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Observational Astrophysics II (L3). What do want to do? Nightly planning overwiew Reduce spectroscopic observations Reduce photometric imaging observations Perhaps, `massage´ our images. http://www.astro.su.se/utbildning/kurser/astro_obs2/. http://www.not.iac.es/observing/cookbook. 23:30.
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Observational Astrophysics II (L3) • What do want to do? • Nightly planning overwiew • Reduce spectroscopic observations • Reduce photometric imaging observations • Perhaps, `massage´ our images http://www.astro.su.se/utbildning/kurser/astro_obs2/ Observational Astrophysics II: May-June, 2004
http://www.not.iac.es/observing/cookbook Observational Astrophysics II: May-June, 2004
23:30 01:15 03:00 07:00 19:00 Grupp 1 + Alla ->23:30 Grupp 3 ->03:00 Grupp 2 ->01:15 Grupp 4 + Alla? 7 h a-natt => 1h45m/ grp n-tid Observational Astrophysics II: May-June, 2004
Data Reductions neither from theoretical nor from reduction point of view any fundamental difference between Spectroscopic image frames Photometric image frames Observational Astrophysics II: May-June, 2004
Correct for / obtain from multiple image frames: • IRAF • Bias imarith • Dark current • Hot/cold columns • Sky background • Cosmic Rays imcombine • Flat Field • Photometric calibration apphot • Spectrometric calibration identify • rectify spectrum in spatial domainlongslit – fitcoords, transform • extract spectrum noao.twodspec.apextract – apall • measure lines (Gaussian fitting) splot • slit losses sbands Observational Astrophysics II: May-June, 2004
Image restauration techniques How to recover the information in an `image´ or, actually, How to optimise the information extraction Observational Astrophysics II: May-June, 2004
Image restauration techniques I is the observed image, which is a function of the angle vector q, and I equals the convolution of the object O with the filter function T. cumbersome simple An equivalent expression is the product of the Fourier Transforms o and t. To derive the object O, one would simply divide i by t and transform back. Observational Astrophysics II: May-June, 2004
Image restauration techniques In practice, this involves division by zero (or very small numbers) and therefore is impractical numerically. One way out are suggestions like: Jan Högbom, em. Stockhom Observatory CLEAN Maximum Entropy Method (MEM) Maximum Likelihood Method Inversion techniques use conditions like: Source has positivity Source has bounded support Observational Astrophysics II: May-June, 2004
Image restauration techniques • Maximum Entropy Method (MEM) / Maximum Likelihood Method • Most probableobject O is that which • Is most consistent with observed image I • Uses least extra information Image `entropy´ is a function which is maximal when image contains minimal (extra) information: max Entropy for Equilibrium min Information Observational Astrophysics II: May-June, 2004
Example: Maximum Likelihood Algorithm (modified Richardson-Lucy) input undersampled observations Restored Image + Noise source positions ? Observational Astrophysics II: May-June, 2004
test case Iteration Number 0 = start value 1 2 3 done! 4 5 Larsson et al. 2000, Astron. Astrophys. 363, 253 Observational Astrophysics II: May-June, 2004
Before we go to the mountain... don´t forget http://www.not.iac.es/observing/cookbook Observational Astrophysics II: May-June, 2004
... och nu en liten övning: Slut ögonen och tänk dig att du närmar dig teleskopdomen... Beskriv (i telegramstil) vad du gör härnäst – steg för steg när du nu ska observera med NOT • numrera gärna stegen • lämna gärna mellanrum mellan stegen • vi kommer att jämföra i realtid och fylla i vid behov glöm inte att skriva ditt namn Observational Astrophysics II: May-June, 2004
