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Astronomy 193 Noise and Data Analysis in Astrophysics
Spring 2015
Time & Place Mondays and Wednesdays, 2:003:30 pm Observatory Classroom A101 Aneta Siemiginowska B425, 60 Garden St. MS4 phone 6174957243 email: asiemiginowska@cfa.harvard.edu Website: http://heawww.harvard.edu/~aneta/HomePage.html Office hours: by appointment, or drop in Tue 11am12pm Vinay Kashyap 60 Garden St. MS70, B301 Phone: 6174957173 email: vkashyap@cfa.harvard.edu Office hours: by appointment, or drop in Fri 23pm http://isites.harvard.edu/k109189 Math 21b or equivalent (Taylor series, multivariate calculus, elementary complex variables, some linear algebra, some probability). Some of the homework will involve computer simulations and access to the random number generator. Computer programming experience (e.g., R, Python, IDL, perl, Fortran, C) is essential. Homework: about 1520 assignments (60%) Class participation: (20%) Challenge project final paper and presentation (20%) The following four books are recommended: Practical Statistics for Astronomers Wall & Jenkins http://id.lib.harvard.edu/aleph/013863621/catalog Bayesian Data Analysis Gelman et al
http://id.lib.harvard.edu/aleph/013863621/catalog

Instructors:

Course Website: Prerequisites:

Work:

Textbooks:



Data Analysis for Scientists and Engineers Edward Robinson
http://www.as.utexas.edu/~elr/DataAnalysis/

Numerical Recipes Press, Flannery, Teukolsky, Vetterling


Other references: Data reduction and error analysis for the physical sciences Bevington and Robinson
http://id.lib.harvard.edu/aleph/009001685/catalog

Astrostatistics Babu & Feigelson
http://id.lib.harvard.edu/aleph/007067433/catalog

Handbook for Xray Astronomy Arnaud, Smith, & Siemiginowska http://id.lib.harvard.edu/aleph/013078357/catalog Papers and monographs

Statement on Collaboration, Required by Harvard University Collaboration Permitted on Problem Sets Discussion and the exchange of ideas are essential to doing academic work. For assignments in this course, you are encouraged to consult with your classmates as you work on problem sets. However, after discussions with peers, make sure that you can work through the problem yourself and ensure that any answers you submit for evaluation are the result of your own efforts. In addition, you must cite any books, articles, websites, lectures, etc., that have helped you with your work, using appropriate citation practices.


Astronomy 193 Noise and Data Analysis in Astrophysics Syllabus
I. II. Introduction Data (2) : Radio, Optical, Xray data, Calibration, Measurement process Basics of Model Fitting (2) : R and Python environments Bestfit, Error bars, Goodness of fit Probability Theory (2) : Distributions (Poisson Gaussian Binomials t Cauchy MultiVariate Normal) Probabilities (absolute and conditional axioms) Random Numbers (draws sampling), Bootstrap Central Limit Theorem Parameter Estimation (2) : Maximum Likelihood Least squares fitting (downhill simplex, levenbergmarquardt, etc.) Bayes' Theorem (Prior distribution Posterior distributions) Confidence/Credible intervals MCMC (2) : M, MH, Gibbs (Partially Collapsed Gibbs) Validation (multiple chains) Model Selection (2) : Hypothesis Tests Type I and Type II (detections, false +ve/ve, power) LRT, ppp, AIC/BIC/DIC Nonparametric tests (2) : KS, Bootstrap, KM Foundations of Signal Processing (2) : FFT, Wavelets Stochastic Processes and Time Series (4) : Power Spectra, Periodic phenomena Correlations/Structure Functions Gaussian Processes, 1/f noise (Brownian motion, OU processes, CAR) Image Processing (2) : Filtering (FFT), Deconvolution methods (maxent limitations), Smoothing Source Detection (scanning statistics wavdetect nondetections)

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VII. VIII XI.

XII.




Astronomy 193 Noise and Data Analysis in Astrophysics
Spring 2015 Tentative Schedule of Lectures
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. M Jan. 26 Introduction W Jan.28 Data I M Feb. 2 Data II W Feb. 4 Basics of Model Fitting I M Feb. 9 Basics of Model Fitting II W Feb 11 Probability Theory I M Feb 16 President's Day holiday no class W Feb 18 Probability Theory II M Feb 23 Parameter Estimation I W Feb 25 Parameter Estimation II M Mar 2 MCMC I W Mar 4 MCMC II M Mar 9 Model Selection I W Mar 11 Model Selection II M Mar 16 Spring break W Mar 18 Spring break M Mar 23 Nonparametric tests I W Mar 25 Nonparametric tests II M Mar 30 Foundations of Signal Processing I W Apr 1 Foundations of Signal Processing II M Apr 6 Stochastic Processes and Time Series I W Apr 8 Stochastic Processes and Time Series II M Apr 13 Stochastic Processes and Time Series III W Apr 15 Stochastic Processes and Time Series IV M Apr 20 Image Processing I W Apr 22 Image Processing II M Apr 27 Review W Apr 29 Project Presentations M May 4 W May 6 M May 11 W May 13