ASTR 600

Spring Semester 2015 Room HBH 254; MWF 1:00-2:00


Instructor: Dr. Patrick Hartigan, Hermann Brown Rm. 350 , Phone: X2245

Office Hrs: After class and by appt.


Grading: Based on short presentations (20%), homework creation, grading, and leading the discussion for the problems (25%), long presentations (25%), completing homework problems (10%), and participation (20%). The class format only functions well if everyone has their responsibilites completed on time. If a homework set is not created or a long presentation is not done the student will receive a 0 for that grade. The first instance will be weighted 50% in the average, and the remaining ones 100%. The problem-creation+long-presentation grade is 50% of the total, and there are three such events for each student during the semester, so it is important to have these ready on time. For example, a student who otherwise gets straight A's on all other work who does not complete a long presentation or homework-creation when it is scheduled will drop a full letter grade to a B.

Prereqs: There are no statistical prerequisites. We will cover material as we go.

Every discipline of science has data, and a researcher generally wants to address a scientific question with the data. The path from the data to the conclusion usually involves some sort of statistical analysis. Even if the problem is not inherently a statistical one, when writing up the results one must always address the question of how certain the conclusions are given that there are uncertainties in the measurements. Often one looks for correlations between different variables, or tries to infer what the underlying structural variables are that define the system, and how they relate to one another. The statistical literature is vast and different disciplines apply different techniques that are suitable to their data. But in most cases the techniques are quite broad, and in principle may be used in many different contexts.

In this class we will identify the statistical methods commonly used by research papers in astrophysics, and the students will chose papers to study that use these methods. By learning how statistics is used in different areas, we will broaden our toolset for use in our own studies. We will have short discussions on many such papers to get an overview of the usage, and then more in-depth discussions that will be led by students on their favorite methods. To solidify this knowledge, students will sometimes construct problems that will be distributed to the remainder of the class, and after a period of time we will discuss the solutions.

The topics will be guided by the professor, but the papers are chosen by the students. Some examples may include hypothesis testing and confidence intervals, extracting periods and signals from time-series data, using principal component analysis, maximum likelihood methods, finding groups and clusters in multidimensional data, maximum entropy reconstructions, non-parametric ranks, optimized profile extractions, Bayesian analysis with priors and so on. Most statistical methods used in astronomy will fall under one of the broad topics in this course, but if you have seen a technique and wondered how it works, we can substitute it for one of the other topics if there is interest. find a paper that uses it and we will study it.

In class we will, (*) find a broad range of papers chosen by the students that use a particular method, (*) study the mathematics of the method, (*) go in-depth into two papers chosen by the students, and (*) work through some problems to understand how the method works in practice. The students will pick the papers, and will be expected to lead the class through an overview of the science objectives. The main focus, however, will be on the method, which we will then all work together to try to understand. The problems may apply the method to some other case, perhaps with contrived data, so we are all sure we could use it if the need arose in another context. There will be a few lectures at the beginning and then interspersed as needed throughout the semester to provide some mathematical structure, and we will follow the overall format of the textbook. But the choice of specific applications is student-driven. The papers need not necessarily be astronomical, though that will likely be the focus of much of our studies.

Work Load, Absence and Late Policies:

(This section is now required by the Rice Administration for all Syllabi)

Students completing this class should be able to do the following: SPRING 2015 Schedule

DATE Topic Class Type Subject and Items Due
W Jan 21 Mathematical Foundation Lecture Probability Distributions Chapter 2
F Jan 23 " " "
M Jan 26 " " Confidence Intervals Chapters 3 and 4
W Jan 28 " " "
F Jan 30 " " HMWK#1 handout [pmh]
M Feb 2 Nonparametrics Short presentations [Everyone] Paper discussions
W Feb 4 " Lecture Chapter 5
F Feb 6 " " "
M Feb 9 " " HMWK#1 due [Everyone]; discussion
W Feb 11 " Long paper [Willie] Paper discussion + HMWK#2 handout [Amanda]
F Feb 13 Regression Short presentations [Everyone] Paper discussions
M Feb 16 " Lecture Chapter 7
W Feb 18 " " "
F Feb 20 Data Smoothing " Chapter 6; HMWK #2 due [All but Amanda] and discussion
M Feb 23 Regression Long paper [Andy] Paper discussion + HMWK#3 handout [Josh]
W Feb 25 Multivariate, PCA Short presentations [Everyone] Paper discussions
F Feb 27 " Lecture Chapter 8
M Mar 2 - F Mar 6 Midterm RECESS [NO CLASS] ---
M Mar 9 " " "
W Mar 11 - F Mar 13 pmh and Andy on travel [NO CLASS] ---
M Mar 16 " " HMWK #3 due [All but Josh] and discussion
W Mar 18 Multivariate PCA Long paper [Amanda] Paper discussion + HMWK#4 handout [Willie]
F Mar 20 Time-Series Analysis Short presentations [Everyone] Paper discussions
M Mar 23 " Lecture Chapter 11
W Mar 25 " " "
F Mar 27 " " HMWK #4 due [All but Willie] and discussio
M Mar 30 " Long paper [Josh] Paper discussion + HMWK#5 handout [Andy]
W Apr 1 Clustering Short presentations [Everyone] Paper discussions
F Apr 3 Easter RECESS [NO CLASS] ---
M Apr 6 " Lecture Chapter 9
W Apr 8 " " "
F Apr 10 " " HMWK #5 due [All but Andy] and discussion
M Apr 13 " Long paper [Willie] Paper discussion + HMWK#6 handout [Amanda]
W Apr 15 Truncated Data Short presentations [Everyone] Paper discussions
F Apr 17 " Lecture Chapter 10
M Apr 19 " " "
W Apr 22 " " HMWK #6 due [All but Amanda] and discussion
F Apr 24 " Long paper [Andy] Paper discussion + HMWK#7 handout [Josh]
M May 1 " HMWK #7 due [All but Josh]

Honor Code: A general description of the honor code is avilable on-line. Students should turn in their own work and analysis on the homework sets, but may discuss the general nature of the problems with one-another.

Disability Accommodation: If you have a documented disability that will impact your work in this class, please contact me to discuss your needs. Additionally, you will need to register with the Disability Support Services Office in the Ley Student Center.

Short summary of techniques