The persistent cosmic web and its filamentary structure I: Theory and implementation

We present DisPerSE, a novel approach to the coherent multi-scale identification of all types of astrophysical structures, and in particular the filaments, in the large scale distribution of matter in the Universe. This method and corresponding piece of software allows a genuinely scale free and parameter free identification of the voids, walls, filaments, clusters and their configuration within the cosmic web, directly from the discrete distribution of particles in N-body simulations or galaxies in sparse observational catalogues. To achieve that goal, the method works directly over the Delaunay tessellation of the discrete sample and uses the DTFE density computed at each tracer particle; no further sampling, smoothing or processing of the density field is required. The idea is based on recent advances in distinct sub-domains of computational topology, which allows a rigorous application of topological principles to astrophysical data sets, taking into account uncertainties and Poisson noise. Practically, the user can define a given persistence level in terms of robustness with respect to noise (defined as a "number of sigmas") and the algorithm returns the structures with the corresponding significance as sets of critical points, lines, surfaces and volumes corresponding to the clusters, filaments, walls and voids; filaments, connected at cluster nodes, crawling along the edges of walls bounding the voids. The method is also interesting as it allows for a robust quantification of the topological properties of a discrete distribution in terms of Betti numbers or Euler characteristics, without having to resort to smoothing or having to define a particular scale. In this paper, we introduce the necessary mathematical background and describe the method and implementation, while we address the application to 3D simulated and observed data sets to the companion paper.Comment: A higher resolution version is available at http://www.iap.fr/users/sousbie together with complementary material. Submitted to MNRA

Combinatorial Gradient Fields for 2D Images with Empirically Convergent Separatrices

This paper proposes an efficient probabilistic method that computes combinatorial gradient fields for two dimensional image data. In contrast to existing algorithms, this approach yields a geometric Morse-Smale complex that converges almost surely to its continuous counterpart when the image resolution is increased. This approach is motivated using basic ideas from probability theory and builds upon an algorithm from discrete Morse theory with a strong mathematical foundation. While a formal proof is only hinted at, we do provide a thorough numerical evaluation of our method and compare it to established algorithms.Comment: 17 pages, 7 figure

Operatori za multi-rezolucione komplekse Morza i ćelijske komplekse

The topic of the thesis is analysis of the topological structure of scalar fields and shapes represented through Morse and cell complexes, respectively. This is achieved by defining simplification and refinement operators on these complexes. It is shown that the defined operators form a basis for the set of operators that modify Morse and cell complexes. Based on the defined operators, a multi-resolution model for Morse and cell complexes is constructed, which contains a large number of representations at uniform and variable resolution.Тема дисертације је анализа тополошке структуре скаларних поља и облика представљених у облику комплекса Морза и ћелијских комплекса, редом. То се постиже дефинисањем оператора за симплификацију и рафинацију тих комплекса. Показано је да дефинисани оператори чине базу за скуп оператора на комплексима Морза и ћелијским комплексима. На основу дефинисаних оператора конструисан је мулти-резолуциони модел за комплексе Морза и ћелијске комплексе, који садржи велики број репрезентација униформне и варијабилне резолуције.Tema disertacije je analiza topološke strukture skalarnih polja i oblika predstavljenih u obliku kompleksa Morza i ćelijskih kompleksa, redom. To se postiže definisanjem operatora za simplifikaciju i rafinaciju tih kompleksa. Pokazano je da definisani operatori čine bazu za skup operatora na kompleksima Morza i ćelijskim kompleksima. Na osnovu definisanih operatora konstruisan je multi-rezolucioni model za komplekse Morza i ćelijske komplekse, koji sadrži veliki broj reprezentacija uniformne i varijabilne rezolucije

Operatori za multi-rezolucione komplekse Morza i ćelijske komplekse

The topic of the thesis is analysis of the topological structure of scalar fields and shapes represented through Morse and cell complexes, respectively. This is achieved by defining simplification and refinement operators on these complexes. It is shown that the defined operators form a basis for the set of operators that modify Morse and cell complexes. Based on the defined operators, a multi-resolution model for Morse and cell complexes is constructed, which contains a large number of representations at uniform and variable resolution.Тема дисертације је анализа тополошке структуре скаларних поља и облика представљених у облику комплекса Морза и ћелијских комплекса, редом. То се постиже дефинисањем оператора за симплификацију и рафинацију тих комплекса. Показано је да дефинисани оператори чине базу за скуп оператора на комплексима Морза и ћелијским комплексима. На основу дефинисаних оператора конструисан је мулти-резолуциони модел за комплексе Морза и ћелијске комплексе, који садржи велики број репрезентација униформне и варијабилне резолуције.Tema disertacije je analiza topološke strukture skalarnih polja i oblika predstavljenih u obliku kompleksa Morza i ćelijskih kompleksa, redom. To se postiže definisanjem operatora za simplifikaciju i rafinaciju tih kompleksa. Pokazano je da definisani operatori čine bazu za skup operatora na kompleksima Morza i ćelijskim kompleksima. Na osnovu definisanih operatora konstruisan je multi-rezolucioni model za komplekse Morza i ćelijske komplekse, koji sadrži veliki broj reprezentacija uniformne i varijabilne rezolucije

Chunk Reduction for Multi-Parameter Persistent Homology

The extension of persistent homology to multi-parameter setups is an algorithmic challenge. Since most computation tasks scale badly with the size of the input complex, an important pre-processing step consists of simplifying the input while maintaining the homological information. We present an algorithm that drastically reduces the size of an input. Our approach is an extension of the chunk algorithm for persistent homology (Bauer et al., Topological Methods in Data Analysis and Visualization III, 2014). We show that our construction produces the smallest multi-filtered chain complex among all the complexes quasi-isomorphic to the input, improving on the guarantees of previous work in the context of discrete Morse theory. Our algorithm also offers an immediate parallelization scheme in shared memory. Already its sequential version compares favorably with existing simplification schemes, as we show by experimental evaluation

\v{C}ech-Delaunay gradient flow and homology inference for self-maps

We call a continuous self-map that reveals itself through a discrete set of point-value pairs a sampled dynamical system. Capturing the available information with chain maps on Delaunay complexes, we use persistent homology to quantify the evidence of recurrent behavior. We establish a sampling theorem to recover the eigenspace of the endomorphism on homology induced by the self-map. Using a combinatorial gradient flow arising from the discrete Morse theory for \v{C}ech and Delaunay complexes, we construct a chain map to transform the problem from the natural but expensive \v{C}ech complexes to the computationally efficient Delaunay triangulations. The fast chain map algorithm has applications beyond dynamical systems.Comment: 22 pages, 8 figure