diff --git a/dashboard.html b/dashboard.html
index 0d1ab40..57cb002 100644
--- a/dashboard.html
+++ b/dashboard.html
@@ -4,7 +4,7 @@
 <meta charset="utf-8">
 <meta name="viewport" content="width=device-width, initial-scale=1">
 <title>PyAutoCortex Dashboard</title>
-<!-- generated by `pyauto-brain cortex dashboard --apply` on 2026-09-13 — regenerate, do not hand-edit -->
+<!-- generated by `pyauto-brain cortex dashboard --apply` on 2026-09-14 — regenerate, do not hand-edit -->
 <style>:root{color-scheme:light dark;--bg:#fff;--fg:#1f2328;--muted:#59636e;
  --line:#d8dee4;--btn:#f6f8fa;--ok:#1a7f37;--warn:#9a6700;--bad:#d1242f;
  --accent:#a5177d;--tint:#a5177d14;--edge:#a5177d3d;
@@ -227,8 +227,8 @@
 </head>
 <body>
 <header class="hero"><span class="orb"><svg class="mark" viewBox="0 0 48 48" fill="none" stroke="currentColor" stroke-width="1.3" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true"><circle cx="24" cy="24" r="20.4"/><path d="M11.8,18.6 C14.4,14.8 17.8,14.4 20.1,17.1 C22.4,19.8 25.8,20.2 28.5,17.9 C31.2,15.6 34.4,16.2 36.1,19.1"/><path d="M11.6,24.6 C14.2,20.8 17.6,20.4 19.9,23.1 C22.2,25.8 25.6,26.2 28.3,23.9 C31.0,21.6 34.2,22.2 35.9,25.1"/><path d="M12.8,30.6 C15.3,27.2 18.4,26.8 20.5,29.4 C22.6,32.0 25.7,32.4 28.2,30.2"/><circle cx="32.6" cy="33.0" r="3.8"/><g fill="currentColor" stroke="none"><circle cx="32.6" cy="33.0" r="1.5"/><circle cx="11.7" cy="18.6" r="1.2"/><circle cx="11.5" cy="24.6" r="1.2"/></g></svg></span><h1>PyAuto<b>Cortex</b><span class="kind">Dashboard</span></h1><div class="rule"></div><p class="tag">Question. Run. Rule.</p></header><p class="lede">One ledger per science project: what is on the cluster right now, what the human said they would do next, and the last few things they wrote down. Nothing here is inferred from a result — the board hands you the check-in and the resume prompt; what a run meant is yours to log.</p>
-<ul class="stats"><li><b>1</b><span>Running</span></li><li><b>20</b><span>Open</span></li><li><b>7</b><span>Projects</span></li></ul>
-<div class="fresh"><p class="muted">Last updated 2026-09-13.</p></div>
+<ul class="stats"><li><b>1</b><span>Running</span></li><li><b>21</b><span>Open</span></li><li><b>8</b><span>Projects</span></li></ul>
+<div class="fresh"><p class="muted">Last updated 2026-09-14.</p></div>
 <p class="muted mdsrc"><a href="https://github.com/PyAutoLabs/PyAutoCortex/blob/main/dashboard.md">markdown version</a> · <a href="https://github.com/PyAutoLabs/PyAutoCortex/blob/main/README.md">GitHub Page</a></p>
 <div class="task"><button class="copy" data-cmd="/cortex — check in on every active science project since the last check-in (2026-09-13T10:34Z): pull each through its sync CLI, show me where each run stands, re-render and push the board, then read me the by-project summary. Record nothing about results — I will tell you what to log." aria-label="Copy the Claude command">📋</button><p>check in since last time</p></div>
 <div class="checkin" id="checkin-box"><span class="mark" id="checkin-mark"></span><div><span class="label">Last check-in</span><time id="checkin" datetime="2026-09-13T10:34Z">2026-09-13T10:34Z</time><span class="note" id="checkin-note"></span></div></div>
@@ -237,6 +237,7 @@
 <tr><th>Project</th><th>Running</th><th>Open</th><th>Last update</th></tr>
 <tr><td><b>subhalo_validation</b></td><td>0</td><td>5</td><td>2026-09-07</td></tr>
 <tr><td><b>euclid_dr1_prelim</b></td><td>1</td><td>3</td><td>2026-09-12</td></tr>
+<tr><td><b>euclid_sersics</b></td><td>0</td><td>1</td><td>2026-09-14</td></tr>
 <tr><td><b>analytic_gaussian</b></td><td>0</td><td>0</td><td>2026-09-10</td></tr>
 <tr><td><b>ep_toy_gaussian</b></td><td>0</td><td>2</td><td>2026-09-10</td></tr>
 <tr><td><b>slope_hierarchy_scale</b></td><td>0</td><td>4</td><td>2026-09-09</td></tr>
@@ -292,6 +293,26 @@ <h3>euclid_dr1_prelim — Refit the ten DR1-prelim tiles and rebuild the catalog
 <div class="task"><button class="copy" data-cmd="/cortex — resume euclid_dr1_prelim: read PyAutoCortex projects/euclid_dr1_prelim.md (Now, Runs, Log) and then /mnt/c/Users/Jammy/Science/euclid_dr1_prelim/wiki/project/state.md and the assistant autolens_assistant's AGENTS.md; tell me where I left off and what I said I would do next. Submit nothing and log nothing until I say." aria-label="Copy the Claude command">📋</button><p>resume euclid_dr1_prelim</p></div>
 </section>
 <section class="project">
+<h3>euclid_sersics — Why does the lens-light Sersic index pile up at the n=5 prior edge</h3>
+<p class="muted">active · ral · <a href="https://github.com/PyAutoLabs/euclid_strong_lens_modeling_pipeline/issues/74">euclid_strong_lens_modeling_pipeline#74</a> · <a href="https://github.com/PyAutoLabs/PyAutoCortex/blob/main/projects/euclid_sersics.md">projects/euclid_sersics.md</a> · local <span class="pathchip">/mnt/c/Users/Jammy/Science/euclid_sersics</span> · RAL <span class="pathchip">/mnt/ral/jnightin/euclid_sersics</span></p>
+<p><b>Now</b></p>
+<p>The four Sersic-stage variants are done on the real sample (342808: 99 of 100 lenses with all four tags; task 72 lost its later variants to the walltime) and the simulated sample's vis_lp stage is done too (342911: 100 of 100). 343043 is now running the same four variants on the 100 mocks — the recovery test, where each fit's n can be read against the truth.json it was simulated from (49 of the 100 were simulated at n = 3.0 under the 4.95 prior-edge rule).<br>Next: when 343043 lands, pull it with the 342911 results, scrape all four variant tags on both samples with catalogue/scripts/lens_sersic.py --unique_tag sersic_lens_model_&lt;variant&gt;, and put the recovered n against truth side by side with the real sample's n distributions. The human rules on whether any variant clears the n = 5 pile-up.</p>
+<p><b>Runs</b></p>
+<ul>
+<li>343043_[0-99] — open — ral — 2026-09-14 — euclid_sersics_sim_variants: the same four Sersic-stage variants — baseline, wide_n, central_noise, sersic_point — run sequentially inside each of 100 array ta…</li>
+</ul>
+<p><b>Last 5</b></p>
+<ul>
+<li>2026-09-14 — <i>run</i> — 343043_[0-99] submitted: euclid_sersics_sim_variants: the same four Sersic-stage variants — baseline, wide_n, central_noise, sersic_point — run sequentially inside each of 100 array tasks on the simulated sample dataset/dr1_sep1_sersics_si…</li>
+<li>2026-09-14 — <i>run</i> — 342911_[0-99] finished — wall 1:59: euclid_sersics_sim_vis_lp: the vis_lp stage (initial_lens_model.py --stage=vis_lp, MGE lens + MGE source, 15 free parameters, identical to the real sample's stage 1) on the new simulated sample dataset/d…</li>
+<li>2026-09-14 — <i>run</i> — 342808_[0-99] finished — wall 18:00: euclid_sersics_variants: the Sersic stage rerun in four variants — baseline, wide_n, central_noise, sersic_point — run sequentially inside each of 100 array tasks (one lens per task), array 0-99, 4 cpu…</li>
+<li>2026-09-13 — <i>note</i> — the simulated sample behind 342911: scripts/simulator.py --from-result rebuilds each of the 100 lenses from its own sersic_lens_model_baseline/vis result of run 342808 (Sersic lens light + Sersic source), keeping that tile's real PSF stamp…</li>
+<li>2026-09-13 — <i>run</i> — 342911_[0-99] submitted: euclid_sersics_sim_vis_lp: the vis_lp stage (initial_lens_model.py --stage=vis_lp, MGE lens + MGE source, 15 free parameters, identical to the real sample's stage 1) on the new simulated sample dataset/dr1_sep1_ser…</li>
+</ul>
+<p class="muted"><a href="https://github.com/PyAutoLabs/PyAutoCortex/blob/main/projects/euclid_sersics.md">full log</a></p>
+<div class="task"><button class="copy" data-cmd="/cortex — resume euclid_sersics: read PyAutoCortex projects/euclid_sersics.md (Now, Runs, Log) and then /mnt/c/Users/Jammy/Science/euclid_sersics/wiki/project/state.md and the assistant autolens_assistant's AGENTS.md; tell me where I left off and what I said I would do next. Submit nothing and log nothing until I say." aria-label="Copy the Claude command">📋</button><p>resume euclid_sersics</p></div>
+</section>
+<section class="project">
 <h3>analytic_gaussian — Graphical and EP against a closed-form Gaussian posterior, at ensemble scale</h3>
 <p class="muted">active · ral · <a href="https://github.com/PyAutoLabs/PyAutoCortex/issues/34">PyAutoCortex#34</a> · <a href="https://github.com/PyAutoLabs/PyAutoCortex/blob/main/projects/analytic_gaussian.md">projects/analytic_gaussian.md</a> · local <span class="pathchip">/mnt/c/Users/Jammy/Science/analytic_gaussian</span> · RAL <span class="pathchip">/mnt/ral/jnightin/analytic_gaussian</span></p>
 <p><b>Now</b></p>
diff --git a/dashboard.md b/dashboard.md
index 6aabf41..4243b0b 100644
--- a/dashboard.md
+++ b/dashboard.md
@@ -1,18 +1,18 @@
 # PyAutoCortex Dashboard
 
-<!-- generated by `pyauto-brain cortex dashboard --apply` on 2026-09-13 — regenerate, do not hand-edit -->
+<!-- generated by `pyauto-brain cortex dashboard --apply` on 2026-09-14 — regenerate, do not hand-edit -->
 
 This is the markdown version of the [PyAutoCortex Dashboard](https://pyautolabs.github.io/PyAutoCortex/), which puts the check-in and each project's resume prompt on your clipboard with a single tap of 📋.
 
 One ledger per science project: what is on the cluster right now, what the human said they would do next, and the last few things they wrote down. Nothing here is inferred from a result — the board hands you the check-in and the resume prompt; what a run meant is yours to log.
 
-> **Last updated 2026-09-13.**
+> **Last updated 2026-09-14.**
 
 | Where | Count |
 |-------|------:|
 | [Running](#projects) | 1 |
-| [Open](#projects) | 20 |
-| [Projects](#projects) | 7 |
+| [Open](#projects) | 21 |
+| [Projects](#projects) | 8 |
 
 <details><summary>📋 check in since last time</summary>
 
@@ -30,6 +30,7 @@ One ledger per science project: what is on the cluster right now, what the human
 |---|---:|---:|---|
 | subhalo_validation | 0 | 5 | 2026-09-07 |
 | euclid_dr1_prelim | 1 | 3 | 2026-09-12 |
+| euclid_sersics | 0 | 1 | 2026-09-14 |
 | analytic_gaussian | 0 | 0 | 2026-09-10 |
 | ep_toy_gaussian | 0 | 2 | 2026-09-10 |
 | slope_hierarchy_scale | 0 | 4 | 2026-09-09 |
@@ -107,6 +108,37 @@ Next: pull, bundle with output_sed, compare magnitudes (µJy fluxes) against the
 
 </details>
 
+### euclid_sersics — Why does the lens-light Sersic index pile up at the n=5 prior edge
+
+active · ral · [euclid_strong_lens_modeling_pipeline#74](https://github.com/PyAutoLabs/euclid_strong_lens_modeling_pipeline/issues/74) · [projects/euclid_sersics.md](https://github.com/PyAutoLabs/PyAutoCortex/blob/main/projects/euclid_sersics.md) · local `/mnt/c/Users/Jammy/Science/euclid_sersics` · RAL `/mnt/ral/jnightin/euclid_sersics`
+
+**Now**
+
+The four Sersic-stage variants are done on the real sample (342808: 99 of 100 lenses with all four tags; task 72 lost its later variants to the walltime) and the simulated sample's vis_lp stage is done too (342911: 100 of 100). 343043 is now running the same four variants on the 100 mocks — the recovery test, where each fit's n can be read against the truth.json it was simulated from (49 of the 100 were simulated at n = 3.0 under the 4.95 prior-edge rule).
+Next: when 343043 lands, pull it with the 342911 results, scrape all four variant tags on both samples with catalogue/scripts/lens_sersic.py --unique_tag sersic_lens_model_<variant>, and put the recovered n against truth side by side with the real sample's n distributions. The human rules on whether any variant clears the n = 5 pile-up.
+
+**Runs**
+
+- 343043_[0-99] — open — ral — 2026-09-14 — euclid_sersics_sim_variants: the same four Sersic-stage variants — baseline, wide_n, central_noise, sersic_point — run sequentially inside each of 100 array ta…
+
+**Last 5**
+
+- 2026-09-14 — *run* — 343043_[0-99] submitted: euclid_sersics_sim_variants: the same four Sersic-stage variants — baseline, wide_n, central_noise, sersic_point — run sequentially inside each of 100 array tasks on the simulated sample dataset/dr1_sep1_sersics_si…
+- 2026-09-14 — *run* — 342911_[0-99] finished — wall 1:59: euclid_sersics_sim_vis_lp: the vis_lp stage (initial_lens_model.py --stage=vis_lp, MGE lens + MGE source, 15 free parameters, identical to the real sample's stage 1) on the new simulated sample dataset/d…
+- 2026-09-14 — *run* — 342808_[0-99] finished — wall 18:00: euclid_sersics_variants: the Sersic stage rerun in four variants — baseline, wide_n, central_noise, sersic_point — run sequentially inside each of 100 array tasks (one lens per task), array 0-99, 4 cpu…
+- 2026-09-13 — *note* — the simulated sample behind 342911: scripts/simulator.py --from-result rebuilds each of the 100 lenses from its own sersic_lens_model_baseline/vis result of run 342808 (Sersic lens light + Sersic source), keeping that tile's real PSF stamp…
+- 2026-09-13 — *run* — 342911_[0-99] submitted: euclid_sersics_sim_vis_lp: the vis_lp stage (initial_lens_model.py --stage=vis_lp, MGE lens + MGE source, 15 free parameters, identical to the real sample's stage 1) on the new simulated sample dataset/dr1_sep1_ser…
+
+[full log](https://github.com/PyAutoLabs/PyAutoCortex/blob/main/projects/euclid_sersics.md)
+
+<details><summary>📋 resume euclid_sersics</summary>
+
+```
+/cortex — resume euclid_sersics: read PyAutoCortex projects/euclid_sersics.md (Now, Runs, Log) and then /mnt/c/Users/Jammy/Science/euclid_sersics/wiki/project/state.md and the assistant autolens_assistant's AGENTS.md; tell me where I left off and what I said I would do next. Submit nothing and log nothing until I say.
+```
+
+</details>
+
 ### analytic_gaussian — Graphical and EP against a closed-form Gaussian posterior, at ensemble scale
 
 active · ral · [PyAutoCortex#34](https://github.com/PyAutoLabs/PyAutoCortex/issues/34) · [projects/analytic_gaussian.md](https://github.com/PyAutoLabs/PyAutoCortex/blob/main/projects/analytic_gaussian.md) · local `/mnt/c/Users/Jammy/Science/analytic_gaussian` · RAL `/mnt/ral/jnightin/analytic_gaussian`
diff --git a/projects.yaml b/projects.yaml
index d81b859..f5d7da3 100644
--- a/projects.yaml
+++ b/projects.yaml
@@ -101,6 +101,20 @@ euclid_dr1_prelim:
   status: active
   note: "created by euclid-dr1-prep phase 4 (a Cortex task after migration); every path is the planned one; active 2026-09-07: phase 4 launched (jobs 342301_[0-9])"
 
+euclid_sersics:
+  remote: none
+  local_path: /mnt/c/Users/Jammy/Science/euclid_sersics
+  ral_root: /mnt/ral/jnightin/euclid_sersics
+  mirror: none
+  sync_cli: hpc/sync
+  sync_verbs: [push, pull, logs, sync, push-data-init, pull-full, status, submit, push-submit, jobs, sacct, cancel, wait-and-pull, tail, du, check, clear-logs]
+  ledger: wiki/project/state.md
+  assistant: autolens_assistant
+  witness_file: catalogue/**/*.csv
+  partition: ral
+  status: active
+  note: "born 2026-09-11: why the lens-light Sersic index piles up at the prior edge n = 5 in the June Euclid catalogue (61 % of 2983 fits above 4.5; 67 of the chosen 100); site copy of PyAutoLabs/euclid_strong_lens_modeling_pipeline, sample dataset/dr1_sep1_sersics (100 unanimous-success lenses on the sep1 Segmentation.zip data, join table sample/lens_map.csv); remote none until the human creates one"
+
 analytic_gaussian:
   remote: none
   local_path: /mnt/c/Users/Jammy/Science/analytic_gaussian
diff --git a/projects/euclid_sersics.md b/projects/euclid_sersics.md
new file mode 100644
index 0000000..e155c4e
--- /dev/null
+++ b/projects/euclid_sersics.md
@@ -0,0 +1,26 @@
+# euclid_sersics — Why does the lens-light Sersic index pile up at the n=5 prior edge
+
+Project: euclid_sersics
+Issue: euclid_strong_lens_modeling_pipeline#74
+
+## Now
+
+The four Sersic-stage variants are done on the real sample (342808: 99 of 100 lenses with all four tags; task 72 lost its later variants to the walltime) and the simulated sample's vis_lp stage is done too (342911: 100 of 100). 343043 is now running the same four variants on the 100 mocks — the recovery test, where each fit's n can be read against the truth.json it was simulated from (49 of the 100 were simulated at n = 3.0 under the 4.95 prior-edge rule).
+Next: when 343043 lands, pull it with the 342911 results, scrape all four variant tags on both samples with catalogue/scripts/lens_sersic.py --unique_tag sersic_lens_model_<variant>, and put the recovered n against truth side by side with the real sample's n distributions. The human rules on whether any variant clears the n = 5 pile-up.
+
+## Runs
+
+- 343043_[0-99] — open — ral — 2026-09-14 — euclid_sersics_sim_variants: the same four Sersic-stage variants — baseline, wide_n, central_noise, sersic_point — run sequentially inside each of 100 array tasks on the simulated sample dataset/dr1_sep1_sersics_sim, array 0-99, 4 cpu / 14 GB / 18 h on partition ral, submitted 2026-09-14 ~16:50 BST; each variant restores 342911's vis_lp result (task 0 confirmed 'Fit Already Completed' at the expected hash) and fits only the Sersic stage, so the recovered n can be read against each mock's truth.json — the recovery test the sim sample was built for; submitter hpc/batch_cpu/submit_sersic_variants_sim
+
+## Log
+
+- 2026-09-14 — run — 343043_[0-99] submitted: euclid_sersics_sim_variants: the same four Sersic-stage variants — baseline, wide_n, central_noise, sersic_point — run sequentially inside each of 100 array tasks on the simulated sample dataset/dr1_sep1_sersics_sim, array 0-99, 4 cpu / 14 GB / 18 h on partition ral, submitted 2026-09-14 ~16:50 BST; each variant restores 342911's vis_lp result (task 0 confirmed 'Fit Already Completed' at the expected hash) and fits only the Sersic stage, so the recovered n can be read against each mock's truth.json — the recovery test the sim sample was built for; submitter hpc/batch_cpu/submit_sersic_variants_sim
+- 2026-09-14 — run — 342911_[0-99] finished — wall 1:59: euclid_sersics_sim_vis_lp: the vis_lp stage (initial_lens_model.py --stage=vis_lp, MGE lens + MGE source, 15 free parameters, identical to the real sample's stage 1) on the new simulated sample dataset/dr1_sep1_sersics_sim, array 0-99, one lens per task, 8 cpu / 64 GB / 36 h on partition ral, submitted ~14:56 BST; the sample is the 100 real lenses resimulated from their own 342808 sersic_lens_model_baseline/vis results, so the recovered n can be read against a known truth — 100 of 100 tasks COMPLETED, max wall 1:59:33; every mock has its vis_lp result at output/dr1_sep1_sersics_sim/<Tile>/initial_lens_model/vis_lp/<hash>.zip on the cluster (not yet pulled to the local clone).
+- 2026-09-14 — run — 342808_[0-99] finished — wall 18:00: euclid_sersics_variants: the Sersic stage rerun in four variants — baseline, wide_n, central_noise, sersic_point — run sequentially inside each of 100 array tasks (one lens per task), array 0-99, 4 cpu / 14 GB / 18 h on partition ral, submitted ~12:10 BST; output tags sersic_lens_model_{baseline,wide_n,central_noise,sersic_point}; all four reuse 342696's vis_lp result; pipeline code euclid_strong_lens_modeling_pipeline#75 (open, CI green) — 99 of 100 tasks COMPLETED, per-variant walls about 10 to 45 minutes; task 72 (Tile102027146RA0860828972205DECNEG0426060761921, the 2.5 h baseline lens) was RESTARTED by SLURM and then hit the 18 h walltime, so that one lens has a partial variant set. Outputs are pulled to the local clone and all four variant tags are on disk for the other 99 lenses.
+- 2026-09-13 — note — the simulated sample behind 342911: scripts/simulator.py --from-result rebuilds each of the 100 lenses from its own sersic_lens_model_baseline/vis result of run 342808 (Sersic lens light + Sersic source), keeping that tile's real PSF stamps, noise and bands, a flat SED and seed = index + 1, into dataset/dr1_sep1_sersics_sim with the per-tile truth at <Tile>/truth.json and the manifest at sample/sim_manifest.csv. Decision taken while the human was out, so it can be re-cut from truth.json: the prior-edge rule --sersic-index-prior-edge=4.95 --sersic-index-replacement=3.0, i.e. a baseline n within 1 % of the n = 5 bound is simulated at n = 3.0 instead — 49 of the 100 were replaced; the edge census of the inferred n is max 4.999966, 10 at >= 4.999, 35 at >= 4.99, 49 at >= 4.95 and 56 at >= 4.9. Two defects were fixed on the way, on pipeline branch feature/simulator-from-result-linear (tip 0f4aed9, issue euclid_strong_lens_modeling_pipeline#77, PR parked on Heart RED, merged into the science clone): --from-result rebuilt a dark tracer from linear light profiles and now reads files/tracer.json, and PyAutoArray's simulator draws Poisson noise unconditionally so FFT round-off raised on 4 tiles, clipped pipeline-side with the library bug filed as a Mind draft prompt.
+- 2026-09-13 — run — 342911_[0-99] submitted: euclid_sersics_sim_vis_lp: the vis_lp stage (initial_lens_model.py --stage=vis_lp, MGE lens + MGE source, 15 free parameters, identical to the real sample's stage 1) on the new simulated sample dataset/dr1_sep1_sersics_sim, array 0-99, one lens per task, 8 cpu / 64 GB / 36 h on partition ral, submitted ~14:56 BST; the sample is the 100 real lenses resimulated from their own 342808 sersic_lens_model_baseline/vis results, so the recovered n can be read against a known truth
+- 2026-09-13 — run — 342808_[0-99] submitted: euclid_sersics_variants: the Sersic stage rerun in four variants — baseline, wide_n, central_noise, sersic_point — run sequentially inside each of 100 array tasks (one lens per task), array 0-99, 4 cpu / 14 GB / 18 h on partition ral, submitted ~12:10 BST; output tags sersic_lens_model_{baseline,wide_n,central_noise,sersic_point}; all four reuse 342696's vis_lp result; pipeline code euclid_strong_lens_modeling_pipeline#75 (open, CI green)
+- 2026-09-12 — run — 342696_[0-99] finished — wall 20:58: euclid_sersics_two_stage: the core two-stage fit (vis_lp then vis_pix) of the 100-lens dr1_sep1_sersics sample, array 0-99, one lens per task, 8 cpu / 64 GB / 36 h on partition ral, submitted 11:43 BST; it is the run the four Sersic-stage variants reuse rather than re-derive — 100/100 tasks completed both stages (vis_lp and vis_pix); task walls 2:47 to 20:58; outputs pulled to the local clone on 2026-09-12
+- 2026-09-11 — run — 342696_[0-99] submitted: euclid_sersics_two_stage: the core two-stage fit (vis_lp then vis_pix) of the 100-lens dr1_sep1_sersics sample, array 0-99, one lens per task, 8 cpu / 64 GB / 36 h on partition ral, submitted 11:43 BST; it is the run the four Sersic-stage variants reuse rather than re-derive
+- 2026-09-11 — note — project born ~11:45 BST once the human confirmed the euclid_dr1 sep1 results good: 100 unanimous-success lenses from the euclid inspection tables, remodelled on the sep1 Segmentation.zip data, to explain why the lens-light Sersic index piles up at the prior edge n = 5 (June catalogue: 61 % of 2983 fits above 4.5; 67 of the chosen 100 above 4.5). Local clone /mnt/c/Users/Jammy/Science/euclid_sersics (a site copy of PyAutoLabs/euclid_strong_lens_modeling_pipeline, its own journal at wiki/project/state.md), RAL root /mnt/ral/jnightin/euclid_sersics, sync CLI hpc/sync, sample dataset/dr1_sep1_sersics (100 tiles) with the June-to-sep1 join in sample/lens_map.csv. The plan is the core two-stage run first, then the Sersic stage rerun in four variants — baseline (the June recipe on the new data), wide_n (lens n prior Uniform(0.5, 10)), central_noise (multiplicative Gaussian noise inflation A = 9, sigma = 0.17 arcsec at the vis_lp lens-light centre) and sersic_point (Sersic + 5-Gaussian point MGE, free centre, +4 params) — with the human ruling on the per-variant n distributions afterwards; nothing is pre-registered as a verdict.
+- 2026-09-11 — note — ledger opened