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Observational Astronomy

Observational Astronomy. SPECTROSCOPIC data reduction. Piskunov & Valenti 2002, A&A 385, 1095. Echelle spectrum image looks strange …. Spectral orders. Spectral line. Data reduction concept. Complex data layout requires in addition to the usual CCD calibrations: Spectral order localization

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Observational Astronomy

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  1. Observational Astronomy SPECTROSCOPICdata reduction Piskunov & Valenti 2002, A&A 385, 1095

  2. Echelle spectrum image looks strange … Spectral orders Spectral line

  3. Data reduction concept Complex data layout requires in addition to the usual CCD calibrations: • Spectral order localization • Extracting 2D data into a sequence of 1D spectra • Wavelength calibration • Blaze function correction

  4. To performed data reduction we need calibration data • Bias (shutter closed, 0-time exposures) • Flat field (uniform source, short exposures) • Dark current (shutter closed, different exposures) • Order tracing (flat field with short slit) • Wavelength map(emission line source, short exposures) • Blaze calibration(flat field or a star with minimum sp. lines)

  5. CCD data reduction in one expression The intensity is given by: s – signal in science exposure b – bias level f – flat field signal g – gain (e-/ADU) d – dark current signal per unit time t – exposure times

  6. Complete data reduction sequence • Create master bias and master flat • Trace spectral orders • Normalize master flat • Extract wavelength calibrations • Extract science spectra (optimal extraction) • Fit the continuum • Construct the wavelength solution

  7. Creating master bias and master flat • The goal is to replace the actual calibration data with a model which is free of random noise but carries all the systematic signatures. • S/N of a master must be much larger than the S/N in science frames!!! • Main issue: getting rid of random errors, e.g. cosmic ray hits • Method:filtering within a frame or across a stack of frames • Cross-check between groups of calibration frames

  8. Example of constructing master flat

  9. Stack of individual flats (fragments)

  10. 6 times larger vertical scale Individual flats with master flat subtracted

  11. Flat field Fragment of a master flat field

  12. Order tracing

  13. Order tracing (2)

  14. Pixel number Order number Wavelength calibration Emission line spectrum (ThAr) with known laboratory wavelengths 2D polynomial

  15. Wavelength solutionand the PSF Line analysis for wavelength solution for ESO UVES spectrometer. Points show measured lines (scaled). Blue line shows best Gaussian fit. • Why the Gaussian misses the bulk of points? • Why do you see so much scatter?

  16. Slit function decomposition Idealized model: Image on the CCD is a sequence of monochromatic images of the entrance slit sampled with detector pixels Watershed pattern is due misalignment between spectral order and detector raws.

  17. Normalizing flat field Original flat “Spectrum” Model flat Normalized flat (data/model)

  18. Extractingsciencespectrum • Extraction of science spectrum: • Simple summation • Gaussian fit to the slit function • Optimal extraction

  19. Continuum fit Blaze function is a good start:

  20. Fringing Accurate fringing removal requires slit illumination by the FF and by the science object to be identical Wide flat Narrow flat Science spectrum

  21. Exam • Written exam on Thursday, March 14th, starting 9am sharp • Oral exam: book time with me

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