WeChat Mini Program
Old Version Features

Polarisation Results from the GOODS-N Field with Apertif and Polarised Source Counts

ASTRONOMY & ASTROPHYSICS(2025)

Ruhr Univ Bochum

Cited 0|Views3
Abstract
Aims. We analysed six Apertif datasets, covering the GOODS-N LOFAR deep field region, aiming to improve our understanding of the faint radio source composition, their polarisation behaviour, and how this affects our interpretation of polarised source counts. Methods. Using a semi-automatic routine, we ran rotation measure synthesis to generate a polarised intensity mosaic for each observation. The routine also performs source finding and cross-matching with the total power catalogue, as well as NVSS, SDSS and allWISE, to obtain a catalogue of 1182 polarised sources in an area of 47.4 deg(2). Using the mid-infrared (MIR) radio correlation, we found no indication of any polarised emission from star formation. To robustly estimate the source counts, we performed an investigation of our sample's completeness as a function of the polarised flux via synthetic source injection. Results. In contrast to previous works, we find no strong dependency of fractional polarisation on the total intensity flux density. We argue that differences regarding previous (small-scale, deep field) analyses can be attributed to sample variance. Relative to the findings of previous works, here we find a steeper slope for our Euclidean-normalised differential source counts. This is also visible as a flattening in cumulative source counts. Conclusions. We attribute the observed steeper slope in Euclidean normalised differential source counts to a change in the source composition and properties at low total intensities.
More
Translated text
Key words
magnetic fields,polarization,galaxies: active,cosmology: observations,radio continuum: general
求助PDF
上传PDF
Bibtex
收藏
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文通过分析Apertif望远镜在GOODS-N区域的数据,研究了弱无线电源的极化特性,发现了与之前研究不同的极化源计数斜率,指出这种差异可能与样本方差和源组成变化有关。

方法】:使用半自动程序进行旋转测量合成,生成每个观测的极化强度镶嵌图,并通过与总功率目录、NVSS、SDSS和allWISE的交叉匹配,得到包含1182个极化源的区域目录。

实验】:通过合成源注入方法研究了样本完整性,发现极化源计数与总强度通量密度无强依赖关系,且Euclidean归一化的微分源计数斜率比之前研究更陡,表现为累积源计数的平坦化。数据集名称为Apertif观测的GOODS-N区域数据。