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Week 2: From telescope to speaker

Follow a measurement through calibration, reduction, representation and playback.

Week 02 · Provenance

2 August 2027

Which transformations happen before a telescope measurement reaches a listener?

Data object
calibrated detector time series
Lecture deckOpen the taught visual narrative

The question

A sonification never starts with “the universe”. It starts with an instrument, units, sampling choices and a reduction pipeline. We draw a provenance chain that distinguishes collected values from corrections, derived values and presentation choices.

Signal chain

Detector counts → remove instrument bias → attach timestamps and units → select a time window → rescale for hearing → render. Every arrow must be documented because each can change the claim a listener is able to make.

NASA’s Exoplanet Watch analysis guide is a useful example: raw telescope images become a light curve only after a named reduction process.

Observation guide 02

Provenance

Use this page before the slides, then return to the checkpoint after the lab.

  1. Trace detector values through calibration, reduction and playback.
  2. Keep observed quantities separate from derived quantities.
  3. Record units and assumptions at every transformation.
detector counts
Synthetic detector counts with a constant 100-count electronic bias. Values are a teaching model, not an astronomical observation.

Counts are not yet brightness

Synthetic detector counts with a constant 100-count electronic bias.

  1. Subtract the documented 100-count bias from every sample.
  2. Divide by the out-of-event median to obtain relative brightness.
  3. Retain timestamps and flag any rejected frame.
  4. Only then choose an audible range and playback interval.

What the method supportsBias subtraction changes the values but not their order. Normalisation changes the unit and makes the dip comparable. Neither operation creates audio.

Which transformation belongs to data reduction rather than sonification: subtracting detector bias or mapping brightness to pitch?Reveal the reasoning

Subtracting detector bias belongs to data reduction.

Bias correction estimates the measured signal before representation. Pitch mapping is a later communication choice. Mixing the two makes it impossible to tell whether a feature came from the instrument or the display.

How to analyse exoplanet observations by NASA Exoplanet Watch

Follow a documented route from FITS images through calibration to a light curve.

The data passport in the Field Guide turns this chain into a reusable project record.

Teaching team