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Paper and outreach plan

1. Collaboration

  • Primary contact: Prof. Dr. Cornelis P. Dullemond (ITA, Heidelberg), thesis supervisor and author of the method’s original formulation (notes section 1.3, coag_py MC prototype). Co-authorship is natural: the method idea is his, the systematic study and implementation are ours.
  • Possible further co-authors / contacts in or near the group: M. Beutel (RPMC, comparison), authors of Schöll et al. 2026 (porosity, fractal dimension). Only via Dullemond.
  • Timing, in two steps: (1) a short note once the kernel views are live on https://coag.mader.xyz (after M4b): the thesis work continues, here is the URL, a paper is planned. (2) The full pitch after M7: the bias finding (F1) with one figure, the corrected estimator matching the full kernel, the plan for p = 2, and the proposal to write a paper together. Ask for his view on scope (porosity recipe, RPMC comparison, journal), on open points of the bouncing recipe (M3.4), and on whether coag_py may be cited as code or redistributed.
  • Form: German email (email-writing skill). Drafted by Claude, approved and sent by Vincent. Register (“Sie” or “du”) per Vincent’s past correspondence with him.

2. Paper structure (draft)

  1. Introduction: multi-property dust evolution and its cost; grid vs particle methods; contribution.
  2. The coagulation-fragmentation equation on a grid: recipes, detailed balance, kernels used (coag, shattering, cratering, bouncing).
  3. Monte Carlo estimation of the collision sum: estimators (thesis, HT, IS, hybrid), bias of unweighted sampling, optimal proposal, mass conservation per sample.
  4. Time integration with sampled kernels: implicit form, Radau IIA and W-methods, Jacobian strategies, noise at steady state.
  5. Results for p = 1: bias/variance, proposals, noise floor, work-precision, break-even.
  6. Results for p = 2 (mass, filling factor): cost surface, accuracy, physical results.
  7. Comparison with other approaches (RPMC, low-rank/TT, DG).
  8. Discussion and outlook (radial transport, GPUs, more properties). Appendices: kernel construction details, Radau implementation notes, test suite.

3. Reproducibility package

  • Tagged release of the repository with the exact commit used for the paper.
  • experiments/ configs and scripts for every figure; one command per figure (just figure <id>).
  • Output data archived with a DOI (Zenodo), including manifests.
  • Code licence decided at release (Q1).

4. Writing tooling

LaTeX with the journal class (A&A provides aa.cls), since co-authors and the journal expect it; figures from Python (matplotlib) via the PyO3 bridge (ADR 0013). Typst only for internal notes.

5. Side outputs

  • A short technical note or blog post on the web tool (optional, after the paper).
  • A talk at the group meeting (ask Dullemond).