The Galactic Neutron Star Population - II. Systemic Velocities and Merger Locations of Binary Neutron Stars
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY(2024)
Abstract
ABSTRACT The merger locations of binary neutron stars (BNSs) encode their galactic kinematics and provide insights into their connection to short gamma-ray bursts (SGRBs). In this work, we use the sample of Galactic BNSs with measured proper motions to investigate their kinematics and predict their merger locations. Using a synthetic image of the Milky Way and its Galactic potential we analyse the BNS mergers as seen from an extragalactic viewpoint and compare them to the location of SGRBs on and around their host galaxies. We find that the Galactocentric transverse velocities of the BNSs are similar in magnitude and direction to those of their Local Standards of Rest, which implies that the present-day systemic velocities are not isotropically oriented and the peculiar velocities might be as low as those of BNS progenitors. Both systemic and peculiar velocities fit a lognormal distribution, with the peculiar velocities being as low as ∼22–157 km s−1. We also find that the observed BNS sample is not representative of the whole Galactic population, but rather of systems born around the Sun’s location with small peculiar velocities. When comparing the predicted BNS merger locations to SGRBs, we find that they cover the same range of projected offsets, host-normalized offsets, and fractional light. Therefore, the spread in SGRB locations can be reproduced by mergers of BNSs born in the Galactic disc with small peculiar velocities, although the median offset match is likely a coincidence due to the biased BNS sample.
MoreTranslated text
Key words
Compact Binary Mergers,Gamma-Ray Bursts
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话