boss_bench

Database overview

Database overview#

The BoSS holds 179,049 stellarator equilibria gathered from many sources. Every device goes through the same pipeline: an ideal MHD equilibrium is computed, followed by fast-ion confinement simulations of alpha particles.

179,049 configurations

2.7 billion alpha particles traced

134 million full particle orbits

3.2 TB of HDF5 files

Sources#

The field source_dataset records where each configuration came from. The configurations come from about 97,000 distinct source devices (source_id). A boundary can appear more than once, with different pressure and current profiles, and scaled to a different machine size and field strength.

Source (source_dataset)

Description

Configurations

quasr

Quasi-axisymmetric and quasi-helically symmetric stellarators from QUASR (Giuliani et al.).

70,764

constellaration

Quasi-isodynamic-like boundaries from the ConStellaration dataset (Cadena et al., 2025).

66,135

model_improvement_2026

Boundaries generated randomly using active learning. Generated for the BoSS paper.

13,922

random_ellipse

Unoptimized rotating ellipses with random elongation, aspect ratio and axis torsion. Generated for the BoSS.

7,932

model_improvement_bo_2026

Boundaries chosen with active learning using surrogate optimization.

7,635

random_ellipse_perturbed

Rotating ellipses with random perturbations in their higher Fourier modes. Generated for the BoSS.

6,872

fast_ion_opt_2026

Boundaries optimized for alpha-particle confinement using a surrogate model. Generated for the BoSS.

5,530

omnigenity

Omnigenous equilibria computed with DESC, from the omnigenity database of Gaur et al. (2023).

185

kappel_2024

Stellarator and tokamak configurations collected by Kappel et al. (2024).

64

Others

bindel_2023: configurations optimized for fast-ion confinement by Bindel et al. (2023). bindel_2023_perturbed, quasr_perturbed: perturbed copies of a few devices.

10

Distributions#

Histograms of the loss fraction of particles born on s = 0.25, the quasi-symmetry error, the aspect ratio, the mean rotational transform, the plasma beta of non-vacuum devices, and the number of field periods. Histograms of the loss fraction of particles born on s = 0.25, the quasi-symmetry error, the aspect ratio, the mean rotational transform, the plasma beta of non-vacuum devices, and the number of field periods.

The top row shows the loss fraction of the particles born on \(s = 0.25\), the quasi-symmetry error (the square root of the summed squares of the Boozer \(\lvert B \rvert\) harmonics that break quasi-symmetry, relative to the symmetric ones, averaged over the flux surfaces) and the aspect ratio. The bottom row shows the mean rotational transform, the plasma \(\beta\) of the non-vacuum devices, and the number of field periods.

Many devices deviate strongly from quasi-symmetry, although a large share come from QUASR, a database of devices optimized for quasi-symmetry. About a third of the devices have finite pressure, with \(\langle\beta\rangle\) up to about 6%; most of them are above the reactor-grade value of 1%.

Files#

The data comes in two kinds of HDF5 files: confinement files and trajectory files. They differ only in the type of guiding-center information they carry. Confinement files have particle initial and final states, whereas trajectory files contain full trajectories.

Confinement files

Trajectory files

Fast-ion data

Start and end state of all 15,000 particles

Full orbits of 6,000 particles

Configurations per file

up to 3,000

up to 40

Train split

52 files, 279 GB, 154,128 configurations

479 files, 2.46 TB, 19,123 configurations

Test split

9 files, 45 GB, 24,921 configurations

80 files, 411 GB, 3,184 configurations

The confinement files (325 GB in all) are enough to compute loss fractions and heat fluxes. The trajectory files (2.9 TB) hold 134 million orbits for 22,307 of the configurations. See Download Data to download the data.

Train/test split#

The file names indicate the split they belong to, train or test. The test split holds 24,921 configurations (14%), drawn at random across sources. All configurations that come from the same source device are in the same split, so a model is never tested on a rescaled copy of a device it was trained on.