HLA-Net: A Physics-Guided Skeleton-Based Framework for Automatic Gravitational Arc Detection in Astronomical Images

Don S

astro-ph.IMNov 3, 2026

HLA-Net: A Physics-Guided Skeleton-Based Framework for Automatic Gravitational Arc Detection in Astronomical Images

Authors: Don S

Organizations: School of Computing, Amrita Vishwa Vidyapeetham, Kerala, India

Abstract

Gravitational arcs produced by strong gravitational lensing provide a unique approach for investigating dark matter distribution, galaxy cluster mass reconstruction and the structure of the Universe. Automated detection remains challenging because gravitational arcs are rare, often faint and morphologically similar to elongated galaxies.In this paper, we propose HLA-Net (Hybrid Lens Arc Network), an interpretable multi-stage framework for automatic gravitational arc detection that explicitly exploits both the geometric morphology and physical characteristics of gravitational lensing without requiring manually annotated training datasets.The proposed framework was evaluated on astronomical FITS images containing complex galaxy cluster structures. Starting from 169 initial arc candidates extracted from ordered skeleton representations, the multi-stage verification pipeline reduced the search space to 83 geometrically consistent candidates, while robust circle fitting successfully modelled 113 candidate arc structures. The results demonstrate that HLA-Net effectively suppresses false-positive detections while preserving physically reasonable lens candidates through an interpretable and modular verification strategy.

Figures & tables

Method	Year	Technique	Dataset	Accuracy	Physics-Guided	Interpretability	Verification	DL	Learning	Automation	Cost	Key Limitation
Lenzen et al. [ 8 ]	2004	Anisotropic Diffusion	HST	High	No	Medium	No	No	No	High	Low	Sensitive to threshold selection and fragmented arcs.
Alard [ 12 ]	2006	Morphological Detection	Ground-based	High	No	High	No	No	No	High	Low	Produces false positives in crowded astronomical fields.
Seidel and Bartelmann [ 9 ]	2007	Curvature Analysis	Simulation	Medium	No	High	No	No	No	High	Low	Performance decreases under noisy observations.
Marshall et al. [ 13 ]	2009	Automated Lens Modeling	HST/EGS + Simulations	High	Yes	High	Yes	No	Yes	High	Medium	Dependence on adopted lens and mass-model assumptions.
Lanusse et al. [ 11 ]	2018	CNN	LSST-like Simulations	High	No	Low	No	Yes	Yes	High	Medium	Requires realistic labelled training simulations.
Petrillo et al. [ 14 ]	2019	Deep CNN	KiDS	Medium	No	Low	No	Yes	Yes	High	Medium	Performance depends on the realism of simulated lensed sources.

Table 1: Comparison of representative gravitational lens detection approaches with the proposed HLA-Net framework.

Figure 1: Overall architecture of the proposed Hybrid Lens Arc Network (HLA-Net).

Module	Primary Function
Image Preprocessing	Noise reduction and normalization
Arc Enhancement	Contrast improvement
Candidate Generation	Connected components
Skeleton Extraction	Medial axis computation
Geometry Analysis	Shape verification
Tangential Analysis	Orientation verification

Table 2: Summary of processing modules in the proposed HLA-Net framework.

Rank	Candidate ID	Average Angle ( ∘ )	Tangential Score	Skeleton Length	Interpretation
1	108	89.30	0.9922	33	Excellent tangential alignment
2	4	87.52	0.9724	26	Strong candidate
3	165	87.07	0.9674	33	Strong candidate
4	1	86.69	0.9633	42	Excellent geometry
5	3	85.93	0.9547	26	Good alignment
6	41	83.93	0.9326	29	Good candidate

Table 3: Top candidates after tangential orientation analysis .

Rank	Candidate	Geometry	Tangential	Final Score	Quality
1	171	0.6407	0.9239	0.7811	Good
2	142	0.7563	0.7881	0.7418	Good
3	175	0.6152	0.9140	0.7067	Good
4	120	0.6002	0.9289	0.7023	Good
5	168	0.6210	0.9090	0.6982	Possible
6	106	0.5890	0.9201	0.6823	Possible

Table 4: Highest-ranked candidates after geometry verification.

Rank	Candidate ID	Geometry	Tangential	Radial	Final Lens Score	Quality
1	120	0.6002	0.9289	0.9908	0.8205	Excellent
2	80	0.7912	0.6450	0.9725	0.7917	Good
3	93	0.7727	0.6072	0.9601	0.7666	Good
4	90	0.6528	0.5595	0.9986	0.7145	Good
5	108	0.4849	0.9922	0.7358	0.7074	Good
6	62	0.5878	0.7550	0.7789	0.7018	Good

Table 5: Final ranking of the highest-confidence gravitational lens candidates produced by the proposed HLA-Net framework.

Module	Complexity
Image preprocessing	$\mathcal{O}(N)$
Adaptive enhancement	$\mathcal{O}(N)$
Connected components	$\mathcal{O}(N)$
Skeleton extraction	$\mathcal{O}(N)$
Geometry computation	$\mathcal{O}\!\left(\sum_{i}P_{i}\right)$
Tangential verification	$\mathcal{O}\!\left(\sum_{i}P_{i}\right)$

Table 6: Computational complexity of HLA-Net modules.

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Scientific ML Astrophysics

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The NSF-DOE Vera C. Rubin Observatory, Roman Space Telescope, Euclid, and other next-generation surveys will deliver imaging, spectroscopic, and time-domain data at scales that increasingly shift the bottleneck in astronomical machine learning (ML) projects from model design to infrastructure. We present Hyrax, an open-source, modular, GPU-enabled Python framework that supports the full ML lifecycle in astronomy: from data acquisition and training to inference and experiment comparison, with capabilities including multimodal dataset support, integrated vector databases for similarity search, and interactive two- and three-dimensional latent-space exploration for unsupervised discovery. We demonstrate Hyrax's versatility through five representative applications on real survey data: (i) unsupervised representation learning on

\sim 4\times10^5

Rubin Legacy Survey of Space and Time (LSST) Data Preview 1 (DP1) galaxies, surfacing new merger and low-surface-brightness candidates missing from reference Euclid and Dark Energy Survey catalogs, while also isolating imaging artifacts -- all without labeled training data; (ii) hybrid density-based clustering for identifying cluster-scale gravitational lens candidates in DP1 data; (iii) multimodal early-time transient classification in the Zwicky Transient Facility leveraging light curves, spectra, images, and metadata; (iv) supervised false-positive filtering in shift-and-stack searches for distant solar system objects in the Dark Energy Camera Ecliptic Exploration Project survey; and (v) supervised detection of semi-resolved dwarf galaxies in Hyper Suprime-Cam and LSST-like imaging using synthetic source injection. Together, these results demonstrate that Hyrax provides astronomy-specific ML infrastructure that enables systematic discovery and rapid methodological iteration across next-generation astronomical surveys.

Abstract

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