Organizations: New Jersey Institute of Technology, Newark, NJ · SouthWest Research Institute, Boulder, CO · Sam Houston State University, Huntsville, TX · University of Iowa, Iowa City, IA
The long observational record of He I 10830 Å offers a means to investigate solar morphology before modern extreme-ultraviolet (EUV) imaging. We adapt the Surya solar foundation model to predict Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) 94, 193, and 304 Å images and a coronal hole (CH) probability map from full-disk helium observations. A convolutional input adapter, low-rank backbone updates, and dedicated output decoders learn from temporally paired, geometrically registered observations, with Spatial Possibilistic Clustering Algorithm (SPOCA) catalog polygons providing CH supervision. On observations held out from downstream fine-tuning, the selected dedicated models achieve disk-restricted correlation coefficients (CCs) of 0.8196, 0.8885, and 0.8398 for the three AIA channels, respectively; the CH model achieves an intersection over union (IoU) of 0.4360. The predictions recover broad solar structure, although local agreement varies substantially by channel. An optional residual refiner addresses spatial detail, and its application on pre-SDO dates improves the correlation of synthetic AIA 304 with Solar and Heliospheric Observatory/Extreme-ultraviolet Imaging Telescope (SOHO/EIT) 304 references from 0.6511 to 0.7085. Together, these results support the feasibility of helium-conditioned EUV morphological proxies and motivate their use in historical reconstruction, within the scope of the downstream test and cross-instrument evaluation.
Figures & tables
Fig. 1: Four independent Surya–LoRA adaptations of the same full-disk He I input. Each row represents a separately tuned backbone adapter and single-output decoder. The selected AIA 304 configuration adds a nonlinear token MLP before linear patch reconstruction; the other dedicated configurations use a linear patch decoder. Each AIA branch optionally applies an independently trained residual refiner conditioned on He I and its synthetic output. The CH path converts logits to probabilities with sigmoid and bypasses refinement.
Split
Months
193 Å
304 Å
94 Å
Training
January–October
679
679
678
Validation
November
87
87
87
Test
December
57
57
57
TABLE I: Full-resolution EUV evaluation cohorts. Counts refer to the quality-controlled manifests used by the current experiments.
Component
Setting
Input/output grid
4096×4096 pixels
Input adapter
1→32→32→13 channels
Output decoder
linear patch projection; optional token MLP
LoRA targets
attention, dynamic, and MLP projections
LoRA rank/scale
varied by output; α=2r in sweeps
LoRA dropout
0.05
TABLE II: Fine-tuning configuration.
Output
Fixed configuration
Metric
r=8
r=16
r=32
r=64
r=128
AIA 94
Linear, L1
MAE ↓
–
0.0648
0.0660
0.0637
–
AIA 94
Linear, L1+G
MAE ↓
–
–
–
0.0628
0.0723
AIA 193
Linear, L1
MAE ↓
0.0578
0.0566
0.0546
0.0558
–
AIA 304
Linear, L1
MAE ↓
–
0.0730
0.0764
0.0724
–
AIA 304
Token MLP, L1
MAE ↓
–
–
–
0.0720
0.0788
CH
Linear, 2 BCE+ 1 Dice
Dice ↑
–
0.4162
0.4102
0.4100
–
TABLE III: Native-resolution hyperparameter tuning for dedicated outputs. Each row keeps its stated head and objective fixed. AIA entries are validation MAE (lower is better); CH entries are validation Dice (higher is better). L1 denotes absolute-error loss, G gradient consistency, and BCE binary cross-entropy. Dashes denote rank–configuration combinations without a reported validation score; bold entries identify the best reported score in each row.
Output
Rank
α
Head/objective
CC
MAE
PSNR
SSIM
F1
IoU
AIA 94
64
128
Linear, L1+G
0.8196
0.0597
21.193
0.2806
–
–
AIA 193
32
64
Linear, L1+G+S
0.8885
0.0443
22.716
0.8532
–
–
AIA 304
64
128
Token MLP, L1
0.8398
0.0606
20.775
0.5317
–
–
CH
n.r.
n.r.
Linear + sigmoid
–
–
–
–
0.6073
0.4360
TABLE IV: Native-resolution held-out performance without refinement. AIA entries are mean image-level scores from 4096×4096 scalar outputs on 57 December observations and only within the WCS solar disk. Each row states the exact retained candidate configuration. G and S denote gradient and SSIM losses, each with weight 0.1.
Fig. 2: Archived illustrative held-out examples from earlier dedicated Surya–LoRA checkpoints; these are not the exact AIA 94/304 rank-64 checkpoints in Table IV . Columns: observed He I 10830 Å , AIA 94, AIA 193, AIA 304, and CH. For each date, the upper row contains observations/catalog targets, and the lower row contains synthetic outputs; the synthetic He I 10830 Å cell is blank. The underlying predictions are native 4096×4096 scalar arrays; color mapping and print rasterization are used only for display.
Fig. 3: Historical synthesis for 2005 August 3 and 2007 May 17. Each date occupies three rows: observations, Surya–LoRA predictions, and refined predictions. The columns contain He I 10830 Å, EIT 195/synthetic AIA 193 Å, and EIT 304/synthetic AIA 304 Å. The layout contains six rows and three columns. He I appears only in the first row of each date group; its two lower cells are blank. Predictions are generated as 4096×4096 scalar arrays; colors are for display. EIT provides an independent cross-instrument reference, not AIA ground truth.
Method
CC ↑
MAE ↓
PSNR ↑
SSIM ↑
Surya–LoRA
0.6511
0.1032
16.985
0.3610
Surya–LoRA + refiner
0.7085
0.0668
20.597
0.4535
TABLE V: Exploratory agreement of synthetic AIA 304 Å with observed SOHO/EIT 304 Å on three historical dates (2005 July 10, 2005 August 3, and 2007 May 17); the latter two are illustrated in Fig. 3 . Mean image-level metrics use a common 1024×1024 grid and only the solar disk. EIT is a cross-instrument reference, not AIA ground truth.
Department of Mechanical and Industrial Engineering, New Jersey Institute of Technology · Department of Physics, New Jersey Institute of Technology · Department of Computer Science, Sam Houston State University +2