AS5001 (SUPAAAA) Advanced (Astronomical) Data Analysis

Keith Horne -- University of St.Andrews

Physics and Astronomy Lecture Theatre C + TEAMS online.

10am Mon, 9am Tue & Thu.


Astronomy Picture of the Day .
Homework Set 1 (due Mon 4 Oct 2021) hw1.pdf. Data: bias.dat . Python: PythonCrib.pdf . hw1.html. hw1.ipynb.
PGPLOT (Graphics Subroutine Library): PGPLOT . Subroutine Descriptions . Quick Reference .
Example PGPLOT codes:
Source code: simple.for . Compile/link: simple.com . Resulting plots: simple.ps .pdf .gif
Source code: simplot.for . Compile/link: simplot.com . Resulting plots: simplot.ps .pdf .gif .
Numerical Recipes . Online . D.Hogg - Data Analysis Recipes: Probability Calculus for Inference .
Opinionated Lessons in Statistics by Bill Press.
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, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 , 41 , 47 , 48 , 49 .
The Ways of Our Errors (Incomplete Draft Textbook) woe.ps , .pdf .

2021 ADA Lectures & Videos
ada01, 6. 2021mp4. Astro datasets, noise, statistical/systematic errors, probability distributions, mean, median, variance, MAD, central moments, skewness, kurtosis
ada02, 6. 2021mp4. Parameterised PDFs (Uniform, Gaussian, Lorentzian, Poisson, Exponential, Chi^2), 5-sigma rule, joint distributions, independence vs correlation, algebra of random variables.
ada03, 6. 2021mp4. linear transformations, co-variance and correlation coefficients and matrices. non-linear transformations, error bar propagation, magnitude bias,

2020 ADA Lectures & Videos (to be updated as we go along)
ada04, 6, 2020mp4. Central Limit Theorem, Sample Mean, Optimal Average.
ada05, 6. 2018mp4. Optimal Scaling, Chi^2 fits, 1-sigma at Delta_Chi^2=1.
ada06, 6, 2020mp4. Chi^2 distn, degrees of freedom, sample variance, robust estimation, mean vs median, MAD, SANE, median filtering, sigma-clipping, various BoF statistics.
ada07, 6, 2020mp4. error bars are model parameters, maximum likelihood, parameters affecting sigma, fits to Poisson data, conditional probabilities, Bayes' theorem, Bayesian inference, gaussian priors.
ada08, 6. 2020mp4. Monte-Carlo and Bootstrap sampling, m-pameter k-sigma confidence regions, fit a line, correlated vs orthogonal parameters.
ada09, 6. 2020mp4. Linear Regression, Normal Equations, Hessian Matrix, Non-Linear Models, Linearisation, Amoeba (Downhill Simplex), MCMC (Markov-Chain Monte-Carlo).
ada10, 6. 2020mp4. Vector Space Methods. Scaling Orthogonal Patterns, Hessian Matrix for Non-Linear Models, Graham-Schmidt Orthogonalisation, Orthogonal Polynomials, Reduced Chi^2 for Rejecting Models, Occam's Razor, AIC, AICc, BIC.
ada12, 6. 2020mp4. Chi^2_min Diagnosis. Re-scaling error bars. Extra variance. Background fits - polys, splines, running optimal average, median filter, sigma clip, variance of median.
ada13, 6. 2020mp4. Timing analysis: ephemerides, O-C diagrams. Periodograms: spectral leakage, Fourier basis, Nyquist frequency, aliasing above Nyquist frequency, beating, pre-whitening, harmonics, data gaps.
ada14, 6. 2020mp4. Fourier analysis: amplitude and phase modulation, spike, white noise, dynamic power spectra. wavelets. spline decomposition. red vs white noise.
ada15, 6. 2020mp4. Cross-correlation analysis. Description of ADA Projects 1 & 2.

ADA Lectures (astro-specific) no longer given.
ada16 (handout only). High-Speed Photometry: photomultiplier tubes. coincidence losses. tagging. sky background fits. extinction and absolute calibration.
ada17 (handout only). CCD detectors. Bias and Flat Field calibration. CCD noise model: Readout noise, Gain. CCD Spectroscopy. Differential refraction. Tracing the spectrum. Sky subtraction. Normal vs Optimal extraction.
ada18 (handout only). Wavelength calibration. Extinction corrections including saturated Telluric lines. Absolute flux calibration. Slit loss corrections.
ada19 (handout only). Crowded Field CCD photometry.
ada20 (handout only). Tomography.
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Keith Horne (kdh1-nospam-@st-andrews.ac.uk)