인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Context . Modern radio interferometers enable high-resolution polarization imaging, providing insights into cosmic magnetism through rotation measure (RM) synthesis. Traditional 2+1D RM synthesis treats the 2D spatial transform and the 1D transform in frequency space separately. A fully three-dimensional approach transforms the data directly from two spatial frequencies and one wave frequency ( u , υ , v ) to sky-Faraday depth space, using a 3D Fourier transform. Faraday synthesis uses the entire dataset for improved reconstruction, but also requires a 3D deconvolution algorithm to subtract artifacts from the residual image. However, applying this method to modern interferometers requires corrections for direction-dependent effects (DDEs). Aims . We extend Faraday synthesis by incorporating direction-dependent corrections, allowing for accurate polarized imaging in the presence of instrumental and ionospheric effects. Methods . We implemented this method within DDFACET , introducing a direction-dependent deconvolution algorithm (DDFSCLEAN) that applies DDE corrections in a faceted framework. Additionally, we parametrized the CLEAN components and evaluated the model on a larger subset of frequency channels, naturally correcting for bandwidth depolarization. We tested our method on both synthetic and real interferometric data. Results . Our results show that Faraday synthesis enables deeper deconvolution, reducing artifacts and increasing the dynamic range. The bandwidth depolarization correction improves the recovery of polarized flux, allowing coarser frequency resolution without losing sensitivity at high Faraday depths. From the 3D reconstruction, we also identified a polarized source in a LOFAR survey pointing that was not detected by previous RM surveys. Faraday synthesis is memory-intensive due to the large transforms between the visibility domain and the Faraday cube, and thus is only now becoming practical. Nevertheless, our implementation achieves comparable or faster runtimes than the 2+1D approach, making it a competitive alternative for polarization imaging.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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