分类： 天文学 >> 天文学 提交时间： 2023-02-19
Lei Zhang
Zhi-Yu Zhang
James. W. Nightingale
Ze-Cheng Zou
Xiaoyue Cao
Chao-Wei Tsai
Chentao Yang
Yong Shi
Junzhi Wang
Dandan Xu
Ling-Rui Lin
Jing Zhou
Ran Li
摘要： The fast growth of supermassive black holes and their feedback to the host galaxies play an important role in regulating the evolution of galaxies, especially in the early Universe. However, due to cosmological dimming and the limited angular resolution of most observations, it is difficult to resolve the feedback from the active galactic nuclei (AGN) to their host galaxies. Gravitational lensing, for its magnification, provides a powerful tool to spatially differentiate emission originated from AGN and host galaxy at high redshifts. Here we report a discovery of a radio lobe in a strongly lensed starburst quasar, H1413+117 or Cloverleaf at redshift $z= 2.56$, based on observational data at optical, sub-millimetre, and radio wavelengths. With both parametric and non-parametric lens models and with reconstructed images on the source plane, we find a differentially lensed, kpc scaled, single-sided radio lobe, located at ${\sim}1.2\,\mathrm{kpc}$ to the north west of the host galaxy on the source plane. From the spectral energy distribution in radio bands, we find that the radio lobe has an energy turning point residing between 1.5 GHz and 8 GHz, indicating an age of 20--50 Myr. This could indicate a feedback switching of Cloverleaf quasar from the jet mode to the quasar mode.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Peng Zhang
Bing Li
RenZhou Gui
Shaolin Xiong
Ze-Cheng Zou
Xianggao Wang
Xiaobo Li
Ce Cai
Yi Zhao
Yanqiu Zhang
Wangchen Xue
Chao Zheng
Hongyu Zhao
摘要： To research the burst phenomenon of gamma-ray bursts (GRBs) in depth, it is necessary to explore an effective and accurate identification of GRBs. Onboard blind search, ground blind search, and target search method are popular methods in identifying GRBs. However, they undeniably miss GRBs due to the influence of threshold, especially for sub-threshold triggers. We present a new approach to distinguish GRB by using convolutional neural networks (CNNs) to classify count maps that contain bursting information in more dimensions. For comparison, we design three supervised CNN models with different structures. Thirteen years Time-Tagged Event (TTE) format data from Fermi/GBM is employed to construct useful data sets and to train, validate and test these models. We find an optimal model, i.e. the ResNet-CBAM model trained on the 64 ms data set, which contains residual and attention mechanism modules. We track this deep learning model through two visualization analysis methods separately, Gradient-weighted Class Activation Mapping (Grad-CAM) and T-distributed Stochastic Neighbor Embedding (t-SNE) method, and find it focused on the main features of GRBs. By applying it on one-year data, about 96% of GRBs in the Fermi burst catalog were distinguished accurately, six out of ten GRBs of sub-threshold triggers were identified correctly, and meaningfully thousands of new candidates were obtained and listed according to their SNR information. Our study implies that the deep learning method could distinguish GRBs from background-like maps effectively and reliably. In the future, it can be implemented into real-time analysis pipelines to reduce manual inspection and improve accuracy, enabling follow-up observations with multi-band telescopes.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Regardless of their different types of progenitors and central engines, gamma-ray bursts (GRBs) were always assumed to be standalone systems after they formed. Little attention has been paid to the possibility that a stellar companion can still accompany a GRB itself. This paper investigates such a GRB-involved binary system and studies the effects of the stellar companion on the observed GRB emission when it is located inside the jet opening angle. Assuming a typical emission radius of $\sim10^{15}\,$cm, we show that the blockage by a companion star with a radius of $R_\mathrm{c}\sim67\,\mathrm{R_\odot}$ becomes non-negligible when it is located within a typical GRB jet opening angle (e.g., $\sim10$ degrees) and beyond the GRB emission site. In such a case, an on-axis observer will see a GRB with a similar temporal behavior but 25% dimmer. On the other hand, an off-axis observer outside the jet opening angle (hence missed the original GRB) can see a delayed "reflected" GRB, which is much fainter in brightness, much wider in the temporal profile and slightly softer in energy. Our study can naturally explain the origin of some low-luminosity GRBs. Moreover, we also point out that the companion star may be shocked if it is located inside the GRB emission site, which can give rise to an X-ray transient or a GRB followed by a delayed X-ray bump on top of X-ray afterglows.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Peng Zhang
Bing Li
RenZhou Gui
Shaolin Xiong
Ze-Cheng Zou
Xianggao Wang
Xiaobo Li
Ce Cai
Yi Zhao
Yanqiu Zhang
Wangchen Xue
Chao Zheng
Hongyu Zhao
摘要： To research the burst phenomenon of gamma-ray bursts (GRBs) in depth, it is necessary to explore an effective and accurate identification of GRBs. Onboard blind search, ground blind search, and target search method are popular methods in identifying GRBs. However, they undeniably miss GRBs due to the influence of threshold, especially for sub-threshold triggers. We present a new approach to distinguish GRB by using convolutional neural networks (CNNs) to classify count maps that contain bursting information in more dimensions. For comparison, we design three supervised CNN models with different structures. Thirteen years Time-Tagged Event (TTE) format data from Fermi/GBM is employed to construct useful data sets and to train, validate and test these models. We find an optimal model, i.e. the ResNet-CBAM model trained on the 64 ms data set, which contains residual and attention mechanism modules. We track this deep learning model through two visualization analysis methods separately, Gradient-weighted Class Activation Mapping (Grad-CAM) and T-distributed Stochastic Neighbor Embedding (t-SNE) method, and find it focused on the main features of GRBs. By applying it on one-year data, about 96% of GRBs in the Fermi burst catalog were distinguished accurately, six out of ten GRBs of sub-threshold triggers were identified correctly, and meaningfully thousands of new candidates were obtained and listed according to their SNR information. Our study implies that the deep learning method could distinguish GRBs from background-like maps effectively and reliably. In the future, it can be implemented into real-time analysis pipelines to reduce manual inspection and improve accuracy, enabling follow-up observations with multi-band telescopes.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Lei Zhang
Zhi-Yu Zhang
James. W. Nightingale
Ze-Cheng Zou
Xiaoyue Cao
Chao-Wei Tsai
Chentao Yang
Yong Shi
Junzhi Wang
Dandan Xu
Ling-Rui Lin
Jing Zhou
Ran Li
摘要： The fast growth of supermassive black holes and their feedback to the host galaxies play an important role in regulating the evolution of galaxies, especially in the early Universe. However, due to cosmological dimming and the limited angular resolution of most observations, it is difficult to resolve the feedback from the active galactic nuclei (AGN) to their host galaxies. Gravitational lensing, for its magnification, provides a powerful tool to spatially differentiate emission originated from AGN and host galaxy at high redshifts. Here we report a discovery of a radio lobe in a strongly lensed starburst quasar, H1413+117 or Cloverleaf at redshift $z= 2.56$, based on observational data at optical, sub-millimetre, and radio wavelengths. With both parametric and non-parametric lens models and with reconstructed images on the source plane, we find a differentially lensed, kpc scaled, single-sided radio lobe, located at ${\sim}1.2\,\mathrm{kpc}$ to the north west of the host galaxy on the source plane. From the spectral energy distribution in radio bands, we find that the radio lobe has an energy turning point residing between 1.5 GHz and 8 GHz, indicating an age of 20--50 Myr. This could indicate a feedback switching of Cloverleaf quasar from the jet mode to the quasar mode.
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