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Data simulations / Community challenges Stephan Birkmann / Jeff Valenti

This document outlines the significance of community challenges using the Jeff Valenti simulator, emphasizing the effective organization of simulated data. It will discuss the establishment of simulation guidelines, operational assumptions, and the instrumental effects to consider during analysis. Participants will learn about various types of data challenges, the recovery of input parameters, and the benefits of engaging in these challenges. Insights into the importance of data quality and familiarization with ground systems will also be addressed to enhance the overall mission strategy.

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Data simulations / Community challenges Stephan Birkmann / Jeff Valenti

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Presentation Transcript

  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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