End to End Simulations

p.2 What’s this ? This is the MUSE datacube of NGC 1068 we just received from ESO Can you remind me how many students we have left ?

p.3 End to End Modelling Data Reduction System Atmos. & AO simulations Astro. Scene Simulations Instrument Numerical Model Data Analysis Software Tools Validation Prototype OK WFM OK NFM Apr 09 Tools & format released Stars & Galaxies Fields First released Dec 09 ? AO PSF modeling WFM OK NFM Apr 09 Quick Simulation QSIM OK Stars & Galaxies Datacube ANR DAHLIA 09-12

p.4 Data Format  Raw data – Fits file – 0: header extension – 1..24: image extensions  Reduced data – Fits file – 0: header extension – 1: 3D data extension (3D image :x:y) – 2: 3D variance extension – 3: 3D bad pixel flag

p.5 Process  Semi-analytical model of galaxy formation (Jeremy)  Datacubes at MUSE spatial and spectral resolution  Noisy datacubes  Analysis

p.6 Semi-analytical model of galaxy formation (1)  Millennium simulation (De Lucia & Blaizot, 2007; Springel et al., 2005)  SAM (dark matter halo -> galaxies) – Catalog selection  K < 31 & FOV=1.2x1.2 arcmin² – Output  X, Y, Rdisk, B/T ratio, Star formation history  Image creation – Exponential disk + Bulge (Hernquist) light profile – Random orientation and PA of the disk

p.7 Semi-analytical model of galaxy formation (2)  Spectra – Stellar population absorption lines – Lyman-alpha lines from HII regions ionized by young stars  Voigt template (absorption + emission)  EW(z=0) 150 A  Normalized to get the correct count at z~3 ? – Other nebular emission lines from Charlot & Longhetti 2001  Input parameters: Z, effective ionization, dust-to- heavy elements -> emission line template – Dust attenuation

p.8 Input datacube  Disk HR image  Bulge HR image  For each image – LR Continnuum + absorption line images – Emission line tables (lambda, flux, sigma) – Lyman alfa profile (to be x by the continuum)

p.9 MUSE datacube creation (1)  Process each object  Convolve by appropriate spectral PSF – Function of x,y  Convolve by appropriate spatial PSF – No AO: MOFFAT seeing model f(lambda) – AO: MOFFAT AO model f(lambda, x, y)

p.10 MUSE datacube creation (2)  Add atmosphere – Continuum + OH emission lines f(moon) + random variation OH – Absorption f(airmass)  Convert in count – Throughput  Add noise – Photon, dark current, readout

p.11 Computing  SAM – Output: 1600 galaxies – CPU time ? – Disk size: 36 Mo  Data cube creation (1) – 80 exposures with different atmospheric conditions – CPU time: 80x8.5 = 28 days – Disk size: 80x1.3 = 104 Go  Data cube creation (2) – 80 exposures of 1 hour – CPU time: 80x1.5 hour = 5 days – Disk size: 80x2.6 Go = 208 Go  Analysis ?

p.12 Deep-Field Simulation

p.13 Spatially Resolved Galaxies Field 20 arcsec

p.14 Dense Stellar Field 1 arcmin 20 arcsec

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