five

On Frequency-Dependent Dispersion Measures and “Extreme Scattering Events”

收藏
DataCite Commons2023-02-07 更新2025-04-16 收录
下载链接:
https://dataverse.jpl.nasa.gov/citation?persistentId=doi:10.48577/jpl.DKRNWR
下载链接
链接失效反馈
官方服务:
资源简介:
Radio emission propagating over an Earth-pulsar line of sight provides a unique probe of the intervening ionized interstellar medium (ISM). Variations in the integrated electron column density along this line of sight, or dispersion measure (DM), have been observed since shortly after the discovery of pulsars. As early as 2006, frequency-dependent dispersion measures have been observed and attributed to several possible causes. Raypath averaging over different effective light-cone volumes through the turbulent ISM contributes to this effect as will DM misestimation due to radio propagation across compact lensing structures such as those caused by “extreme scattering events”. We present methods to assess the variations in frequency-dependent dispersion measures due to the turbulent ISM versus these compact lensing structures along the line of sight. We analyze recent Low-Frequency Array (LOFAR) observations of PSR J2219+4754 to test the underlying physical mechanism of the observed frequency-dependent DM. Previous analyses have indicated the presence of strong lensing due to compact overdensities halfway between the Earth and pulsar. Instead we find the frequency dependence of the DM timeseries for PSR J2219+4754 is consistent with being due solely to ISM turbulence and there is no evidence for any extreme scattering event or small-scale lensing structure. The data show possible deviations from a uniform turbulent medium, suggesting that there may be an enhanced scattering screen near one of the two ends of the line of sight. We present this analysis as an example of the power of low-frequency observations to distinguish the underlying mechanisms in frequency-dependent propagation effects.
提供机构:
Root
创建时间:
2023-02-07
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作