--- license: gpl-3.0 task_categories: - other pretty_name: toric — FRST + vex Calabi–Yau phases from the Kreuzer–Skarke list tags: - physics - string-theory - calabi-yau - mathematics - toric-geometry - kreuzer-skarke - triangulations size_categories: - 100M h^{1,1}(V)\\). Their \\(\kappa\\)/\\(c_2\\) are stored over the **unfolded** divisor list and therefore span **all** of \\(H^{1,1}(X)\\), in the *same* format as the favorable case — favorable is the degenerate case where every `n_components` is 1. **One exception.** The split assumes a reducible divisor is \\(g+1\\) pairwise-disjoint smooth rational surfaces, which forces \\(\chi(\mathcal{O}_D) = \kappa(D,D,D)/6 + (c_2\!\cdot\!D)/12 = g+1\\). It held in every FRST phase measured (1,351 sampled across \\(h^{1,1}=5\ldots10\\)) and fails for **17,461 of 34,079** non-favorable **vex** phases, which keep the ambient truncation instead; the other **16,618** are unfolded. **FRST is unfolded throughout.** *Why* it fails is an **open question** — \\(\chi(\mathcal{O}_D)\\) alone cannot distinguish components meeting along curves, fewer than \\(g+1\\) components in a non-star model, or components with \\(\chi \neq 1\\). **Determine completeness per phase from the geometry**, not from `oob_dim`: `len(c2) == sum(n_components)` means unfolded, `len(c2) == len(n_components)` means ambient. `oob_dim` and the favourability flags are stored **once per polytope** and are shared by both modes, so they are neither per-phase nor per-mode. `CYPhase.covers_full_h11` does exactly this check. - `wall_hash` **semantics**: comparable across `frst`/`vex` when the bases agree; it omits torsion and is not a full diffeomorphism invariant. **For a non-favorable polytope the two modes are not comparable**: FRST hashes the rank-\\(h^{1,1}\\) unfolded Wall data, while a vex phase left ambient hashes the rank-`basis_dim` data, so they can never collide. Full identification (GL\\((h^{1,1},\mathbb Z)\\) acting jointly on \\((\kappa,c_2)\\)) is the deferred *small-ICY* step. ## Quick start The consumer class `ToricCYDatabase` (`query(mode, …)`, `query_polytopes(…)`, O(1) `load(mode, …, in_basis=)`, shared `get_polytope(…)`) and the per-phase object `CYPhase` ship with `stringforge`; they read the **sharded** layout (below) directly: ```python from stringforge import CYPhase, ToricCYDatabase db = ToricCYDatabase.from_local("…/cy-database") # local build; the dir with toric/, or toric/ pcat = db.query_polytopes(h11=4) # shared: n_frst_classes AND n_vex_classes cp = db.query("frst", h11=4) # per-FRST-phase (thin) catalog geom = db.load("frst", h11=4, ks_id=0, triang_id=0, in_basis=True) # O(1) via the _ksid_index phase = CYPhase.from_database(db, mode="frst", h11=4, ks_id=1, triang_id=0) # -> ToricCYPhase kappa = phase.intersection_numbers(in_basis=True) # stored; no CYTools import ``` Note that `ks_id` is unique only **within** one `h11`, so all four of `(mode, h11, ks_id, triang_id)` are needed to name a phase. Access is currently **local only**; lazy download of the sharded layout from the Hub is not yet implemented. To read the Parquet directly, use a per-h11 dataset over the sharded parts (they are large — always filter, and at h11≥10 never load a whole catalog): ```python import pyarrow.dataset as pds ds = pds.dataset("…/toric/frst/catalog/h11_4") # data-*.parquet + _metadata df = ds.to_table(filter=pds.field("fav_N") == True).to_pandas() ``` ## Sub-dataset layout (sharded per h11) ``` toric/ README.md schema.json manifest.json provenance.json polytope_catalog/h11_{N}/data-*.parquet ← ONE row/polytope (shared): meta + FRST counts polytope/h11_{N}/data-*.parquet ← ONE copy: vertices, cy_basis, glsm_charge_matrix, polytope_hash polytope_vex_counts/h11_{N}/data-*.parquet← ks_id, n_vex, n_vex_classes (h11=2..7; joined on read) frst/catalog/h11_{N}/data-*.parquet ← thin per-FRST-phase rows frst/geom/h11_{N}/data-*.parquet ← heights, intnums_coo_{i,j,k,v}, c2, c2_origin vex/catalog/h11_{N}/ … vex/geom/h11_{N}/ … (h11 = 2..7) ``` Each split dir also has **`_ksid_index.parquet`** (`ks_id → (part, row0, n)` for O(1) point lookups) and a pyarrow **`_metadata`** (for efficient dataset scans). Parts are immutable (streaming build); h11=12 has thousands of parts per split. Each part is written with **~25k-row row groups**, so a single record is fetched by reading only its row group — locally, or via HTTP **range reads** remotely (e.g. `pyarrow.parquet.ParquetFile(url, filesystem=HfFileSystem())`) — **not** the whole part/file. ### Schemas - **`polytope_catalog/`** (shared, per polytope): `h11, ks_id, h12, polytope_hash, fav_N, fav_M, trilayer, n_rigids, n_rigids_dual`, **FRST counts** `n_frsts, n_ntfe_frsts:Int64` (nullable; present for \\(h^{1,1}\ge 10\\)), `n_frst_classes`, `oob_dim` (the **unfolded** divisor count, \\(h^{1,1}+4\\)), `basis_dim` (< \\(h^{1,1}\\) iff non-favorable). - **`polytope_vex_counts/`** (per vex polytope): `ks_id, n_vex, n_vex_classes`. `query_polytopes` left-joins this onto `polytope_catalog` by `ks_id` (`null` where a polytope has no vex). - **`{mode}/catalog/`** (**thin**, per phase): `h11, ks_id, triang_id, h12, fav_N, fav_M, trilayer, wall_hash:binary(32)` (raw sha256 digest — `.hex()` for the string form), `geom_shard_id, geom_row_index`. `polytope_hash` is **not** stored (join via `ks_id`); `phase_id` is **derived** (`"{mode}:{h11}:{ks_id}:{triang_id}"`). - **`{mode}/geom/`** (per phase): `h11, ks_id, triang_id, heights:list` (**verbatim**; vex may be non-integer), `intnums_coo_{i,j,k}:list` + `intnums_coo_v:list` (0-indexed positions into the unfolded divisor list), `c2:list` (length `oob_dim`; shorter — the ambient length — for a vex phase left ambient), `c2_origin:Int64` (dropped origin \\(c_2\\); `null` for vex). - **`polytope/`** (shared): `h11, ks_id, polytope_hash, vertices`, `cy_basis` (0-indexed positions into the unfolded divisor list; length \\(h^{1,1}\\)), `n_components` (components per prime toric divisor; all 1 iff favorable), `glsm_charge_matrix`. Bucketed by \\(h^{1,1}\\) (phase count explodes — frst h11=12 ≈ 2.4 B phases); zstd compression. **Scale caveat**: at h11≥10 use point lookups (`ks_id`) or filtered scans; an unfiltered whole-h11 query returns billions of rows. ## Provenance & reproducibility `provenance.json` records the exact CYTools version + install path + git SHA (if available), Python, numpy, pandas, pyarrow versions, and `experimental_features: true` (**vex** `vector_config` and non-favorable CY construction use CYTools experimental features). `ks_id` is the Kreuzer–Skarke emission order, map-verified (`ks_id_collection`). Every phase is **self-verifying**: rebuild the triangulation from the stored `heights` via CYTools (FRST → `CalabiYau` via `.cy()`; vex → a toric `Fan` via `p.vc().triangulate(heights)`) and re-derive \\(\kappa\\)/\\(c_2\\)/`wall_hash`. Cite the frozen HuggingFace revision used. ## Citation FRST classes: [arXiv:2310.06820](https://arxiv.org/abs/2310.06820); NTFE FRSTs: [arXiv:2309.10855](https://arxiv.org/abs/2309.10855); vex triangulations: [arXiv:2512.14817](https://arxiv.org/abs/2512.14817); CYTools: [arXiv:2211.03823](https://arxiv.org/abs/2211.03823); Kreuzer–Skarke: [hep-th/0002240](https://arxiv.org/abs/hep-th/0002240).