Finding manifold structures in noisy and high-dimensional point clouds is a challenging but important problem.In astronomical observation survey and simulation data the detection of filaments, streams (1D), walls (2D) and clusters (3D) gives rise to deeper understanding of the evolution of our universe.The Locally Aligned Ant Technique (LAAT) uses biologically inspired agents to efficiently recover faint and multidimensional structures.However, very dense hubs (e.g.nodes or globular clusters) dominate the ants' activity, creating unnecessary computational overheads.In this paper we propose a two-stage solution.First a fast preprocessing step locates the hubs and replaces them with a tailored likelihood model.Subsequently, a mixed likelihood-pheromone strategy guides the ants to efficiently bridge the dense regions.We demonstrate improvements in detection efficiency and robustness of LAAT with synthetic and a large-scale astronomical N-body simulation of the cosmic web.

The detection and extraction of noisy manifolds from data have various applications, ranging from dimensionality reduction, computer graphics, signal processing, and robotics to the modelling of astronomical structures. In Astronomy, the detection of faint streams and filaments is challenging due to background contamination, which immerses and hides them in noise. The biologically inspired Locally Aligned Ant Technique (LAAT), followed by Manifold Blurring Mean Shift (MBMS) have been demonstrated as an efficient and flexible algorithm for detecting and denoising versatile structures within noisy backgrounds. Our contribution extends both methods by introducing a dynamic local radius, thereby allowing a flexible configuration and reducing sensitivity to a critical hyper-parameter for both of them. For LAAT we propose additional synergy by introducing also locally variable pheromone deposition. The former avoids highlighting spurious patterns in noisy regions and allows smaller movement in areas with strong alignment. The latter increases pheromone deposition in fainter zones. We demonstrate and analyse the novel extensions in two astronomical datasets, namely a synthetic jellyfish galaxy and an N-body cosmic web simulation.

The detection and extraction of noisy manifolds from data have various applications.In Astronomy, the detection of faint streams and filaments is particularly difficult due to background contamination, which immerses and hides them in noise.The biologically inspired Locally Aligned Ant Technique (LAAT) has been demonstrated as an efficient and flexible algorithm to detect and denoise versatile structures within noisy backgrounds.Our contribution extends LAAT two-fold: (1) introduction of a dynamic local radius, and (2) locally variable pheromone deposition.The former avoids highlighting spurious patterns in noisy regions and allows smaller jumps in areas with strong alignment.The latter increases pheromone deposition in fainter zones.We demonstrate this in 2 datasets.

The automatic detection, extraction, and modeling of manifold structures from large data-sets are of great interest, especially in Astronomy.Existing manifold learning techniques for feature extraction in Computer Vision, Bioinformatics and signal denoising typically fail in astronomical scenarios, since they mostly assume low levels of noise and one manifold of fixed dimension.Therefore, the Locally Aligned Ant Technique (LAAT) was recently proposed to discover multiple faint and noisy structures of varying dimensionality embedded in large amounts of background noise.Although it demonstrates excellent results in multiple scenarios, its performance depends on global thresholding and user tuning.Here, we improve LAAT and replace the global threshold by a flexible local strategy.

Dimensionality reduction and clustering are often used as preliminary steps for many complex machine learning tasks. The presence of noise and outliers can deteriorate the performance of such preprocessing and therefore impair the subsequent analysis tremendously. In manifold learning, several studies indicate solutions for removing background noise or noise close to the structure when the density is substantially higher than that exhibited by the noise. However, in many applications, including astronomical datasets, the density varies alongside manifolds that are buried in a noisy background. We propose a novel method to extract manifolds in the presence of noise based on the idea of Ant colony optimization. In contrast to the existing random walk solutions, our technique captures points that are locally aligned with major directions of the manifold. Moreover, we empirically show that the biologically inspired formulation of ant pheromone reinforces this behavior enabling it to recover multiple manifolds embedded in extremely noisy data clouds. The algorithm performance in comparison to state-of-the-art approaches for noise reduction in manifold detection and clustering is demonstrated, on several synthetic and real datasets, including an N-body simulation of a cosmological volume.

Component-graphs provide powerful and complex structures for multi-band image processing. We propose a multiband astronomical source detection framework with the component-graphs relying on a new set of component attributes. We propose two modules to differentiate nodes belong to distinct objects and to detect partial object nodes. Experiments demonstrate an improved capacity at detecting faint objects on a multi-band astronomical dataset.