The four-image set worked out on Centaurus A is now generated for any
target, because its value is the comparison: the same data at four levels
of treatment, so a viewer can see what processing did and did not add.
colour.py's assembly is driven by flags - gradient, neutralise, denoise,
saturation, hdr, protect-compact - so the baseline and the fully
corrected version come from ONE code path and the only differences
between them are the ones named.
Image 3 applies what the measurements justify rather than a house style.
Each item is there because measuring the first session caught the
conventional version getting something wrong: a plane fit that had
absorbed 17.9 ADU/px of galaxy halo, a core flattened by a white point
set by field stars, deconvolution ringing around every bright star, and
denoising erasing faint compact sources that turned out to be globular
clusters. Compact sources are now explicitly protected from smoothing -
2374 of them on NGC 2030.
The close-up revealed a real design error, caught by its own assertion.
Forcing a square crop cannot contain a target wider than the frame is
tall, which is the normal case for a nebula in a wide field, and the
assertion fired rather than silently cutting the subject in half. Crops
are no longer square, and when a target genuinely fills the field the
close-up is skipped with that said plainly - re-saving image 3 under a
name claiming to be a close-up would be worse than producing nothing.
science.py adds the measurements that generalise to any target:
photometric calibration from the field's own Gaia stars, the limiting
magnitude actually reached, a source catalogue with calibrated
magnitudes, an annotated field placed by the plate solution, and a radial
surface-brightness profile. Object-specific analyses stay hand-driven,
because a cluster survey suits a galaxy and is meaningless for a nebula.
All of it depends on astrometry, so an unsolved session gets no science
and says so instead of quietly producing less. NGC 2030 calibrates to a
zero point of 24.794 with 0.202 mag scatter on 917 stars, 3470 sources,
limiting G of 18.2.
colour.py assembles a viewable image from whatever a session actually
has: LRGB, RGB with a synthetic luminance, SHO in the Hubble palette,
HOO, or a single filter as greyscale. Nothing is assumed about which
filters exist, which is the whole point - three of the four archived
sessions have no luminance channel.
The background fit takes the lesson from Centaurus A, where a plane
fitted around a large galaxy absorbed 17.9 ADU/px of its halo. The
excluded region is now sized from the data: the ellipse grows until it
contains most of the flux above sky, so it suits a galaxy filling the
frame and a small nebula equally, and only a plane is ever fitted, never
a flexible surface. One mask is derived from the deepest channel and
applied to all of them, so the fit cannot shift the colour balance.
Broadband and narrowband need opposite stretches, which cost a round to
discover. Broadband channels share the luminance's midtone, preserving
the real brightness ratios that keep star colours honest. Narrowband
cannot: the lines differ enormously in strength, so a shared stretch
renders the entire nebula in whichever colour Ha was mapped to. Each
narrowband channel is now stretched to its own sky target instead.
Green suppression is applied to broadband only. On a narrowband palette
it would fight the palette, green being a deliberate channel assignment
rather than an artefact.
Measured rather than eyeballed, because the SHO render looked wrong by
eye and was not: both palettes put the sky at 0.098 against a 0.10
target and neutral to within 0.004. What differs is the data. NGC 2030's
brightest pixels reach 0.80 to 0.87, the Tarantula's only 0.23 to 0.33,
because that session is a single 60 second frame per filter - three
minutes in total. A correct pipeline renders nearly empty data as a
nearly empty frame.
