We have measured the Faraday rotation of 62 extra-galactic background sources in 58 fields using the CSIRO Australia Telescope Compact Array (ATCA) with a frequency range of 1.1 - 3.1 GHz with 2048 channels. Our sources cover a region ∼12 deg × 12 deg (∼1kpc) around the Galactic Centre region. We show that the Galactic Plane for |l| < 10○ exhibits large Rotation Measures (RMs) with a maximum |RM| of 1691.2 ± 4.9 rad m−2 and a mean |RM| = 219 ± 42 rad m−2. The RMs decrease in magnitude with increasing projected distance from the Galactic Plane, broadly consistent with previous findings. We find an unusually high fraction (95%) of the sources show Faraday complexity consistent with multiple Faraday components. We attribute the presences of multiple Faraday rotating screens with widely separated Faraday depths to small-scale turbulent RM structure in the Galactic Centre region. The second order structure function of the RM in the Galactic Centre displays a line with a gradient of zero for angular separations spanning 0.83○ − 11○ (∼120 − 1500 pc), which is expected for scales larger than the outer scale (or driving scale) of magneto-ionic turbulence. We place an upper limit on any break in the SF gradient of 66”, corresponding to an inferred upper limit to the outer scale of turbulence in the inner 1 kpc of the Galactic Centre of 3 pc. We propose stellar feedback as the probable driver of this small-scale turbulence.
Observing the magnetic fields of low-mass interacting galaxies tells us how they have evolved over cosmic time and their importance in galaxy evolution. We have measured the Faraday rotation of 80 extra-galactic radio sources behind the Small Magellanic Cloud (SMC) using the CSIRO Australia Telescope Compact Array (ATCA) with a frequency range of 1.4 – 3.0 GHz. Both the sensitivity of our observations and the source density are an order of magnitude improvement on previous Faraday rotation measurements of this galaxy. The SMC generally produces negative rotation measures (RMs) after accounting for the Milky Way foreground contribution, indicating that it has a mean coherent line-of-sight magnetic field strength of −0.3 ± 0.1μG, consistent with previous findings. We detect signatures of magnetic fields extending from the north and south of the Bar of the SMC. The random component of the SMC magnetic field has a strength of ∼5μG with a characteristic size-scale of magneto-ionic turbulence <250 pc, making the SMC like other low-mass interacting galaxies. The magnetic fields of the SMC and Magellanic Bridge appear similar in direction and strength, hinting at a connection between the two fields as part of the hypothesised ‘pan-Magellanic’ magnetic field.
Faraday rotation measures (RMs) have been used for many studies of cosmic magnetism, and in most cases having more RMs is beneficial for those studies. This has lead to the development of RM surveys that have produced large catalogs, as well as meta-catalogs collecting RMs from many different publications. However, it has been difficult to take full advantage of all of these RMs, as the individual catalogs have been published in many different places, and in many different formats. In addition, the polarization spectra used to determine these RMs are rarely published, limiting the ability to reanalyze data as new methods or additional observations become available. We propose a standard convention for RM catalogs, RMTable2023, and a standard for source-integrated polarized spectra of radio sources, PolSpectra2023. These standards are intended to maximize the value and utility of these data for researchers and to make them easier to access. To demonstrate the use of the RMTable2023 standard, we have produced a consolidated catalog of 55,819 RMs collected from 42 published catalogs.
Faraday complexity describes whether a spectropolarimetric observation has simple or complex magnetic structure. Quickly determining the Faraday complexity of a spectropolarimetric observation is important for processing large, polarised radio surveys. Finding simple sources lets us build rotation measure grids, and finding complex sources lets us follow these sources up with slower analysis techniques or further observations. We introduce five features that can be used to train simple, interpretable machine learning classifiers for estimating Faraday complexity. We train logistic regression and extreme gradient boosted tree classifiers on simulated polarised spectra using our features, analyse their behaviour, and demonstrate our features are effective for both simulated and real data. This is the first application of machine learning methods to real spectropolarimetry data. With 95% accuracy on simulated ASKAP data and 90% accuracy on simulated ATCA data, our method performs comparably to state-of-the-art convolutional neural networks while being simpler and easier to interpret. Logistic regression trained with our features behaves sensibly on real data and its outputs are useful for sorting polarised sources by apparent Faraday complexity.
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