System wide optimization for dark energy science: DESC-LSST collaborations Tony Tyson LSST Dark Energy Science Coll

1. System wide optimization for dark energy science: DESC-LSST collaborations Tony TysonLSST Dark Energy Science Collaboration meetingJune 12-13, 2012

2. Multiple LSST probes of dark energy • Use the same LSST survey data products • Analyzed for different signals • Multiple cross checks • Combination is far more powerful than root mean square • Maximally sensitive to new physics

3. DE Probes and DESC Tasks • Measure geometry with Baryon Acoustic Oscillations • Photo-z effects, Non-linear corrections • Measure mass and geometry with Weak Lensing Tomography • Measure and control systematics • Combine multiple LSST probes • Break degeneracies • Minimize sensitivity to systematics • Even better precision: DESC R&D • Photo-z and shear estimation algorithms, and their validation • System-wide systematics relevant to DE • Optics, Detectors, Wavefront sensor, Guider, Calibration, …

4. DETF – Science Book – Astro2010 – CD-1

5. DESC WHY NOW? Getting started: https://www.lsstcorp.org/sciencewiki/index.php?title=Getting_Started Register for the All Hands Meeting (Aug 13-17): https://www.lsstcorp.org/ahm2012/  

6. Optimize LSST Survey for Dark Energy LSST PROJECT LSST DARK ENERGY SCIENCE COLLABORATION Get close to the experiment: System-wide involvement Validate algorithms, develop new algorithms Ensure Simulator fidelity Test the Simulator and System Components Explore cadence scenarios and systematics

7. Modes of Interaction LSST PROJECT LSST DARK ENERGY SCIENCE COLLABORATION STUDENTS LSST PROJECT STUDENTS LSST PROJECT LSST PROJECT DESC students work with existing LSST R&D groups Help validate algorithms, develop new algorithms Help ensure Simulator fidelity Help test the Simulator and System Components Explore cadence scenarios and systematics

8. Tasks WEAK LENS SHEAR • PSF Systematics • PSF Control • Wavefront sensing • Stack-Fit and Multi-Fit • Galaxy shear • End-to-end simulations PHOTOMETRIC REDSHIFT • Algorithms, • Photometry, Calibration • Required precision • Systematics, Simulations BARYON ACOUSTIC OSCILLATIONS • Simulations WIDE AREA ISSUES • Dither, Patch effects SUPERNOVAE • Third parameter EXTRACTING DE SCEINCE • Joint WL+BAO • Cross-calibration • All probes MAPPING ONTO THEORY • Experiment constraints • Statistical inference DATA MANAGEMENT • Automated DQA: document LSE-63 • Computation LSST Hardware systematics • CCDs, Optics  CALIBRATION • Spec/Phot PRECURSOR AND TEST DATA • Subaru • f/1.2 LSST Camera Beam tests

9. Example near-term focus areas WAVEFRONT SENSING PHOTOMETRIC CALIBRATION IMSIM: LSS WL STACKFIT DEVELOPMENT AND TESTS DESC students work with existing LSST R&D groups Help validate algorithms, develop new algorithms Help ensure Simulator fidelity Help test the Simulator and System Components Explore cadence scenarios and systematics

10. Wavefront curvature sensing • F. Roddier, Applied Optics, 27, 1223, 1998 More intense Less intense Science Focal Plane

11. Photometric calibration: flat field Direct rays Ghost rays (about 3% of direct intensity)

12. IMAGE SIMULATIONS All Sky Database Milkyway Extended Sources Transients Defects Base Catalog DESC Solar System Cosmology Generate the seed catalog as required for simulation. Includes: Instance Catalog Generation Operation Simulation Metadata Size Position Color Brightness Proper motion Type Variability DM Data base load simulation Source Image Generation Introduce shear parameter from cosmology metadata Generate per FOV Atmosphere Photon Propagation Operation Simulation Telescope Camera Defects Generate per Sensor Formatting DM Pipelines Calibration Simulation LSST Sample Images and Catalogs

13. Shape measurements on galaxy and star images

14. Measuring faint galaxy shear: Stack-Fit • Measure the shape of galaxies whose apparent shape is distorted by the point-spread function (PSF) • PSF varies within CCDs and between CCDs and between exposures due to optics and atmosphere variations • The Stack-Fit Algorithm: 1. Measure PSF within each CCD for each exposure 2. Separately make weighted co-add of all dithered images of the field 3. Co-add with same weights the CCD PSF eigenfunctions 4. Use this PSF co-add map to interpolate the PSF at each galaxy’s position 5. Convolve this PSF with a galaxy model, and fit. • Test performance on end-to-end LSST image simulations • Test via comparing HST and Subaru WL mass reconstruction

15. Subaru-HST shear component comparison @ 40 source galaxies/arcmin2 Systematic offset test: post Stack-Fit galaxy-by-galaxy distribution of Subaru-HST shear components e1 Subaru-e1 HST e2 Subaru-e2 HST Binned by magnitude Will Dawson, UCD

16. Test Stack-Fit using full LSST image simulations LSST cosmic shear residual systematic errors Initial tests on LSST image simulations, including atmosphere and focal plane errors, suggest that residual systematic shear correlations may be reduced below the shot noise in ~100 images of a field. R&D on this and similar algorithms is planned, using the end-to-end LSST survey image simulations -- including realistic lensing power spectra and observational systematics. Jee & Tyson 2011

17. Testing CCD systematics: f/1.2 beam

18. DETF FoM(t) during ten year survey

19. Testing general models of dark energy Science Book Ch. 15.1