Lens Parameters for Gaia18cbf -- a Long Gravitational Microlensing Event in the Galactic Plane
semanticscholar（2021）
Abstract
Context. The size of the Einstein Radius in a microlensing event scales as a square root of the mass of the lens. Therefore, long-lasting microlensing events are the best candidates for massive lenses, including black holes. Aims. Here we present the analysis of the Gaia18cbf microlensing event reported by the Gaia Science Alerts system. It has exhibited a long timescale and features characteristic to the annual microlensing parallax effect. We deduce the parameters of the lens based on the derived best fitting model. Methods. We used photometric data collected by the Gaia satellite as well as the follow-up data gathered by the ground-based observatories. We investigate the range of microlensing models and use them to derive the most probable mass and distance to the lens using a Galactic model as a prior. Using known mass-brightness relation we determined how likely it is that the lens is a main sequence star. Results. This event is one of the longest ever detected, with the Einstein timescale of tE = 491.41+128.31 −84.94 days for the best solution and tE = 453.74+178.69 −105.74 days for the second-best. This translates to a lens mass of ML = 2.91 +6.02 −1.70 M and ML = 1.88 +4.40 −1.19 M respectively. The limits on the blended light suggest that this event was most likely not caused by a main sequence star, but rather by a dark remnant of stellar evolution.
MoreTranslated text
求助PDF
上传PDF
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
Upload PDF to Generate Summary
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