Adam K. Leroy, Annie Hughes, Daizhong Liu, Jerome Pety, Erik Rosolowsky, Toshiki Saito, Eva Schinnerer, Andreas Schruba, Antonio Usero, Christopher M. Faesi, Ci… 展开作者
Adam K. Leroy, Annie Hughes, Daizhong Liu, Jerome Pety, Erik Rosolowsky, Toshiki Saito, Eva Schinnerer, Andreas Schruba, Antonio Usero, Christopher M. Faesi, Cinthya N. Herrera, Melanie Chevance, Alexander P. S. Hygate, Amanda A. Kepley, Eric W. Koch, Miguel Querejeta, Kazimierz Sliwa, David Will, Christine D. Wilson, Gagandeep S. Anand, Ashley Barnes, Francesco Belfiore, Ivana Beslic, Frank Bigiel, Guillermo A. Blanc, Alberto D. Bolatto, Mederic Boquien, Yixian Cao, Rupali Chandar, Jeremy Chastenet, I-Da Chiang, Enrico Congiu, Daniel A. Dale, Sinan Deger, Jakob S. den Brok, Cosima Eibensteiner, Eric Emsellem, Axel Garcıa-Rodrıguez, Simon C. O. Glover, Kathryn Grasha, Brent Groves, Jonathan D. Henshaw, Maria J. Jimenez Donaire, Jenny J. Kim, Ralf S. Klessen, Kathryn Kreckel, J. M. Diederik Kruijssen, Kirsten L. Larson, Janice C. Lee, Ness Mayker, Rebecca McElroy, Sharon E. Meidt, Angus Mok, Hsi-An Pan, Johannes Puschnig, Alessandro Razza, Patricia Sanchez-Blazquez, Karin M. Sandstrom, Francesco Santoro, Amy Sardone, Fabian Scheuermann, Jiayi Sun, David A. Thilker, Jordan A. Turner, Leonardo Ubeda, Dyas Utomo, Elizabeth J. Watkins, Thomas G. Williams
We describe the processing of the PHANGS-ALMA survey and present the PHANGS-ALMA pipeline, a public software package that processes calibrated interferometric and total power data into science-ready data products. PHANGS-ALMA is a large, high-resolution survey of CO J=2-1 emission from nearby galaxies. The observations combine ALMA's main 12-m array, the 7-m array, and total power observations and use mosaics of dozens to hundreds of individual pointings. We describe the processing of the u-v data, imaging and deconvolution, linear mosaicking, combining interferometer and total power data, noise estimation, masking, data product creation, and quality assurance. Our pipeline has a general design and can also be applied to VLA and ALMA observations of other spectral lines and continuum emission. We highlight our recipe for deconvolution of complex spectral line observations, which combines multiscale clean, single scale clean, and automatic mask generation in a way that appears robust and effective. We also emphasize our two-track approach to masking and data product creation. We construct one set of "broadly masked" data products, which have high completeness but significant contamination by noise, and another set of "strictly masked" data products, which have high confidence but exclude faint, low signal-to-noise emission. Our quality assurance tests, supported by simulations, demonstrate that 12-m+7-m deconvolved data recover a total flux that is significantly closer to the total power flux than the 7-m deconvolved data alone. In the appendices, we measure the stability of the ALMA total power calibration in PHANGS--ALMA and test the performance of popular short-spacing correction algorithms.
Complex Organic Molecules in Protostars with ALMA Spectral Surveys (COMPASS) II. Approach to data reduction and products for line-rich broadband (sub)millimeter spectra
本文介绍 COMPASS 项目的数据归约流程与产品，使用 CASA 和 IMAGER 处理 11 个原恒星区域的 ALMA 观测，生成光谱立方体、提取光谱和连续谱图像，以支持化学研究。
VAPOLA -- A multi-year, multi-band polarization survey of AGN and Sgr A* at mm wavelengths with ALMA I. Survey Overview and Science-Ready Archival Products
Università degli Studi di Cagliari（卡利亚里大学）；INAF - Osservatorio Astronomico di Cagliari（意大利国家天体物理研究所卡利亚里天文台）；University of Balearic Islands（巴利阿里群岛大学）；Institute of the Balearic Islands (IAIB)（巴利阿里群岛研究所）；Health Research Institute of the Balearic Islands (IdISBa)（巴利阿里群岛健康研究所）；Universidade São Paulo, Instituto de Astronomia, Geofísica e Ciéencias Atmosféricas（圣保罗大学天文、地球物理与大气科学研究所）；INAF-Istituto di Radioastronomia（意大利国家天体物理研究所射电天文学研究所）；INAF–Italian Centre for Astronomical Archive（意大利天体物理研究所意大利天文档案中心）
A Classifier Framework for ALMA Bandpass Calibration Anomalies
National Radio Astronomy Observatory（国家射电天文台）；NSF–Simons AI Institute for Cosmic Origins（NSF-Simons宇宙起源人工智能研究所）；University of Utah（犹他大学）；Joint ALMA Observatory（ALMA联合观测站）
针对射电干涉观测中带通校准异常检测，提出基于 XGBoost 和扫描统计特征的分类器框架，在 ALMA 数据上显著降低误分类率，可扩展至下一代设施。
"Superluminal" FITS File Processing on Multiprocessors: Zero Time Endian Conversion Technique
FITS是天文学中的标准文件格式，它已被扩展以满足当今天文学的需求。然而，天文数据集逐年膨胀。在ALMA望远镜的情况下，可以为一个目标产生~ TB尺度的四维数据立方体。考虑到典型的互联网带宽最多只有几个10 MB/s，FITS格式的原始数据立方体托管在VO服务器上，并且用户感兴趣的区域应该被剪切并传输给用户（Eguchi et al.，2012年）。该系统将配备一个非常高速的磁盘阵列，在几个10秒内处理一个TB规模的数据立方体，磁盘I/O速度，字节序转换和数据处理之一将是可比的。因此，减少端序转换时间是实现本系统的问题之一。在本文中，我介绍了一种名为“即时字节序转换”的技术，它延迟了字节序转换每个像素之前，它是真正需要的，扫出字节序转换时间;通过应用这种方法，FITS处理速度增加20%的单线程，和40%的多线程CFITSIO相比。该方法的加速与现代CPU体系结构紧密相关，由于“因果关系”，即程序指令代码序列的中断，提高了指令流水线的效率。
