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CAS IR Grid
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高能物理研究所 [1]
中国科学院大学 [1]
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期刊论文 [2]
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2019 [1]
2008 [1]
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Timing analysis of Swift J1658.2-4242's outburst in 2018 with Insight-HXMT, NICER and AstroSat
期刊论文
OAI收割
Journal of High Energy Astrophysics, 2019, 卷号: 24, 页码: 30-40
作者:
HXMT
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浏览/下载:57/0
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提交时间:2022/02/08
Accretion
accretion disks
Black hole physics
X-rays: binaries:
Swift J1658.2-4242
Astrophysics - High Energy Astrophysical Phenomena
Abstract: We present the observational results from a detailed timing analysis of the black hole candidate Swift J1658.2-4242 during its 2018 outburst with the observations of Hard X-ray Modulation Telescope (Insight-HXMT), Neutron Star Interior Composition Explorer (NICER) and AstroSat in 0.1-250 keV. The evolution of intensity, hardness and integrated fractional root mean square (rms) observed by Insight-HXMT and NICER are presented in this paper. Type-C quasi-periodic oscillations (QPOs) observed by NICER (0.8-3.5 Hz) and Insight-HXMT (1-1.6 Hz) are also reported in this work. The features of the QPOs are analyzed with an energy range of 0.5-50 keV. The relations between QPO frequency and other characteristics such as intensity, hardness and QPO rms are carefully studied. The timing and spectral properties indicate that Swift J1658.2-4242 is a black hole binary system. Besides, the rms spectra of the source calculated from the simultaneous observation of Insight-HXMT, NICER and AstroSat support the Lense-Thirring origin of the QPOs. The relation between QPO phase lag and the centroid frequency of Swift J1658.2-4242 reveals a near zero constant when < 4Hz and a soft phase lag at 6.68 Hz. This independence follows the same trend as the high inclination galactic black hole binaries such as MAXI J1659-152.
Non-stationary thermal time accumulation reduces the predictability of climate change effects on agriculture
期刊论文
iSwitch采集
Agricultural and forest meteorology, 2008, 卷号: 148, 期号: 10, 页码: 1412-1418
作者:
Zhang, Tianyi
;
Zhu, Jiang
;
Yang, Xiaoguang
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浏览/下载:62/0
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提交时间:2019/05/10
Phenology
Lowland rice
Thermal time accumulation
Observed trend
Model deficiency
Climate change