Gamma-Ray Spectral Properties of the Galactic Globular Clusters: Constraint on the Number of Millisecond Pulsars
The Astrophysical Journal(2022)SCI 2区SCI 3区
Yunnan Univ | Chinese Acad Sci
Abstract
We study the γ-ray spectra of 30 globular clusters (GCs) thus far detected with the Fermi Gamma-ray Space Telescope. Presuming that γ-ray emission of a GC comes from millisecond pulsars (MSPs) contained within the GC, a model that generates spectra for the GCs is built based on the γ-ray properties of the detected MSP sample. We fit the GCs’ spectra with the model, and for 27 of them, their emission can be explained as arising from MSPs. The spectra of the other three, NGC 7078, 2MS-GC01, and Terzan 1, cannot be fit with our model, indicating that MSPs’ emission should not be the dominant one in the first two and the third one has a unique hard spectrum. We also investigate six nearby GCs that have relatively high encounter rates compared to the comparison cases. The candidate spectrum of NGC 6656 can be fit with that of one MSP, supporting its possible association with the γ-ray source at its position. The five others do not have detectable γ-ray emission. Their spectral upper limits set limits of ≤1 MSPs in them, consistent with the numbers of radio MSPs found in them. The estimated numbers of MSPs in the γ-ray GCs generally match well those reported for radio pulsars. Our studies of the γ-ray GCs and the comparison nearby GCs indicate that the encounter rate should not be the only factor determining the number of γ-ray MSPs a GC contains.
MoreTranslated text
Key words
Gamma-Ray Astronomy,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
GPU is busy, summary generation fails
Rerequest