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KMOS Instrument Overview & Data Processing

KMOS Instrument Overview & Data Processing. Richard Davies Max Planck Institute for Extraterrestrial Physics. What does KMOS do? When will it do it? What does the data look like? How is the data processed?. What & When?. Phase B start July 2004 Preliminary Design Review May 2006

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KMOS Instrument Overview & Data Processing

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  1. KMOS Instrument Overview & Data Processing Richard Davies Max Planck Institute for Extraterrestrial Physics • What does KMOS do? • When will it do it? • What does the data look like? • How is the data processed?

  2. What & When? • Phase B start July 2004 • Preliminary Design Review May 2006 • Final Design Review July 2007 • Preliminary Acceptance Europe Spring 2010 • Preliminary Acceptance Chile Autumn 2010 2m 2800kg

  3. Science Drivers • Investigate the physical processes which drive galaxy formation and evolution over redshift range 1<z<10 • Map the variations in star formation histories, spatially resolved star-formation properties, and merger rates • Obtain dynamical masses of well-defined samples of galaxies across a wide range of environments at a series of progressively earlier epochs need: multiplexing (large numbers of sources), NIR (optical diagnostics at z>1), moderate spectral resolution (kinematics), integral field (mergers vs disks)

  4. roof mirror Instrumental Features pick off mirror (covered) multiple-object cryogenic integral field spectrograph to K-mirror & filter wheel • R~3500 spectroscopy at 0.8-2.5m • 7.2arcmin patrol field • 24 robotic pickoff arms, each with a 2.8”×2.8” FoV sampled at 0.2 arcsec • IFUs are consolidated in groups of 8 • each set feeds one of 3 identical spectrographs

  5. Instrumental Features • 24 arms in 2 layers, 20mm above & below focal plane • positioning within 0.1” (<60μm) • mass ~4.5kg each • size ~30cm • each path has 45 optical surfaces • in total 1080 optical surfaces and 60 cryogenic motors

  6. Instrumental Configuration(s) Fixed instrument configuration: Instrument configuration options:

  7. Raw Data Format first RTD: raw data from the 3 2k×2k detectors wavelength spatial position 14 pixels per slitlet (plus a gap) 14 slitlets per IFU 8 IFUs per detector 3 detectors IFU 2 IFU 1

  8. Reconstructed Images second RTD: reconstructed images for each of the 24 IFUs either arrayed in a grid

  9. Reconstructed Images second RTD: reconstructed images for each of the 24 IFUs or positioned in the 7.2’ patrol field

  10. Association Map

  11. Templates & Recipes calibration templates & recipes: KMOS_spec_cal_dark KMOS_spec_cal_calunit KMOS_spec_cal_skyflat KMOS_spec_tec_verticalslit KMOS_spec_cal_wave KMOS_spec_cal_std kmo_dark kmo_flat kmo_illumination kmo_spec_align kmo_wave_cal kmo_std_star science templates & recipes: any acquisition frame KMOS_spec_obs_nodtosky KMOS_spec_obs_stare KMOS_spec_obs_mapping kmo_rtd_image kmo_sci_red note: reconstruction works on 1 IFU at a time (i.e. in effect recipe runs 24 times for each data set).

  12. other Recipes • Modular design also useful to observer when re-processing their data back home Basic Tools used in recipes: kmo_create_cube kmo_set_value kmo_arithmetic kmo_stats kmo_copy kmo_rotate kmo_shift kmo_flip_axis kmo_euro3D_convert More Complex Tools used in recipes: kmo_reconstruct kmo_make_image kmo_extract_spec kmo_combine kmo_sky_mask* kmo_sky_tweak* kmo_bkg_sub* kmo_fit_profile kmo_cosmic*† Additional (Advanced) Tools: kmo_extract_pv* kmo_fit_continuum kmo_extract_moments* kmo_convolve kmo_median kmo_voronoi* * = prototype version in use for SINFONI data † = based on ‘L.A.Cosmic’ by P. van Dokkum

  13. Recipe Hierarchy

  14. What KMOS will & won’t do • Things we will do (and think are a good idea) • keep everything modular so astronomers can add in their own extra processing steps or leave some out • provide basic tools so astronomer can manipulate their datacubes • provide some more advanced tools to extract information from a datacube (e.g. emission line kinematics, Voronoi binning, etc) • Things we won’t be providing • a 3D data viewing tool (since there are already many good ones, e.g. QFitsView) • tools for deconvolution, line deblending, extracting stellar kinematics, etc (because they’re very user/data/model dependent) • mosaicing tool – it will be possible to combine datacubes with the right offsets to make a larger field, but no scaling/background adjustments will be made

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