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…rerun by eye Registers the science project `euclid_dr1` in the body map and files its first task, `sep1_delivery_reproduces_prelim_ten` (State: ready, awaiting submission). - projects.yaml: new active row after euclid_dr1_prelim (remote none until the human creates one, CPU-only, partition ral, assistant autolens_assistant, the full 17-verb sync_verbs list of the pipeline's hpc/sync). - tasks/euclid_dr1/sep1_delivery_reproduces_prelim_ten.md: the DR1 data delivery just arrived (Segmentation.zip, 3 nested batch zips, 15032 tiles, every tile already in the pipeline dataset layout), and the first question is whether it is good to use — the ten euclid_dr1_prelim lenses refitted from the sep1 delivery through prelim's own two-stage CPU route, judged BY EYE against prelim run 342629. Mask, positions and segmentation were all regenerated, so the numbers must differ; a numerical witness would be measuring the rerun scatter that ordered_rerun_catalogue_parity already found (median z ~ 15 on the same code and the same data). The three-clause witness is completion of both stages on 10/10, a per-tile side-by-side sheet showing the same lens configuration, and an Einstein-radius table naming any tile off by more than 10 %. The ten prelim -> sep1 tile pairs (3 exact, 7 recentred by 0.8-2.2 arcsec) and the extraction facts are in ## Notes. - dashboards regenerated. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01DGLY4Av4CAhiwZc6oiSDYk
…route SLURM array job 342650 (342650_[0-9], partition ral) is on RAL: the ten euclid_dr1_prelim tiles refitted from the sep1 delivery through hpc/batch_cpu/submit_initial_lens_model_two_stage, one array task per tile in the ## Notes list order. - sep1_delivery_reproduces_prelim_ten: ready → submitted, Runs: 342650. - dashboards regenerated. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01DGLY4Av4CAhiwZc6oiSDYk
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Submitted: SLURM array job 342650 ( |
This was referenced Sep 11, 2026
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Closing: the Generated by Claude Code |
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Registers the science project
euclid_dr1inprojects.yamland files its first task.projects.yamlis code, so this needs a human merge (the ledger auto-merge refuses it).The row — active,
local_path/mnt/c/Users/Jammy/Science/euclid_dr1,ral_root/mnt/ral/jnightin/euclid_dr1,sync_clihpc/syncwith the pipeline's full 17-verb list, ledgerwiki/project/state.md, assistantautolens_assistant, partitionral,remote: noneuntil the human creates one. Placed directly aftereuclid_dr1_prelim, which it mirrors.The task —
tasks/euclid_dr1/sep1_delivery_reproduces_prelim_ten.md, State: ready (Epic: euclid-dr1-prep, budget 36:00). The DR1 delivery has just arrived (Segmentation.zip, three nested batch zips, 15032 tiles, every tile already in the pipeline dataset layout) and the first question is whether it is good to use: refit the teneuclid_dr1_prelimlenses from the sep1 delivery through prelim's own two-stage CPU route and judge them by eye against prelim run 342629.The comparison is deliberately not numerical. Mask, positions and segmentation were all regenerated (tile 102005065: mask radius 5.65" vs 3.32", 4 positions vs 2), and
ordered_rerun_catalogue_parityalready measured what a numerical witness would pick up — run-to-run scatter with a median of z ≈ 15 on the same code and the same data. The three-clause witness is instead: both stages complete on 10/10 tiles; a per-tile side-by-side sheet (newvis_pixfit beside 342629's) showing the same lens configuration on 10/10; and an Einstein-radius table naming any tile off by more than 10 %.## Notescarries the ten prelim → sep1 tile pairs (3 exact matches, 7 recentred by 0.8–2.2 arcsec, in array-index order) and the extraction facts.python3 scripts/cortex.py checkis OK;pytest -q testsis 126 passed; dashboards regenerated in the same commit.The job id will be recorded on this branch once the run is submitted (
cortex.py move … submitted --run <id>), so please do not delete the branch before that lands.🤖 Generated with Claude Code
https://claude.ai/code/session_01DGLY4Av4CAhiwZc6oiSDYk