Search

Most massive stars are members of a binary or a higher-order stellar systems, where the presence of a binary companion can decisively alter their evolution via binary interactions. Interacting binaries are also important astrophysical laboratories for the study of compact objects. Binary population synthesis studies have been used extensively over the last two decades to interpret observations of compact-object binaries and to decipher the physical processes that lead to their formation. Here, we present POSYDON, a novel, binary population synthesis code that incorporates full stellar-structure and binary-evolution modeling, using the MESA code, throughout the whole evolution of the binaries. The use of POSYDON enables the self-consistent treatment of physical processes in stellar and binary evolution, including: realistic mass-transfer calculations and assessment of stability, internal angular-momentum transport and tides, stellar core sizes, mass-transfer rates and orbital periods. This paper describes the detailed methodology and implementation of POSYDON, including the assumed physics of stellar- and binary-evolution, the extensive grids of detailed single- and binary-star models, the post-processing, classification and interpolation methods we developed for use with the grids, and the treatment of evolutionary phases that are not based on pre-calculated grids. The first version of POSYDON targets binaries with massive primary stars (potential progenitors of neutron stars or black holes) at solar metallicity., Comment: 60 pages, 33 figures, 8 tables, referee's comments addressed. The code and the accompanying documentations and data products are available at https:\\posydon.org

Traditionally, clustering of multivariate data aims at grouping objects described with multiple heterogeneous attributes based on a suitable similarity (conversely, distance) function. One of the main challenges is due to the fact that it is not straightforward to directly apply mathematical operations (e.g., sum, average) to the feature values, as they stem from heterogeneous contexts. In this work we take the challenge a step further and tackle the problem of clustering multivariate datasets based on jointly considering: (a) similarity among a subset of the attributes; and (b) distance-based diversity among another subset of the attributes. Specifically, we focus on astrophysics data, where the snapshots of the stellar evolution for different stars contain over 40 distinct attributes corresponding to various physical and categorical (e.g., 'black hole') attributes. We present CSD-CAMD -- a prototype system for Coupling Similarity and Diversity for Clustering Astrophysics Multivariate Datasets. It provides a flexibility for the users to select their preferred subsets of attributes; assign weight (to reflect their relative importance on the clustering); and select whether the impact should be in terms of proximity or distance. In addition, CSD-CAMD allows for selecting a clustring algorithm and enables visualization of the outcome of clustering.