions of numerical methods than for the routines. The 3rd edition has more information than the previous ones, although the other editions have the essentials.

Statistics, Data Mining, and Machine Learning in Astronomy by Ivezic, Connolly, VanderPlas, and Gray.

We will mostly cover the first five chapters, which are the chapters that focus on statistics. Please read David Weinberg's Reader's Guide to Chapters 3-5 before you start reading. The book uses python, and so is a good way to improve your knowledge of that language.

If you are not already conversant in python, I recommend that you also obtain an introduction to the python programming language. One geared toward scientific programming is likely to be most relevant. David Weinberg has recommended this one to past classes, and I think it is really good too:

Effective Computation in Physics: Field Guide to Research with Python by Scopatz and Huff.

Python References

Here are links to other python references:
Demitri Muna's Introduction to Python Slides
Thomas Robitaille's Python for Scientists
The python module AstroML was started to accompany Statistics, Data Mining, and Machine Learning in Astronomy and contains numerous resources
These references are primarily aimed at scientists. There are also a seemingly exponentially increasing number of other guides and tutorials online.


The following are all of the Tuesdays and Thursdays in the Autumn Semester. If we have class on a given day, I will include the topic, reading(s), and any in class presentation for that day. If definitely will not meet, I will make that clear. And if I have not determined this yet, I will mark the date with "TBD."

August 20 (Tuesday): Numerical Integration

Readings: David Weinberg's Numerical Notes 1: Numerical Integration. Numerical Recipes Chapter 4, focus on 4.0 to 4.4.

August 22 (Thursday): No Class - Department Symposium

August 27 (Tuesday): No Class

August 29 (Thursday): No Class

September 3 (Tuesday): Finish Numerical Integration, start Ordinary Differential Equations

Readings: David Weinberg's Numerical Notes 2: Ordinary Differential Equations. Numerical Recipes Chapter on ODEs, first three sections

September 5 (Thursday): Finish ODEs, Start Root Finding and Minimization

Readings: David Weinberg's Numerical Notes 3: Root Finding and Minimization. Numerical Recipes Chapter 9, focus on 9.0, 9.1, and 9.4

September 10 (Tuesday): No Class

September 12 (Thursday): No Class

September 17 (Tuesday): Finish Root Finding, Start Intro to Statustics

Readings: David Weinberg's Statistical Notes 1: Background. Statistics, Data Mining, ... Chapter 3

September 19 (Thursday): Finish Intro to Statistics

September 24 (Tuesday): No Class

September 26 (Thursday): No Class

October 1 (Tuesday): Bayes Parameter Estimation

Readings: David Weinberg's Statistical Notes 2: Bayesian Parameter Estimation

October 3 (Thursday): Finish Bayes, Start Stats 3

Readings: David Weinberg's Statistical Notes 3: Errors, Likelihood, and MCMC

October 8 (Tuesday): No Class

October 10 (Thursday): No Class - Autumn Break

October 15 (Tuesday): Finish Stats 3

October 17 (Thursday): Stats 4 (Fisher Matrices)

Presentation by Warfield: pandas
Presentation by Pai: Gehrels (1986)
Readings: David Weinberg's Statistical Notes 4: Fisher Matrices
Readings: David Wittman's Fisher Matrix for Beginners
Readings: Gould (2003) Chi^2 and Linear Fits

October 22 (Tuesday): No Class

October 24 (Thursday): No Class

October 29 (Tuesday): Stats 5: Hypothesis Testing

Presentation by Duck: healpy
Readings: David Weinberg's Statistical Notes 5: Hypothesis Testing

October 31 (Thursday): Finish Stats 5

Presentation by Boley: scitkit-learn classification
Presentation by Neustadt: astropy.table

November 5 (Tuesday): No Class

November 7 (Thursday): No Class

November 12 (Tuesday): Numerical 4: FFTs

Presentation by Phillips: github
Presentation by Duck: Isobe et al. (1990)
Readings: David Weinberg's Numerical Notes 4: FFTs and Their Applications

November 14 (Thursday): Numerical 4: FFTs

Presentation by Pai: scikit-lean dimensionality reduction
Presentation by Warfield: Gott et al. (2001)

November 19 (Tuesday): Stats 6: Error Estimation

Presentation by Neustadt: Busca and Balland (2018)
Presentation by Phillips: VanderPlas (2017)
Readings: David Weinberg's Statistical Notes 6: Estimating Errors From Data

November 21 (Thursday): Stats 6: Error Estimation

Presentation by Boley: Forman-Mackey et al. (2012)
Presentation by Phillips: VanderPlas (2017)

November 26 (Tuesday): No Class

November 28 (Thursday): No Class - Thanksgiving Holiday

December 3 (Tuesday): Finish Stats 6: Error Estimation - Last Class

Presentation by Mayker: fits
Presentation by Mayker: Kelly (2017)

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