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Understanding Spurious Responses in HIFI Data: Detection and Cataloging Techniques

This document explores spurious responses in HIFI data, detailing our findings and identifying gaps in knowledge. We developed an automated algorithm for detecting spurs in cold-load data from various HIFI observations, including SOVT-2 AOT, Tsys surveys, and gas-cell data. The algorithm identifies spikes and narrow saturated regions in the spectrum by analyzing the second derivative of the data. This process will become part of the standard data pipeline, allowing for effective trend analysis and adjustments for spur positions during space operations.

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Understanding Spurious Responses in HIFI Data: Detection and Cataloging Techniques

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Presentation Transcript

  1. Spurious response in HIFI data What we know, what we don’t, and what to look for.

  2. Spur pathology in gas-cell observations

  3. Spur detection and cataloging • Based on work with the gas-cell data, we wrote an automated algorithm to search for spurs in cold-load data of all HIFI observations, which include: • SOVT-2 AOT observations • Tsys survey • Gas-cell observations. • Eventually, this will be part of the standard pipeline (level 0->0.5). • In a nutshell, the algorithm examines the second-derivative of the cold-load data and looks for spikes. It also looks for saturated yet narrow (< 40 MHz) regions of the spectrum. Secondly, it checks to see if the spur candidate is also seen in the other polarization.

  4. Spur detection and cataloging

  5. 551GHz water line concern

  6. To-do and closing thoughts In addition to having the routine in the standard pipeline, the output has to be used by the SPG to enable trend analysis. Spur positions will change when we get to space. Spurs can also be moved by hardware table changes in flight. HSPOT will provide a warning to the user when a known spur is near the LO requested.

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