With the signal correction pipeline delivering clean, calibrated data, it’s time to tackle the next challenge: extracting meaningful spectral signals from the AIRS-CH0 frames. The goal is to transform bulky 3D arrays into a focused 1D time series that capture the wavelength signals over time for each star.

Checkout the AIRS-CH0 signal extraction notebook on GitHub

1. The challenge

After signal correction, each planet’s AIRS-CH0 data consists of thousands of frames, each containing a 32×282 pixel spectrogram. But here’s the key insight: not all detector pixels contain useful signal. The spectral data is concentrated in just a few rows where the dispersed starlight creates a distinct spectral trace.

The question becomes: how do we automatically identify and extract just the signal-bearing rows from each frame?

2. Intelligent signal extraction

The solution involves analyzing the signal strength across detector rows to identify the “spectral strip” - the handful of rows containing the actual dispersed spectrum:

Signal strength by detector row

The plot reveals the signal structure clearly: rows 14-17 contain the strongest signals. Outside of that narrow spatial band, the signal drops off quickly. This makes sense - the telescope’s grism disperses starlight into a narrow horizontal band across the detector.

Rather than hardcoding row numbers (which might vary between planets), the extraction algorithm uses an adaptive threshold approach:

Signal strength by detector row

  1. Analyze signal strength: Sum pixel values across each row in the first frame
  2. Apply inclusion threshold: Select rows with signal above a configurable threshold (typically 75-95% of peak signal)
  3. Extract and sum: Pull out the selected rows from all frames and sum them within each frame to create a 1D spectrum for each time point
  4. Optional smoothing: Apply moving average filtering to each wavelength index across the frames to reduce noise

3. Extracted data

The results are impressive: the extracted signal strip shows exoplanet transits just as clearly as using the total frame flux, but with dramatically reduced data volume and a subjective reduction in outliers. Summing the brightest rows also reduces noise in the spectrum derived from each frame.

Signal strength by detector row

4. Performance impact

The signal extraction provides substantial benefits:

  • Data reduction: From 9024 pixels per frame down to just 282 wavelength values (97% reduction)
  • Noise reduction: Focusing on high-signal rows improves signal-to-noise ratio
  • Processing speed: Smaller datasets mean faster downstream analysis