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LSST/HEP Simulations

LSST/HEP Simulations. Science “End-to-End” (E2E) Simulation Calibration simulation Generalized geometry Cosmic / radioactive backgrounds New Sky Model A Blind Search. B. Popescu et al, U. Cincinnati D. Cinabro, Wayne State Leif Wilden, Lance Simms, Stanford

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LSST/HEP Simulations

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  1. LSST/HEP Simulations Science “End-to-End” (E2E) Simulation Calibration simulation Generalized geometry Cosmic / radioactive backgrounds New Sky Model A Blind Search • B. Popescu et al, U. Cincinnati • D. Cinabro, Wayne State • Leif Wilden, Lance Simms, Stanford • D. Kirkby, UC Irvine • J. Peterson, Purdue

  2. Track the history of each photon from the sky model Through the layers of a Kolmogorov atmosphere Into and through the optics of LSST To the conversion into an electron that diffuses in the depletion layer of a silicon panel Record charge detected in a single pixel. LSST End-to-End (E2E) Simulation A primary goal: to understand systematics of weak lensing shear measurements. J. Peterson (Purdue), Garret Jernigan (LBL), et al. These simulations have been used to: • Inform the design of LSST • Verify scientific performance, AND • Aid in the construction of algorithms to analyze the anticipated large data sets.

  3. Optically Induced Ellipticities Weak lensing measurements limited by proper understanding of these distortions Optics + Mirror Perturbations Optics Only Calibration: Can be used to estimate current distortions using multi-epoch observations of overlying star arrays. Atmosphere + Wind Atmosphere

  4. Calibration System Simulation • LSST/DE science relies on precise astrometry • To extract WL signal from current distortions • AND photometry • In particular to understand photo-z measurements • To understand how the calibration affects the science, need ability to adjust assumptions  Need simulation of calibration system Bogdan Popescu U. Cincinnati

  5. Calibration Simulation Team WP1 - Simulation Main Program - Bogdan Popescu, et al (U. of Cincinnati), and David Cinabro (Wayne StateUniversity) WP2 - Standards and Targets - Lynne Jones and Zeljko Ivezic (U. of Washington, Seattle) WP3 - Instrument and Hardware Calibration - Raul Armendariz, Jim Frank and John Haggerty (Brookhaven and Harvard University) WP4 - Auxiliary Instrumentation and Atmosphere - Jim Bartlett (APC Paris 7) and David Burke (SLAC) WP 5 – Pipelines - Tim Axelrod (LSSTC) and representatives from WP1-WP4 August 2007 Bogdan Popescu

  6. LSST Calibration Simulation LSST Calibration Simulation Main Program i (obsHistID, fieldID), RA and Dec (fieldRA,fieldDec), elevation and azimuth (computed from RA, Dec,etc), camera rotation (rotSkyPos, rotTelPos), filter, date and time (expDate, expMJD, expTime), cloud conditions(xparency), sky brightness (skyBright) LSST Operations Simulator LSSTFOV(i) Generate References & Standards (WP2) Standard SEDs RA and Dec (standard stars), SED's (mean stellar spectra) Simulate Precursor Campaign and Priors (WP2) Standard s Catalog Generate Test Targets (WP2) Target SEDs RA and Dec (targets), SED's (mean stellar spectra) Generate Instrument Response (WP3) Ir(x,y,n,i) ADUs (n,x,y,i) Simulate Calibration Pipeline (WP3) Im(x,y,n,i) Generate Instrument Calibration (WP3) Flats and Bias unknown (at this time) Generate Aux Telescope Ops (WP4) j , RA and Dec, elevation and azimuth(computed from RA, Dec), date and time AUXFOV(j) Simulate Aux Observing(WP4) Aux Object Catalog Zr(az,el,n,i/j) Atmospheric transmission(n) Generate Atmosphere(WP4) Compute Model Source Catalog ADUs, i,j, RA and Dec (azimuth, elevation), SEDs Simulate Image Processing Pipeline (WP5) Model Source Catalog Object Catalog and Zm(az,el,n,i/j) Simulation Analysis and Reporting

  7. html Centralized Geometry Specification D. Kirkby, UC Irvine .xml file + C++ code “POV” – Persistence of Vision http://www.povray.org ROOT Geant4 OSLO ZEMAX

  8. Cosmic Rays and Background Radiation Leif Wilden, Lance simms, Stanford • LSST uses thick Silicon CCD •  cosmic rays could cause significant background • Used Geant4 to study of effect on “transient phenomena”: • Built CCD geometry + dome and LSST structures • Realistic distribution of cosmic rays at ~9,000 ft. • Track cosmics (and other backgrounds) into silicon detector • Add background to charge response in E2E simulator.

  9. Cosmic Rays Alone LEFT: Real data taken with a CCD sensor @ NOAO (credits to Don Groom) RIGHT: Simulation LSST Simulation Workshop, Leif H. Wilden For 15 sec exposure (1 chip): muons camera@45˚ protons Average number of pixels effected: 180 304 11 Average number of “ tracks” seen: 15 12 1.4 Average number of pixels per “ track”: 12 26 8 But: Expect large fluctuations for individual exposures due to statistical nature of atmospheric air showers, solar cycle dependence, geomagnetic effects, ... muons protons

  10. Add Secondary Scattering & Radioactive Backgrounds Simulations Workshop 9/19/06, Lance Simms

  11. New Sky ModelBased on DES “CatSim1” D. Kirkby + Katie Richardson, UC Irvine This catalog samples ~500 sq.deg. z  1.4 with: Galaxies: • ~15 million “primary” galaxies obtained from N-body simulation • ~40 million random “dim” galaxies Stars: • ~0.5 million bright stars from USNO-B • ~4.2 million simulated faint stars

  12. Catalog Contents(HEP Style) D. Kirkby + Katie Richardson, UC Irvine • Each Galaxy described by: • RA, dec • Red shift z • SDSS magnitudes ugriz (no y) • 136 shapelet coefficients • Converted to ROOT file !

  13. Tailor to LSST Environment D. Kirkby + Katie Richardson, UC Irvine Provides sky source for Each LSST pointing. Evolve out to z = 3.0 • Generate SED using Blanton kcorrect algorithm to recover Y band • Problem for 1.5 < z < 3.0* • Generate photons using shapelet parameters *Katie is now using the Millenium Hubble-volume simulation (z up to 127)

  14. Blind Search for Super MassiveStar Clusters B. Popescu, M. Hanson, U. Cincinnati • Finding super massive star clusters is difficult and subjective using well-worn astronomical techniques • Internal Extinction • Dominance of Red Giants • Large backgrounds • Try to design a new search algorithm using a HEP-inspired strategy: • Simulate the “signal” • Superimpose it on the background • Try to find it • Estimate efficiency, systematics, etc. • Now look at data !

  15. Package Description Mass Geneva Database Mass Distribution Stellar Evolution Salpeter IMF rt , rc Spatial Distribution King Model Cluster Model (FITS file) AV , RV Extinction CCM Model Age August 2007 Bogdan Popescu

  16. RESULTS : Images - Westerlund 1 SIMULATION - May help identify search strategy for other super massive clusters in MW. IMAGE • Only 4-5 million yrs old. • Originally observed ~ 1961 as “open cluster with ~20 stars ! • Actually has over 2 x 105 stars ! – some over 40 Solar masses • Dust extinction makes it difficult to see how massive it is. August 2007 Bogdan Popescu

  17. Summary • Simulations are certainly not a new HEP innovation in cosmology, but probably are in other areas of astrophysics • The way they are used seems to be, however • Proprietary code vs. tools of general use • Use in estimation of systematic effects • Use in blind searches • EPP culture of collaborating on large systems.

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