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Recovering Phase Curves with Random Snapshot Observations: Challenges and Preliminary Results

This study explores the feasibility of reconstructing phase curves using random snapshot observations when long-term stability isn't achievable or full phases can't be observed. We present preliminary results from our work, including raw and corrected data findings. The challenges we face involve gain variations, undersampling issues, and residual non-linearity. We make assumptions about the stability of the phase curve during observations and discuss techniques for time series data reduction with IRAC, focusing on noise identification and removal.

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Recovering Phase Curves with Random Snapshot Observations: Challenges and Preliminary Results

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  1. Snapshot Phase Curves Krick, Ingalls, Carey, Grillmair, IRAC Team

  2. Snapcurves – P80016 • Suppose you don’t have stability on long (day) timescales, but you think you have a well characterized instrument. • Or you don’t want to observe an entire phase • Or can’t observe an entire phase. Is it possible to recover a phase curve with random snapshot observations?

  3. Preliminary Results - raw

  4. Preliminary Results - corrected

  5. Preliminary Results – fit

  6. Challenges • Gain as a function of position & undersampling • Pmap data which is not the observation itself • Residual non-linearity • Is randomly observing possible? • Assumes no changes in phase curve between phases

  7. Time Series Data Reduction With IRAC:Identifying and Removing Sources of Correlated Noise Boston AAS splinter session Sunday June 1, 2014 1:00 – 5:30pm • Short talks about warm data reduction • Data challenge Please contact Sean Carey, Carl Grillmair, Jim Ingalls or Jessica Krick at the SSC for details

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