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

2. 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 Primary LSST probes Weak Lens shear cross correlation tomography Weak Lens magnification cross correlation tomography 2-D Baryon Acoustic Oscillations Supernovae Shear peak statistics Galaxy cluster counts Secondary LSST probes Time domain tomography of QSOs and AGNs Anisotropy of WL+BAO and SN signals New Energy or New Gravity?

3. 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. 4 DETF – Science Book – Astro 2010 – CD-1 4

5. 5 DESC 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. 6 Optimize LSST Survey for Dark Energy LSST PROJECT 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. 7 Modes of Interaction LSST PROJECT LSST DARK ENERGY SCIENCE COLLABORATION STUDENTS 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 LSST PROJECT STUDENTS

8. 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. 9 Example near-term focus areas WAVEFRONT SENSING 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 PHOTOMETRIC CALIBRATION IMSIM: LSS WL STACKFIT DEVELOPMENT AND TESTS

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

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

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

13. 13 Shape measurements on galaxy and star images

14. 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. 15 Subaru-HST shear component comparison @ 40 source galaxies/arcmin 2 e 1 Subaru -e 1 HST e 2 Subaru -e 2 HST Binned by magnitude Systematic offset test: post Stack-Fit galaxy-by-galaxy distribution of Subaru-HST shear components Will Dawson, UCD

16. 16 Test Stack-Fit using full LSST image simulations LSST cosmic shear residual systematic errors Jee & Tyson 2011 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.

17. 17 Testing CCD systematics: f/1.2 beam

18. 18 DETF FoM(t) during ten year survey

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