Near-IR Integral Field Spectroscopy SINFONI and HARMONI Javier Piqueras López (University of Oxford) 2nd SELGIFS Advance School on IFS Data Analysis 2016
Overview of the talk ๏ Introduction: IFS in the Near-IR ๏ VLT/SINFONI ๏ Instrument configurations ๏ Data reduction, calibration and analysis of SINFONI data ๏ E-ELT/HARMONI 2
IFS in the NIR: basic concepts ๏ Integral field spectrograph = Spectrograph + Integral field unit ๏ IFU: divides the 2D FoV into a continuous array ๏ Lenslet array: input image split up by a microlens array ๏ Fibres: input image formed on a bundle of optical fibers ๏ Fibres + lenslets: array of lenslets in front of the fibre bundle ๏ Image slicer: input image formed on a mirror that re-arrange the image into a pseudoslit Focal plane Spectrograph input Spectrograph output y λ Adapted from Alington-Smith and Content (1998) x 3
IFS in the NIR: basic concepts 4
IFS in the NIR: basic concepts 2000 30 Sky lines 25 20 Slitlet Defect 15 1500 10 Wavelength 5 Pixel 1000 Bad pixels Object Slitlets 500 Object line 500 1000 1500 2000 Pixel 5
SINFONI: the NIR IFU at VLT ๏ Near-IR (1.1-2.45) integral field spectrograph at the Cassegrain focus of VLT- UT4 ( Eisenhauer et al. 2003, Bonnet et al 2004 ) ๏ Seeing-limited and AO- assisted observations ๏ Four gratings: J, H, K and H +K ๏ ~4000 individual spectra per data cube 6
SINFONI configurations ๏ Spectrograph modes: ๏ Field of view: ๏ 8”x8”, plate scale of 125x250 mas / spaxel (seeing- limited) ๏ 3”x3”, plate scale of 50x100 mas / spaxel (seeing- limited, AO) ๏ 0.8”x0.8”, plate scale of 12.5x25 mas / spaxel (AO) ๏ Wavelength bands: ๏ J [1.10 – 1.40] μ m, R~2000, FWHM ~ 4pix ๏ H [1.45—1.85] μ m, R~3000, FWHM ~ 3pix ๏ K [1.95—2.45] μ m, R~4000, FWHM ~ 2pix ๏ H+K [1.45—2.45] μ m, R~1500, FWHM ~ 2pix 7
Data reduction and calibration Data and reduction workflow Master dark On-source (SCI) Sky (SCI) Linearity On-source (STD) Sky (STD) Distortion Calibration frames (~nigth) Master flat field Calibration frames (~month) Wavelength calibration Calibration frames (~static) Cube reconstruction 8
SINFONI calibration dataset ๏ A complete dataset should contain: ๏ Science frames: on-source frames + sky frames [OBJECT, SKY] ๏ Dark frames: ~3 frames per DIT [DARK] ๏ Linearity frames: ~24 frames [LINEARITY_LAMP] ๏ Distortion frames: ~75 fibre frames + 2 flat fields + 2 lamp frames [FIBRE_NS; FLAT_NS; WAVE_NS] ๏ Lamp frames: ~2 frames, lamp on and off [WAVE_LAMP] ๏ STD star frames: ~1 on-source + 1 sky (optional) [STD,SKY_STD] ๏ In addition, some static calibration tables are needed (provided with the pipeline) 9
Thermal background and sky emission 8 Flux density [x10 -11 erg s -1 cm -2 µ m -1 arcsec -2 ] H - b a n d K - b a n d Vibrational transitions 6 2-0 3-1 4-2 5-3 6-4 8-6 9-7 4 2 Thermal background 0 1.4 1.6 1.8 2.0 2.2 2.4 Wavelength [ µ m] ๏ Two sources: ๏ Thermal background: atmospheric (+ telescope) emission dominates beyond ~2.3 μ m ๏ Airglow emission: OH vibrational lines that dominates below ~2.3 μ m 10
Atmospheric transmission: Efficiency curves ๏ Atmospheric absorption in the near-IR: vibrational transitions of water vapor ๏ Depends on the airmass, varies with time… ๏ Although it can be modeled, it is usually corrected using standard stars: efficiency curves H+K band 4 Spectrum 3 Normalised flux 2 1 0 1.4 1.6 1.8 2.0 2.2 2.4 Wavelength [ µ m] 11
Atmospheric transmission: Efficiency curves ๏ Atmospheric absorption in the near-IR: vibrational transitions of water vapor ๏ Depends on the airmass, varies with time… ๏ Although it can be modeled, it is usually corrected using standard stars: efficiency curves H+K band 4 Spectrum BB fit 3 Normalised flux 2 1 0 1.4 1.6 1.8 2.0 2.2 2.4 Wavelength [ µ m] 12
Atmospheric transmission: Efficiency curves ๏ Atmospheric absorption in the near-IR: vibrational transitions of water vapor ๏ Depends on the airmass, varies with time… ๏ Although it can be modeled, it is usually corrected using standard stars: efficiency curves H+K band 4 Spectrum Efficiency BB fit 3 Normalised flux 2 1 0 1.4 1.6 1.8 2.0 2.2 2.4 Wavelength [ µ m] 13
Instrumental line profiles H-band (super-sampled) H-band 1.0 1.0 Median 0.8 0.8 Gaussian Spectral resolution in SINFONI ๏ 0.6 0.6 depends on pre-optics: Band ๏ 0.4 0.4 Plate scale ๏ 0.2 0.2 Wavelength ๏ 0.0 0.0 Line profile can be characterized ๏ -0.2 -0.2 using arc/sky lines 0 50 100 150 0 5 10 15 K-band (super-sampled) K-band H-band, 250 mas, [1.62,1.70] μ m ๏ 1.0 1.0 ~[FeII] line 0.8 0.8 K-band, 250 mas, [2.12,2.22] μ m ๏ ~H 2 1-0S(1) and Br γ lines 0.6 0.6 Kinematic studies: It is essential to ๏ 0.4 0.4 characterize the instrumental line profile 0.2 0.2 0.0 0.0 -0.2 -0.2 0 50 100 150 0 5 10 15 14
European Extremely Large Telescope: E-ELT 15
HARMONI General Assembly 10 m 5 m 8.75 m 16
HARMONI in a nutshell ๏ Optical and near-IR IFS (~ 32000 spectra) ๏ Workhorse instrument: design for a wide range of scientific programs ๏ LTAO, SCAO and seeing-limited observations ๏ Spectral coverage ๏ Wavelength range: 0.47 - 2.45 μ m ๏ Spectral resolution: 3000, 7000, 18000 ๏ Spatial setup: ๏ FoV: ~9”x6”, 4”x3”, 2”x1.5”, 0.8”x0.6” ๏ Pixel scales: ~30 mas, 20 mas, 10 mas, 4 mas ๏ Sensitivity: up to H AB ~27.4 mag (5h, S/N~5, LTAO) 17
HARMONI spatial set-up 60 mas x 30 mas 6.42” x 9.12” For non-AO & visible observations 3.04” x 4.28” 20 mas For optimal sensitivity (faint 1.52” x 2.14” targets) ~214 x 152 (32000) spaxels at all Best combination 10 mas 0.61” x 0.86” scales sensitivity & spatial resolution Highest spatial 4 mas resolution (diffraction limited) 18
HARMONI simulations of high-z (U)LIRGs 1.0 20 0.5 10 y [arcmin] y [arcsec] 0.0 0 -10 -0.5 500 pc 2 kpc -20 -10 -5 0 5 10 x [arcsec] -1.0 -1.0 -0.5 0.0 0.5 1.0 x [arcmin] 19
HARMONI simulations of high-z (U)LIRGs HSIM maps @ z ~2.26 SCAO + 4 mas, T exp ~8h H α emission v [km/s] σ [km/s] 100 80 0.4 0.4 0.4 67 67 0.2 0.2 0.2 33 53 y [arcsec] 0.0 0.0 0.0 0 40 -33 27 -0.2 -0.2 -0.2 -67 13 1 kpc 1 kpc 1 kpc -0.4 -0.4 -0.4 -100 0 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 x [arcsec] x [arcsec] x [arcsec] physical scales of ~70 pc 20
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