anonym-submit-26/bess-bench-26

BESS-Bench: Long-baseline, mixed-quality Be-star spectra for ML time-series research

339 115 spectral rows · 37 624 physical observations · 1 468 Be stars · 35 years · 91.6 % of observations from amateur observers A benchmark for representation learning and temporal modelling on heterogeneous stellar spectroscopic time series.

Three granularity levels (see §Dataset composition): parquet rows (atom of the file), physical observations (one observing session ≡ one (star_name, mjd) group; an échelle observation spans a median of 26 orders), and unique stars (split unit). The amateur share is $91.6,%$ at the observation level and $81.7,%$ at the row level (échelle orders multiply professional row counts).

TL;DR

BESS-Bench is an ML-ready, per-star split release of the BeSS community Be-star spectral archive, together with:

• Full-range optical spectra (1 150 – 10 442 Å native coverage). The fraction of spectra that strictly cover each commonly studied Be-star line at ±50 Å is reported at two granularities — per physical observation (Hα 77.6 %, Hβ 35.7 %, He I 5876 36.0 %, Na D 36.0 %, Hγ 32.5 %, Fe II 5169 35.6 %, Hδ 19.4 %, O I 7772 4.0 %) and per parquet row (row-level coverage used inside tasks: Hα 7.9 %, Hβ 0.8 %, …). Both tables are reproduced in §Line coverage below.
• A pre-trained Hα-window spectral encoder (803 K parameters, $R^2 = 0.93$ on held-out stars).
• Three benchmarked downstream tasks (SpecProbe feature regression, LineTransfer Hβ → Hα generalisation, EWForecast short-horizon EW(Hα) forecasting), with fixed protocols and bootstrap / multi-seed confidence intervals. The three tasks focus on Hα and Hβ because these are the only two lines with row-level coverage large enough to support meaningful CI95 bootstraps in v1.0.
• A transparent comparison on EWForecast: a bundled causal temporal transformer fails to beat persistence (ratio_mae 1.166 over four seeds, all seeds > 1.0), while a simple PCA+Ridge temporal baseline does (ratio_mae 0.942, bootstrap CI95 [0.927, 0.957]), matching modern zero-shot time-series foundation models.

Full methodology, datasheet and reproducibility instructions are in the accompanying paper and in DATASHEET.md. Result-level JSON artefacts (per-task summaries, bootstrap CIs, coverage audits) live in the companion code repository; this card quotes their content for self-containedness.

Quick start

from datasets import load_dataset

# Full dataset (9.5 GB, 21 shards)
ds = load_dataset("anonym-submit-26/bess-bench-26", split="train")

# Or a 500 MB inspection sample
ds = load_dataset("anonym-submit-26/bess-bench-26", split="train", streaming=True)

# Apply the official per-star split
import pandas as pd
splits = pd.read_csv("splits.csv")          # bundled in this repo
train_stars = set(splits.query("split == 'train'").star_name)
train = ds.filter(lambda x: x["star_name"] in train_stars, num_proc=8)

Reproduction of every paper table from frozen checkpoints is documented in REPRODUCE.md of the companion code repository.

Dataset composition

Three granularity levels

BESS-Bench is distributed as a parquet table, and we distinguish three nested units of measurement:

Unit	Count	Definition
Parquet rows	339 115	One row = one 1D spectrum or one échelle order
Physical observations	37 624	One observing session ≡ one (star_name, mjd)
Unique stars (split unit)	1 468	Per-star split, no leakage

The ~9× inflation from physical observations to parquet rows comes from 9 917 échelle sessions each stored as 19–40 individual orders (median 26 orders / session) — an intentional choice that preserves the native resolution (R ≈ 10 000 – 17 000). The 27 707 remaining observations are single-range spectra (one row = one observation).

Stream	Rows	Physical obs.	Unique stars	Median R
Single-range	62 476	27 707	1 354	9 000
Échelle orders	276 639	9 917	915	11 000
Total	339 115	37 624	1 468	—

Summary metrics

Metric	Value
Parquet rows	339 115
Physical observations	37 624
Unique stars	1 468
Temporal coverage	1990-03 → 2025-12 (35 years)
Wavelength coverage	1 150 – 10 442 Å
Median SNR	189
Amateur fraction (rows)	81.7 %
Amateur fraction (obs.)	91.6 %
Spectrographs / telescopes	26 / 62
Licence	CC-BY-4.0

Filter échelle orders with the is_echelle_order flag. v1.0 downstream tasks group by (star_name, mjd, instrument_setup) so that an échelle observation contributes once, not 26 times.

Wavelength frame convention

The wavelength column is shipped as in the BeSS FITS (no shift applied), so users can reproduce any rest frame they wish. For ≈ 97 % of spectra the released grid is therefore topocentric. To reach the heliocentric frame, apply

λ_helio = λ_obs · (1 − bss_rqvh / c)         (c = 299 792.458 km/s)

helio_velocity (= FITS BSS_VHEL) is the correction already applied by the observer (0 for 97 % of spectra) and bss_rqvh (= FITS BSS_RQVH) is the residual correction still to apply (populated for 99.8 % of spectra, ±20 km s⁻¹, σ ≈ 10 km s⁻¹).

The bundled BESS-Bench baselines (encoder pre-training, SpecProbe/LineTransfer/EWForecast) apply this correction internally before the 128-bin Hα/Hβ crop so that features and embeddings live in a common heliocentric frame compatible with external surveys (SDSS, LAMOST, APOGEE).

Line coverage — two complementary tables

BESS-Bench ships the native wavelength coverage of each observation (not a cropped Hα window). Two coverage tables are computed on the v1.0 snapshot (audit script and full JSON in the companion code repository).

Table A — coverage per physical observation (astrophysical view: of the 37 624 observing sessions, which contain this line at ±50 Å? Échelle rows are aggregated via λ_min=min, λ_max=max over their orders.)

Line	λ₀ (Å)	Coverage	Observations covered (out of 37 624)
Hα	6562.8	77.63 %	29 207
Na D	5893.0	35.98 %	13 536
He I 5876	5876.0	35.97 %	13 532
Hβ	4861.3	35.66 %	13 416
Fe II 5169	5169.0	35.55 %	13 376
Hγ	4340.5	32.54 %	12 242
Hδ	4101.7	19.45 %	7 317
O I 7772	7772.0	4.05 %	1 523

Table B — coverage per parquet row (per-row coverage, used when tasks iterate over individual parquet rows rather than over observing sessions)

Line	λ₀ (Å)	Coverage	Rows covered (out of 339 115)
Hα	6562.8	7.94 %	26 937
Na D	5893.0	3.37 %	11 413
He I 5876	5876.0	1.94 %	6 570
Hβ	4861.3	0.78 %	2 652
Fe II 5169	5169.0	0.71 %	2 421
Hγ	4340.5	0.69 %	2 337
Hδ	4101.7	0.66 %	2 246
O I 7772	7772.0	0.41 %	1 387

The 10× gap between the two tables is explained entirely by the échelle decomposition: each échelle row covers a narrow sub-range by construction, so a single observing session that covers Hα contributes 26 rows in Table B but only 1 observation in Table A. Wavelength distribution across rows: λ_min p5/p50/p95 = 3 980 / 5 277 / 7 006 Å; λ_max p5/p50/p95 = 4 113 / 5 413 / 7 351 Å.

v1.0 baselined tasks (SpecProbe, LineTransfer, EWForecast) use the Hα and Hβ windows because they combine high observation-level coverage (77.6 % / 35.7 %) with enough row-level density (26 937 / 2 652 spectra) for stable CVs. He I, Na D and Fe II 5169 each reach ~36 % observation-level coverage and are released for community experimentation, but no v1.0 leaderboard task targets them.

Splits

Split by star (not by spectrum) to prevent temporal leakage:

Split	Stars	Spectra	Fraction
train	1 024	241 057	≈71 %
validation	219	44 383	≈13 %
test	225	53 675	≈16 %

Assignment is deterministic via md5("bess_bench_v1::" + star_name)[:8] mod 100 ↦ {0–69: train, 70–84: val, 85–99: test}. The result is bundled as splits.csv; the generating script is shipped in the companion code repository for full reproducibility.

Schema

Column	Type	Description
wavelength	list[float32]	Native wavelength grid (Å)
flux	list[float32]	Calibrated flux
flux_error	list[float32]?	Flux uncertainty (null for a substantial fraction of amateur FITS; see DATASHEET §3)
spectrum_id	string	MD5 (star_name, mjd, instrument_setup)
star_name	string	Canonical de-duplicated name (1 468 unique)
star_name_raw	string	Original BeSS object name
ra, dec	float64	J2000 (degrees)
lambda_min, lambda_max	float64	Spectral coverage (Å)
n_pixels	int32	Number of spectral pixels
spectral_resolution	float64	Theoretical R
spectral_resolution_measured	float64?	Measured R (null when unavailable)
snr	float64	DER_SNR
snr_continuum	float64	Continuum-window SNR
snr_quality	string	excellent/good/medium/low/very_low/unknown
observation_date	string	ISO-8601 date
mjd	float64	Modified Julian Date
exposure_time	float64	Seconds
temporal_quality	string	good / borderline / unknown
spectrograph	string	Normalised
telescope	string	Normalised
detector	string	Normalised
instrument_setup	string	Canonical (spectrograph, telescope) pair
instrument_confidence	string	high / medium / low
observer_type	string	amateur / professional / unknown
site_latitude	float64?	Degrees, rounded to 0.1° (≈11 km)
site_longitude	float64?	Degrees, rounded to 0.1° (≈11 km)
site_elevation	float64?	Metres, rounded to 100 m
helio_velocity	float64?	Heliocentric correction already applied by the observer (= FITS BSS_VHEL, km s⁻¹). 0 for ≈97 % of spectra.
bss_rqvh	float64?	Residual heliocentric correction to apply to reach the heliocentric frame (= FITS BSS_RQVH, km s⁻¹). ±20 km s⁻¹, 1σ≈10 km s⁻¹, populated for 99.8 % of spectra.
telluric_corrected	string	Raw FITS header string (free-form, often None)
normalized	string	Raw FITS header string (free-form, often None)
is_echelle_order	bool	True when the row is one order of an echelle
fits_format	string	Original FITS flavour

Benchmark protocol

BESS-Bench v1.0 releases three quantitative tasks (SpecProbe, LineTransfer, EWForecast). Every task reports reproducible mean ± std and/or bootstrap CI95 over seeds {42, 123, 456} or 10 000 bootstrap resamples, under the rule that a learned-model claim of improvement requires the corresponding CI to exclude the baseline value.

SpecProbe — Hα single-line spectral-feature regression (Ridge probe, 3-seed)

Frozen 128-D embeddings from the released Hα-window encoder → Ridge regression on Hα spectral features: EW, FWHM, central depth, Δv, vr_ratio, peak intensity. 5-fold GroupKFold on star_id over 26 858 scored Hα spectra (Δv / vr_ratio defined on the 10 804 cleanly double-peaked profiles), repeated across encoder seeds {42, 123, 456}. Baselines: Ridge on PCA(10) and PCA(50) of the raw 128-D normalised Hα flux. Reported numbers (mean ± std over 3 seeds; full per-fold artefacts in the companion code repository):

feature	z (128D, encoder)	PCA(10)	PCA(50)
ew	0.9951 ± 0.001	0.9998 ± 0.000	1.0000 ± 0.000
fwhm	0.5704 ± 0.036	0.1329 ± 0.000	0.1730 ± 0.000
central_depth	0.9250 ± 0.006	0.1062 ± 0.000	0.1627 ± 0.000
delta_v	0.5679 ± 0.020	0.2479 ± 0.000	0.3077 ± 0.000
vr_ratio	0.4010 ± 0.030	0.2838 ± 0.000	0.2868 ± 0.000
peak_intensity	0.9960 ± 0.001	0.9640 ± 0.000	0.9664 ± 0.000

The encoder adds clear value on FWHM (kinematics), central depth (opacity), Δv and V/R ratio (kinematics); EW and peak_intensity are already linearly decodable from the raw flux so the encoder's marginal value is expected to be small.

LineTransfer — Cross-line probing (Hβ → Hα features)

Same Hα labels as SpecProbe, input is the Hβ ±50 Å window (128 bins, locally normalised), restricted to the 2 525 (star, MJD) pairs where both lines are observed. Demonstrates whether a second Balmer line carries predictive signal for Hα features. Best result (full per-feature breakdown in the companion code repository):

Target	Best Hβ representation	R² ± CV std
ew (Hα)	Hβ PCA(10)	0.692 ± 0.029
other features	all representations	≈ 0 (no transferrable signal)

EWForecast — Short-horizon EW(Hα) forecasting (bootstrap CI95)

One-step-ahead forecast of EW(Hα) at the next observation epoch given past sequences. Primary metric: ratio_mae = MAE(model) / MAE(persistence), computed on the v1.0 temporal test set (cutoff MJD = 59215). A published claim of beating persistence requires the CI95 to exclude 1.0 from above (for learned temporal transformers: all four seeds' ratios must be < 1.0; for deterministic baselines: bootstrap CI95 on per-prediction residuals, n = 10 000, must exclude 1.0). Full bootstrap artefacts in the companion code repository.

Method	point ratio	CI95 / per-seed spread	beats persistence?
Transformer Δz+Δt, 4 seeds	1.166	[1.095, 1.279] (spread of 4 seeds)	no (all seeds > 1.0)
PCA(10)+Ridge, ctx=5	0.942	bootstrap CI95 [0.927, 0.957]	yes
PCA(10)+Ridge, ctx=10	0.956	bootstrap CI95 [0.937, 0.976]	yes
Chronos-Bolt Base (zero-shot)	0.968	bootstrap CI95 [0.955, 0.980]	yes
TimesFM-2.0-500M (zero-shot)	0.969	bootstrap CI95 [0.949, 0.989]	yes
PCA(50)+Ridge, ctx=5	0.983	bootstrap CI95 [0.966, 1.002]	inconclusive
PCA(50)+Ridge, ctx=10	1.040	bootstrap CI95 [1.016, 1.065]	no

The headline finding of the temporal protocol: the bundled learned transformer is worse than persistence on every seed, while a simple PCA+Ridge temporal baseline (best 0.942) and modern zero-shot TS-FMs (Chronos-Bolt Base 0.968, TimesFM-2.0 0.969) beat persistence with comfortable margins.

Citation

@misc{bess_bench_2026,
  title        = {BESS-Bench: Long-baseline Be-star spectra for ML time-series research},
  author       = {Anonymous, for the NeurIPS 2026 D\&B Track review},
  year         = {2026},
  note         = {See paper for final authorship list upon acceptance.},
  howpublished = {\url{https://huggingface.co/datasets/anonym-submit-26/bess-bench-26}},
}

When using BESS-Bench, please also cite the underlying BeSS database:

@article{neiner2011bess,
  title   = {The BeSS database},
  author  = {Neiner, C. and others},
  journal = {Astronomical Journal},
  volume  = {142},
  pages   = {149},
  year    = {2011},
  doi     = {10.1088/0004-6256/142/5/149}
}

Licence

CC-BY-4.0. Attribution must credit the BeSS database (Neiner et al. 2011) and the BeSS consortium (LESIA / Observatoire de Paris). Commercial use is permitted under the same attribution conditions.

Acknowledgements

We thank the BeSS community of amateur and professional observers for 35 years of sustained spectroscopic monitoring. BESS-Bench packages and structures these public observations for ML use, and credits the original observers via the BeSS database.