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Data simulations / Community challenges Stephan Birkmann / Jeff Valenti

Data simulations / Community challenges Stephan Birkmann / Jeff Valenti. Simulator uses. Create simulator. Performance Description. Release simulated data. Organize data challenge. Release simulator. Agree on simulation g uidelines. Science inputs

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Data simulations / Community challenges Stephan Birkmann / Jeff Valenti

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  1. Data simulations / Community challengesStephan Birkmann / Jeff Valenti

  2. Simulator uses Create simulator Performance Description Release simulated data Organize data challenge Release simulator

  3. Agree on simulation guidelines • Science inputs • e.g., simulated spectrum, parameter space • Operational assumptions (the “rules”) • e.g., reset, read, read • Instrumental effects to consider • e.g., photon noise, read noise, intra-pixel, … • Presentation of results • e.g., PPM vs. wavelength

  4. Performance description examples PPM vs. wavelength 6-hr clock time  1 hr R=700 bin  R=100

  5. How to agree on simulation guidelines? • (Very) small working group? • 1-2 participants per IDT • when?

  6. Types of data challenges • Types of simulated data • Raw images • Instrument artifacts • Reduced spectroscopy/photometry • Model constraints • Types of analyses • Recover inputs with knowledge of input • Recover inputs from blind analysis

  7. (Benefits of data challenges) • (Build community support) • We are beyond this point • (List key factors that affect experiment) • Data quality issues • Model parameters • Product of simulation step • (Build familiarity with ground system) • Standard data products • Observing templates • Product of simulation step

  8. Benefits of data challenges • Formulate observing strategy based on... • Ability to remove known instrumental effects • e.g., persistence, ghosts, jitter • Only useful until real data are released • Ability to recover input model parameters • e.g., composition, thermal structure • Useful throughout mission lifetime • Inform TAC • Allocate observing time wisely • Facilitate pipeline algorithm development

  9. Participation in data challenges • Advantages • Craft better proposals • Become a recognized expert • Advertise instrument capabilities • (Obtain funding) • Disadvantages • May help competing proposals • Requires significant effort

  10. Which must be done? Would you participate? Create simulator Performance Description Release simulated data Data challenge Release simulator

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