Old-vs-new mask comparison on real data (tiles + exposures)

[cailmdaley]

The gate for retiring ShapePipe's internal masking (#847 keeps the old path config-selectable until this lands). Part of epic #845.

Goal. Run both mask paths on the same real data — the Nibi P3 run (#808: 10 tiles, ~1960 single-CCD exposures) is the natural testbed — and understand, visually and quantitatively, what each mask piece does and where it comes from, before the old path is removed.

Deliverables:

• Side-by-side visual overlays of old (WeightWatcher/GSC star halos+spikes, NGC/Messier regions) vs new (healsparse-rasterized maskforce) masks on real image pixels, for several tiles and exposure CCDs. The question each panel answers: do the masks land on the artifacts, and which system catches what?
• Component-provenance audit. A table of every mask ingredient and where it enters each path: saturation / bleeding / bad columns (instrument flag files from CFHT preprocessing — summed in at the exposure level via USE_EXT_FLAG), cosmic rays (Stephen's masking), bright-star halos + spikes (old: GSC-2.3 + WeightWatcher; new: PhotoPipe bright-star masks), extended galaxies (old: NGC/Messier region files; new: 2MASS manual masks), MaxiMask. Per Axel (Slack 2026-07-16), the exposure level is where pixel masks matter — the audit should make explicit what the exposure-level flag image contains under each path.
• Quantitative comparison: masked-area fractions, IMAFLAGS_ISO population changes, PSF star-selection differences (counts + size–magnitude locus), and PSF model quality on the affected CCDs.

Non-goal: deciding halo/spike sizes — that validation is the tangential-shear test (separate sub-issue), though the two share infrastructure.

— Fable, on behalf of Cail

[cailmdaley]

Old vs new masks on real data — 10 tiles + 9 exposure CCDs from the P3 run (#808).

All panels: CFIS r-band pixels (zscale + asinh stretch); percentages = masked fraction per component. These figures compare only what gets masked in ShapePipe: the NEW panels show the external healsparse masks as #847 rasterizes them into the pipeline — in current production those are applied post-production, at the catalogue level.

• Tile figures (OLD | NEW). OLD = ShapePipe's internal tile pipeline_flag from this run: GSC-2.3 star halos + diffraction spikes rasterized by WeightWatcher (Messier/NGC masking enabled but empty on these tiles; no instrument content at tile level). NEW = the external healsparse reason maps (nside 131072 ≈ 1.6″) queried at each pixel's sky position: faint star halo (bit 1), bright star halo (2), bright stars (4), manual galaxy mask (8), MaxiMask (1024).
• Exposure figures (INSTRUMENT | OLD | NEW). One full 2112×4644 MegaCam CCD each. INSTRUMENT = the CFHT/Elixir flag file (bad pixels, saturation, bleed trails); in production it is summed into both old and new pipeline_flag, so it is shown separately — the OLD/NEW panels isolate what each masking system itself adds. OLD caveat: bit 2 is ambiguous (halo=2 collides with instrument saturated=2 under summing).

Tiles

Exposure CCDs

— Fable, on behalf of Cail

[aguinot]

If I may make a few comments:

• Using MaxiMask on the coadds is hard to justify. MaxiMask was not trained on coadds but on single exposures.
• More generally, I don't see a justification to use MaxiMask at all. It was trained on old and outdated CFHT images and the outcome is redundant in some case, or just bad.
• We don't need pixel level masks on the coadds.
• The bright stars/objects can be constructed at the single exposure level and combined to a full sky mask at posteriori.

A mask like this is the best way to inject systematics in the data