---
license: cc-by-sa-4.0
pretty_name: Weight Systems Defining Five-Dimensional IP Lattice Polytopes
configs:
  - config_name: non-reflexive
    data_files:
      - split: full
        path: non-reflexive/*.parquet
  - config_name: reflexive
    data_files:
      - split: full
        path: reflexive/*.parquet
size_categories:
  - 100B<n<1T
tags:
  - physics
  - math
---

# Weight Systems Defining Five-Dimensional IP Lattice Polytopes

This dataset contains all weight systems defining five-dimensional reflexive and
non-reflexive IP lattice polytopes, instrumental in the study of Calabi-Yau fourfolds in
mathematics and theoretical physics. The data was compiled by Harald Skarke and Friedrich
Schöller in [arXiv:1808.02422](https://arxiv.org/abs/1808.02422). More information is
available at the [Calabi-Yau data website](http://hep.itp.tuwien.ac.at/~kreuzer/CY/). The
dataset can be explored using the [search
frontend](http://rgc.itp.tuwien.ac.at/fourfolds/). See below for a short mathematical
exposition on the construction of polytopes.

Please cite the paper when referencing this dataset:

```
@article{Scholler:2018apc,
    author = {Schöller, Friedrich and Skarke, Harald},
    title = "{All Weight Systems for Calabi-Yau Fourfolds from Reflexive Polyhedra}",
    eprint = "1808.02422",
    archivePrefix = "arXiv",
    primaryClass = "hep-th",
    doi = "10.1007/s00220-019-03331-9",
    journal = "Commun. Math. Phys.",
    volume = "372",
    number = "2",
    pages = "657--678",
    year = "2019"
}
```

## Dataset Details

The dataset consists of two subsets: weight systems defining reflexive (and therefore IP)
polytopes and weight systems defining non-reflexive IP polytopes. Each subset is split
into 4000 files in Parquet format. Rows within each file are sorted lexicographically by
weights. There are 185,269,499,015 weight systems defining reflexive polytopes and
137,114,261,915 defining non-reflexive polytopes, making a total of 322,383,760,930 IP
weight systems.

Each row in the dataset represents a polytope and contains the six weights defining it,
along with the vertex count, facet count, and lattice point count. The reflexive dataset
also includes the Hodge numbers \\( h^{1,1} \\), \\( h^{1,2} \\), and \\( h^{1,3} \\) of
the corresponding Calabi-Yau manifold, and the lattice point count of the dual polytope.

For any Calabi-Yau fourfold, the Euler characteristic \\( \chi \\) and the Hodge number
\\( h^{2,2} \\) can be derived as follows:

$$ \chi = 48 + 6 (h^{1,1} − h^{1,2} + h^{1,3}) $$

$$ h^{2,2} = 44 + 4 h^{1,1} − 2 h^{1,2} + 4 h^{1,3} $$

This dataset is licensed under the
[CC BY-SA 4.0 license](http://creativecommons.org/licenses/by-sa/4.0/).

### Data Fields

- `weight0` to `weight5`: Weights of the weight system defining the polytope.
- `vertex_count`: Vertex count of the polytope.
- `facet_count`: Facet count of the polytope.
- `point_count`: Lattice point count of the polytope.
- `dual_point_count`: Lattice point count of the dual polytope (only for reflexive
  polytopes).
- `h11`: Hodge number \\( h^{1,1} \\) (only for reflexive polytopes).
- `h12`: Hodge number \\( h^{1,2} \\) (only for reflexive polytopes).
- `h13`: Hodge number \\( h^{1,3} \\) (only for reflexive polytopes).

## Usage

The dataset can be used without downloading it entirely, thanks to the streaming
capability of the `datasets` library. The following Python code snippet demonstrates how
to stream the dataset and print the first five rows:

```python
from datasets import load_dataset

dataset = load_dataset("calabi-yau-data/ws-5d", name="reflexive", split="full", streaming=True)

for row in dataset.take(5):
    print(row)
```

When cloning the Git repository with Git Large File Storage (LFS), data files are stored
both in the Git LFS storage directory and in the working tree. To avoid occupying double
the disk space, use a filesystem that supports copy-on-write, and run the following
commands to clone the repository:

```bash
# Initialize Git LFS
git lfs install

# Clone the repository without downloading LFS files immediately
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/calabi-yau-data/ws-5d

# Change to the repository directory
cd ws-5d

# Test deduplication (optional)
git lfs dedup --test

# Download the LFS files
git lfs fetch

# Create working tree files as clones of the files in the Git LFS storage directory using
# copy-on-write functionality
git lfs dedup
```

## Construction of Polytopes

This is an introduction to the mathematics involved in the construction of polytopes
relevant to this dataset. For more details and precise definitions, consult the paper
[arXiv:1808.02422](https://arxiv.org/abs/1808.02422) and references therein.

### Polytopes

A polytope is the convex hull of a finite set of points in \\(n\\)-dimensional Euclidean
space, \\(\mathbb{R}^n\\). This means it is the smallest convex shape that contains all
these points. The minimal collection of points that define a particular polytope are its
vertices. Familiar examples of polytopes include triangles and rectangles in two
dimensions, and cubes and octahedra in three dimensions.

A polytope is considered an *IP polytope* (interior point polytope) if the origin of
\\(\mathbb{R}^n\\) is in the interior of the polytope, not on its boundary or outside it.

For any IP polytope \\(\nabla\\), its dual polytope \\(\nabla^*\\) is defined as the set
of points \\(\mathbf{y}\\) satisfying

$$
  \mathbf{x} \cdot \mathbf{y}
  \ge -1 \quad \text{for all } \mathbf{x} \in \nabla \;.
$$

This relationship is symmetric: the dual of the dual of an IP polytope is the polytope
itself, i.e., \\( \nabla^{**} = \nabla \\).

### Weight Systems

Weight systems provide a means to describe simple polytopes known as *simplices*. A weight
system is a tuple of real numbers. The construction process is outlined as follows:

Consider an \\(n\\)-dimensional simplex in \\(\mathbb{R}^n\\), i.e., a polytope in
\\(\mathbb{R}^n\\) with vertex count \\(n + 1\\) and \\(n\\) of its edges extending in
linearly independent directions. It is possible to position \\(n\\) of its vertices at
arbitrary (linearly independent) locations through a linear transformation. The placement
of the remaining vertex is then determined. Its position is the defining property of the
simplex. To specify the position independently of the applied linear transformation, one
can use the following equation. If \\(\mathbf{v}_0, \mathbf{v}_1, \dots, \mathbf{v}_n\\)
are the vertices of the simplex, this relation fixes one vertex in terms of the other
\\(n\\):

$$ \sum_{i=0}^n q_i \mathbf{v}_i = 0 \;, $$

where \\(q_i\\) is the tuple of real numbers, the weight system.

It is important to note that scaling all weights in a weight system by a common factor
results in an equivalent weight system that defines the same simplex.

The condition that a simplex is an IP simplex is equivalent to the condition that all
weights in its weight system are bigger than zero.

For this dataset, the focus is on a specific construction of lattice polytopes described
in subsequent sections.

### Lattice Polytopes

A lattice polytope is a polytope with vertices at the points of a regular grid, or
lattice. Using linear transformations, any lattice polytope can be transformed so that its
vertices have integer coordinates, hence they are also referred to as integral
polytopes.

The dual of a lattice with points \\(L\\) is the lattice consisting of all points
\\(\mathbf{y}\\) that satisfy

$$
  \mathbf{x} \cdot \mathbf{y} \in \mathbb{Z} \quad \text{for all } \mathbf{x} \in L \;.
$$

*Reflexive polytopes* are a specific type of lattice polytope characterized by having a
dual that is also a lattice polytope, with vertices situated on the dual lattice. These
polytopes play a central role in the context of this dataset.

The weights of a lattice polytope are always rational. This characteristic enables the
rescaling of a weight system so that its weights become integers without any common
divisor. This rescaling has been performed in this dataset.

The construction of the lattice polytopes from this dataset works as follows: We start
with the simplex \\(\nabla\\), arising from a weight system as previously described. Then,
we define the polytope \\(\Delta\\) as the convex hull of the intersection of
\\(\nabla^*\\) with the points of the dual lattice. In the context of this dataset, the
polytope \\(\Delta\\) is referred to as ‘the polytope’. Correspondingly,
\\(\Delta^{\!*}\\) is referred to as ‘the dual polytope’. The lattice of \\(\nabla\\) and
\\(\Delta^{\!*}\\) is taken to be the coarsest lattice possible, such that \\(\nabla\\) is
a lattice polytope, i.e., the lattice generated by the vertices of \\(\nabla\\). This
construction is exemplified in the following sections.

A weight system is considered an IP weight system if the corresponding \\(\Delta\\) is an
IP polytope; that is, the origin is within its interior. Since only IP polytopes have
corresponding dual polytopes, this condition is essential for the polytope \\(\Delta\\) to
be classified as reflexive.

### Two Dimensions

In two dimensions, all IP weight systems define reflexive polytopes and every vertex of
\\(\nabla^*\\) lies on the dual lattice, making \\(\Delta\\) and \\(\nabla^*\\) identical.
There are exactly three IP weight systems that define two-dimensional polytopes
(polygons). Each polytope is reflexive and has three vertices and three facets (edges):

| weight system | number of points of \\(\nabla\\) | number of points of \\(\nabla^*\\) |
|--------------:|---------------------------------:|-----------------------------------:|
|     (1, 1, 1) |                                4 |                                 10 |
|     (1, 1, 2) |                                5 |                                  9 |
|     (1, 2, 3) |                                7 |                                  7 |

The polytopes and their duals are depicted below. Lattice points are indicated by dots.
<img src="pictures/ws-2d.png" style="display: block; margin-left: auto; margin-right: auto; width:520px;">

### General Dimension

In higher dimensions, the situation becomes more complex. Not all IP polytopes are
reflexive, and generally, \\(\Delta \neq \nabla^*\\).

This example shows the construction of the three-dimensional polytope \\(\Delta\\) with
weight system (2, 3, 4, 5) and its dual \\(\Delta^{\!*}\\). Lattice points lying on the
polytopes are indicated by dots. \\(\Delta\\) has 7 vertices and 13 lattice points,
\\(\Delta^{\!*}\\) also has 7 vertices, but 16 lattice points.
<img src="pictures/ws-3d-2-3-4-5.png" style="display: block; margin-left: auto; margin-right: auto; width:450px;">

The counts of reflexive single-weight-system polytopes by dimension \\(n\\) are:

| \\(n\\) | reflexive single-weight-system polytopes |
|--------:|-----------------------------------------:|
|       2 |                                        3 |
|       3 |                                       95 |
|       4 |                                  184,026 |
|       5 |           (this dataset) 185,269,499,015 |

One should note that distinct weight systems may well lead to the same polytope (we have
not checked how often this occurs). In particular it seems that polytopes with a small
number of lattice points are generated many times.