A new article of the GRATOS team has been recently published in Monthly Notices of the Royal Astronomical Society (MNARS).

MNARS is one of the world’s leading primary research journals in astronomy and astrophysics, as well as one of the longest established.

It publishes the results of original research in positional and dynamical astronomy, astrophysics, radio astronomy, cosmology, space research and the design of astronomical instruments.

The article is entitled “Detecting and analysing the topology of the cosmic web with spatial clustering algorithms I: Methods”. The work is a joint work by researchers from NTUA (D. Kelesis,D. Fotakis), Spyros Vasilakos (National Observatory of Athens and Academy of Athens), and Vicky Papadopoulou and Andreas Efstathiou (EUC).

The article explores the use of spatial clustering algorithms as a new computational approach for modeling the cosmic web. The authors demonstrate that such algorithms are efficient in terms of computing time needed. They explore three distinct spatial methods which we suitably adjust for (i) detecting the topology of the cosmic web and (ii) categorizing various cosmic structures as voids, walls, clusters and superclusters based on a variety of topological and physical criteria such as the physical distance between objects, their masses and local densities. The methods explored are (1) a new spatial method called Gravity Latticexs; (2) a modified version of another spatial clustering algorithm, the ABACUS; and (3) the well known spatial clustering algorithm HDBSCAN. HDBSCAN is utilized in order to detect cosmic structures and categorize them using their overdensity. Finally, we further extend our experiments and verify their effectiveness by showing their ability to scale well with different cosmic web structures that formed at different redshifts.

The article is accessible in:




Paper citation:

Dimitrios Kelesis, Spyros Basilakos, Vicky Papadopoulou Lesta, Dimitris Fotakis, Andreas Efstathiou, Detecting and analysing the topology of the cosmic web with spatial clustering algorithms I: Methods, Monthly Notices of the Royal Astronomical Society, 2022