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Per-entry paper-skill in wave500_coronal_source_mapping_pfss_045 (HelioSI 501-corpus). See body and metadata.yaml for paper identity and claim boundary.
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Based on SOC occupation classification
| name | macneice-2024-fisk-heliospheric-field-source-mapping |
| description | Per-entry paper-skill in wave500_coronal_source_mapping_pfss_045 (HelioSI 501-corpus). See body and metadata.yaml for paper identity and claim boundary. |
| paper | {"authors_verified":false} |
Runtime-neutral paper-skill. Layered: (1) scientific invariants, (2) executable protocol against abstract capabilities, (3) adapter notes (optional examples only), (4) research-generation affordances.
Reach for this skill when source-mapping L1 disturbances with a Fisk-style heliospheric field rather than a pure Parker spiral on top of PFSS.
Combining Fisk-field connectivity with PFSS source-surface topology yields source-region identifications for L1 disturbances that are statistically more consistent with EUV coronal-hole boundaries than Parker-only mapping.
In scope. The paper's window + Fisk-field parameterization.
Out of scope — do NOT generalize:
| Capability | Purpose | Notes |
|---|---|---|
magnetogram.fetch_synoptic_br() | Br | |
pfss.solve() | PFSS | |
fisk.heliospheric_field() | Fisk-field topology | parameter knob |
backmap.fisk_or_parker() | back-mapping under both | |
ch.detect_from_euv() | CH boundary | |
omni.fetch_disturbance_list() | L1 disturbances |
Recover Fisk-vs-Parker comparison statistic.
Use when the user asks about HelioSI, the heliophysics paper-skill corpus, the 501-skill corpus, paper-to-skill compilation, generating hypotheses or experiments from heliophysics literature, selecting corpus entries by topic (PFSS, open flux, PSP switchbacks, SEPs, turbulence, solar-wind classification, instruments, MHD, agent-runtime design), or deciding which papers to verify against full text.
Use when running Deep-SWIM, a semi-supervised CNN pipeline that classifies 5-minute Wind/MFI magnetic-field windows (3s-resampled, B_x/B_y/B_z stacked as channels) as discontinuity vs ambient solar wind. Central paper claim: a four-layer 1D-CNN trained with contrastive learning + pseudo-labeling + Online Hard Example Mining (OHEM) reaches AUC ≈ 0.90 on the held-out validation set and precision 0.52 / recall 0.73 / AUC 0.82 on a one-day expert-hand-labelled test set (2018-11-18), under heavy class imbalance (~15 % discontinuity-positive windows). NeurIPS 2021 Machine Learning and the Physical Sciences workshop (Lamdouar et al. 2022, arXiv:2203.01184). Note: the slug retains a legacy first-author placeholder; the verified lead author is Hala Lamdouar (University of Oxford), not Hu.
Use when detecting and cataloguing quasi-Periodic Density Structures (PDSs) in Solar Orbiter density data with Multitaper + wavelet, and quantifying their radial-length-scale evolution between 0.3 and 1 au — central claim is slow-wind PDSs expand at ~10% and fast-wind PDSs compress at ~10% across that range (Katsavrias et al. 2025, arXiv:2511.15518; venue TODO verify).
Use when running EMBER (Electron heating from Modulated Burst-mode Event Recognition) on PSP FIELDS Digital Burst Memory (DBM) voltage waveforms to detect modulated ion-acoustic waves (mIAWs — including triggered IAWs and frequency-dispersed IAWs) and correlate them with anomalous core-electron heating. Central paper claim: an ensemble of 16 background-only anomaly detectors (physics-motivated + classical outlier + deep-learning), run on log-scaled Fourier spectrograms of DBM bursts from PSP Encounters 6–9 and 15, recovers ≈ 93 % of labelled mIAW events at an estimated 1 % FAR on a curated catalog of 42 anomalous + 496 background spectrograms; flagged intervals show core perpendicular T_e above adiabatic cooling and elevated T_e/T_i ratios, reproducing previously reported heating phenomenology without using temperatures in the detection step. Sasli et al. 2026, arXiv:2605.00162, submitted to Earth and Space Science.
Use when the agent is positioning the consuming agent/manuscript against ai-scientist-system literature, borrowing a agent-runtime-async-tournament design pattern, or auditing a benchmark/manuscript claim related to it - central claim is "A multi-agent system on Gemini 2.0 implementing a generate-debate-evolve loop with asynchronous task execution and tournament-style hypothesis evolution improve" (arXiv preprint 2025).
Use when the agent is positioning the consuming agent/manuscript against domain-specific-ai-scientist literature, borrowing a agent-runtime-full-pipeline-cloud design pattern, or auditing a benchmark/manuscript claim related to it - central claim is "An AI Co-Scientist framework automates the full search-ranking research pipeline (idea -> code -> GPU training -> analysis) with single-LLM agents for routine t" (arXiv preprint 2026).
