分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Strong lensing in galaxy clusters probes properties of dense cores of dark matter halos in mass, studies the distant universe at flux levels and spatial resolutions otherwise unavailable, and constrains cosmological models independently. The next-generation large scale sky imaging surveys are expected to discover thousands of cluster-scale strong lenses, which would lead to unprecedented opportunities for applying cluster-scale strong lenses to solve astrophysical and cosmological problems. However, the large dataset challenges astronomers to identify and extract strong lensing signals, particularly strongly lensed arcs, because of their complexity and variety. Hence, we propose a framework to detect cluster-scale strongly lensed arcs, which contains a transformer-based detection algorithm and an image simulation algorithm. We embed prior information of strongly lensed arcs at cluster-scale into the training data through simulation and then train the detection algorithm with simulated images. We use the trained transformer to detect strongly lensed arcs from simulated and real data. Results show that our approach could achieve 99.63 % accuracy rate, 90.32 % recall rate, 85.37 % precision rate and 0.23 % false positive rate in detection of strongly lensed arcs from simulated images and could detect almost all strongly lensed arcs in real observation images. Besides, with an interpretation method, we have shown that our method could identify important information embedded in simulated data. Next step, to test the reliability and usability of our approach, we will apply it to available observations (e.g., DESI Legacy Imaging Surveys) and simulated data of upcoming large-scale sky surveys, such as the Euclid and the CSST.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Strong lensing in galaxy clusters probes properties of dense cores of dark matter halos in mass, studies the distant universe at flux levels and spatial resolutions otherwise unavailable, and constrains cosmological models independently. The next-generation large scale sky imaging surveys are expected to discover thousands of cluster-scale strong lenses, which would lead to unprecedented opportunities for applying cluster-scale strong lenses to solve astrophysical and cosmological problems. However, the large dataset challenges astronomers to identify and extract strong lensing signals, particularly strongly lensed arcs, because of their complexity and variety. Hence, we propose a framework to detect cluster-scale strongly lensed arcs, which contains a transformer-based detection algorithm and an image simulation algorithm. We embed prior information of strongly lensed arcs at cluster-scale into the training data through simulation and then train the detection algorithm with simulated images. We use the trained transformer to detect strongly lensed arcs from simulated and real data. Results show that our approach could achieve 99.63 % accuracy rate, 90.32 % recall rate, 85.37 % precision rate and 0.23 % false positive rate in detection of strongly lensed arcs from simulated images and could detect almost all strongly lensed arcs in real observation images. Besides, with an interpretation method, we have shown that our method could identify important information embedded in simulated data. Next step, to test the reliability and usability of our approach, we will apply it to available observations (e.g., DESI Legacy Imaging Surveys) and simulated data of upcoming large-scale sky surveys, such as the Euclid and the CSST.
分类： 核科学技术 >> 放射性计量学 提交时间： 2024-04-26
摘要： The aim of this study is to evaluate the uncertainty of 2πα and 2πβ surface emission rates using the windowless multiwire proportional counter method. This study used the Monte Carlo method (MCM) to validate the conventional Guide to the Expression of Uncertainty in Measurement (GUM) method. A dead-time measurement model for the two-source method was established based on the characteristics of a single-channel measurement system, and the voltage threshold correction factor measurement function was indirectly obtained by fitting the threshold correction curve. The uncertainty in the surface emission rate was calculated using the GUM method and the law of propagation of uncertainty. The MCM provided clear definitions for each input quantity and its uncertainty distribution, and the simulation training was realized with a complete and complex mathematical model. The results of the surface emission rate uncertainty evaluation for four radioactive plane sources using both methods showed an uncertainty consistency En < 0.070 for the comparison of each source, and the uncertainty results of the GUM were all lower than those of the MCM. However, the MCM has a more objective evaluation process and can serve as a validation tool for GUM results.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Shi Dai
Jiguang Lu
Chen Wang
Weiyang Wang
Renxin Xu
Yuanpei Yang
Songbo Zhang
George Hobbs
Di Li
Rui Luo
Miroslav Filipovic
Jinchen Jiang
摘要： Fast spinning (e.g., sub-second) neutron star with ultra-strong magnetic fields (or so-called magnetar) is one of the promising origins of repeating fast radio bursts (FRBs). Here we discuss circularly polarised emissions produced by propagation effects in the magnetosphere of fast spinning magnetars. We argue that the polarisation-limiting region is well beyond the light cylinder, suggesting that wave mode coupling effects are unlikely to produce strong circular polarisation for fast spinning magnetars. Cyclotron absorption could be significant if the secondary plasma density is high. However, high degrees of circular polarisation can only be produced with large asymmetries in electrons and positrons. We draw attention to the non-detection of circular polarisation in current observations of known repeating FRBs. We suggest that the circular polarisation of FRBs could provide key information on their origins and help distinguish different radiation mechanisms.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Rui Luo
George Hobbs
Suk Yee Yong
Andrew Zic
Lawrence Toomey
Shi Dai
Alex Dunning
Di Li
Tommy Marshman
Chen Wang
Pei Wang
Shuangqiang Wang
Songbo Zhang
摘要： We describe a new software package for simulating channelised, high-time resolution data streams from radio telescopes. The software simulates data from the telescope and observing system taking into account the observation strategy, receiver system and digitisation. The signatures of pulsars, fast radio bursts and flare stars are modelled, including frequency-dependent effects such as scattering and scintillation. We also simulate more generic signals using spline curves and images. Models of radio frequency interference include signals from satellites, terrestrial transmitters and impulsive, broadband signals. The simulated signals can also be injected into real data sets. Uses of this software include the production of machine learning training data sets, development and testing of new algorithms to search for anomalous patterns and to characterise processing pipelines.
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