Class handouts and assignments
Course Information and syllabus
(handout at first class; updated November 16)
Our textbook, Statistics, Data Mining, and Machine
Learning in Astronomy is available
online
(this may require using a Princeton computer).
Homework 1, due Monday, September
26, in class.
Solutions to Homework 1.
Homework 2, due Wednesday, October
5.
The quasar spectrum needed for
Homework 2. This is a spectrum from the SDSS; the columns are
wavelength, flux density (in units of 10^{-17}erg/s/cm^{2}/A),
flux density error (same units), and a mask that indicates possible problems
with each pixel. For this homework, you need only use the
first two columns of data.
Solutions to Homework 2, together
with the Python code needed to do Problems 1 and 2.
Homework 3, due Monday, October
17.
The A star spectrum needed for
Homework 3. First two columns are wavelength in Angstroms and flux density
(in units of 10^{-17}erg/s/cm^{2}/A).
Solutions to Homework 3, together
with the Python code needed to do Problem 3.
Homework 4, due Monday, November
7.
Solutions to Homework 4, together
with the Python code needed to do Problem 4.
Homework 5, due Thursday, November
13. The data file needed for Problem 1.
Solutions to Homework 5, together
with the Python code needed to do Problem 1.
Homework 6, due Wednesday, November
30.
Solutions to Homework 6
Homework 7, due Tuesday, January 17
(Dean's Date; hand into Michael's office), together with
the sky spectrum needed for Problem 3.
Resources for the final JWST proposal
project (updated December 1).
Computers and Python resources
A description of how to get started using
computers in Peyton Hall, including information on python.
A brief introduction to Unix at
Princeton, by Robert Lupton and Jill Knapp.
An introduction to X windows (the
window-manager system that many of the computers in the building use), by Robert Lupton.
An alternative
introduction to Unix; the bare minimum is contained in the first five tutorials.
A General Introduction
to Python, including numpy and SciPy.
Programming in
Python, for astronomers.
Python
for Data Analysis, a 470-page textbook available online.
An introduction to
SciPy.
Astropy, a project for
building useful utilities for astronomers in Python.
There is a
blog
associated with the book A Student's Guide to Python for Physical
Modeling.
AstroML is a website
accompanying the book Statistics, Data Mining, and Machine Learning
in Astronomy.
Useful
ipython notebooks from Jake Vander Plas.
Unix
and Python reference page from Physics 209.
The NIST Digital Library of
Mathematical Functions, an update of the classic handbook by
Abramowitz and Stegun.
The Second Edition of Numerical Recipes (i.e., not the latest
version) is available for free on the
web in C, Fortran 77, and Fortran 90.
General Astronomy Resources
Useful astronomical
links. These are from AST 203, so tend to the elementary, and are
a bit dated...
Science
White Papers for the James Webb Space Telescope.
The weekly
calendar of astrophysics-related talks in the Princeton area.
ArXiv, the repository of the daily
preprints of the astrophysics community, often referred to as
"astro-ph". There is a page describing how to sign
up to receive a daily update of astrophysics papers, and a website organizing
the Peyton Hall daily discussion of these papers.
The Astrophysics Data
System, a portal to essentially the complete journal literature of
astronomy.
AstroBetter, a blog where
professional astronomers share tips and tricks forbeing successful
in the astronomy world.
Astrobites, a website
run by grad students for undergrads, where they
summarize interesting astro-ph articles and provide general tips.
The full, downloadable report of ASTRO2010,
The Astronomy and Astrophysics Decadal Survey, and the
2016 Midterm
Report.
Professors:
Michael Strauss and
Jenny Greene.