Most massive stars are members of a binary or a higher-order stellar system, 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, publicly available, 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 postprocessing, classification, and interpolation methods we developed for use with the grids, and the treatment of evolutionary phases that are not based on precalculated grids. The first version of POSYDON targets binaries with massive primary stars (potential progenitors of neutron stars or black holes) at solar metallicity.

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 angularmomentum 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.

Recent 1D core-collapse simulations indicate a nonmonotonicity of the explodability of massive stars with respect to their precollapse core masses, which is in contrast to commonly used prescriptions. In this work, we explore the implications of these results on the formation of coalescing black hole (BH)–neutron star (NS) binaries. Furthermore, we investigate the effects of natal kicks and the NS’s radius on the synthesis of such systems and potential electromagnetic counterparts (EMCs) linked to them. Models based on 1D core-collapse simulations result in a BH–NS merger detection rate ( ∼ 2.3 yr−1), 5–10 times larger than the predictions of “standard” prescriptions. This is primarily due to the formation of low-mass BHs via direct collapse, and hence no natal kicks, favored by the 1D simulations. The fraction of observed systems that will produce an EMC, with the supernova engine from 1D simulations, ranges from 2% to 25%, depending on the NS equation of state. Notably, in most merging systems with EMCs, the NS is the first-born compact object, as long as the NS’s radius is ≲ 12 km. Furthermore, models with negligible kicks for low-mass BHs increase the detection rate of GW190426_152155-like events to ∼ 0.6 yr−1, with an associated probability of EMC ≤10% for all supernova engines. Finally, models based on 1D core-collapse simulations predict a ratio of BH–NSs to binary BHs’ merger rate density that is at least twice as high as other prescriptions, but at the same time overpredicting the measured local merger density rate of binary black holes.

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Recent work in signal processing in general and image processing in particular deals with sparse representation related problems. Two such problems are of paramount importance: an overriding need for designing a well-suited overcomplete dictionary containing a redundant set of atoms-i.e., basis signals-and how to find a sparse representation of a given signal with respect to the chosen dictionary. Dictionary learning techniques, among which we find the popular K-singular value decomposition algorithm, tackle these problems by adapting a dictionary to a set of training data. A common drawback of such techniques is the need for parameter-tuning. In order to overcome this limitation, we propose a fully-automated Bayesian method that considers the uncertainty of the estimates and produces a sparse representation of the data without prior information on the number of non-zeros in each representation vector. We follow a Bayesian approach that uses a three-tiered hierarchical prior to enforce sparsity on the representations and develop an efficient variational inference framework that reduces computational complexity. Furthermore, we describe a greedy approach that speeds up the whole process. Finally, we present experimental results that show superior performance on two different applications with real images: denoising and inpainting.