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kappa)
directly on a NESTED HEALPix sphere from noisy, incomplete shear maps.
Nside=1024 workflow. Positional interpolation is disabled.| Property | Value |
|---|---|
| Resolution | Nside=1024 (12,582,912 pixels) |
| Input | Physical gamma1, gamma2, and integer mask_npix |
| Output | One physical convergence (kappa) map |
| Ordering | HEALPix NESTED |
| Patch size | 16 pixels |
| Encoder | 12 layers, hidden size 768, 12 attention heads |
| Decoder | 8 layers, hidden size 512, 16 attention heads |
| Position embedding | Learned projection; interpolation disabled |
| Released evaluation | One fixed DECaLS-labeled footprint; map rotation disabled |
mask_npix uses 0 = visible, 1 = reconstruction edge, and
2 = unseen/excluded. The repository contains complete weights under the
standard filenames config.json and model.safetensors; it does not contain
LoRA/adapter tensors.python -m pip install "healformers>=0.2,<0.3"1from healformers import get_public_release
2
3release = get_public_release(1024)
4artifacts = release.download_evaluation_artifacts(include_ensemble=False)
5sample = release.load_representative_sample(artifacts.representative_sample)
6pipeline = release.load_pipeline(device="cuda")
7
8kappa = pipeline(*sample.shear, sample.mask_npix)[0, 0]
9print(kappa.shape) # (12582912,)device="cpu" when a GPU is unavailable. get_public_release(1024) pins
the model and dataset revisions, verifies SHA-256 digests, and rejects an
interpolation checkpoint, a rotated map, or any mask other than the released
fixed footprint. For custom inputs, follow the
inference example.| Metric | Ensemble mean ± sample standard deviation |
|---|---|
| Power-ratio RMSE around one | 0.0884 ± 0.0179 |
| Mean harmonic cross correlation | 0.9622 ± 0.0010 |
Nside=256
fixed-mask cases and compares HealFormer with spherical Kaiser--Squires.DECaLS label note: the footprint labeled “DECaLS” in the paper and released artifacts is the combined DECaLS+DES footprint. This is a label-recording offset; the stored mask, calculations, method, and conclusions are unchanged.
Nside=1024 under the released fixed-footprint, preprocessing,
and physical-unit conventions. It has not been validated as a drop-in estimator
for real survey catalogs, survey-specific calibration systematics, spatially
varying noise, arbitrary masks, or rotated footprints. It does not provide
posterior uncertainty or cosmological-parameter inference.release-manifest.json records the byte size and SHA-256 digest of every
release file. The companion dataset repository contains the fixed footprint,
representative inference sample, and raw 100-sample harmonic arrays.1@article{wang2026advancing,
2 title={Advancing weak lensing mass mapping with a mask-aware HEALPix transformer},
3 author={Wang, Yihe and Yu, Yu},
4 journal={Physical Review D},
5 volume={113},
6 number={4},
7 pages={043553},
8 year={2026},
9 publisher={APS},
10 doi={10.1103/kc9z-jllp}
11}