相关论文: Chimenea and other tools: Automated imaging of mul…

Producing images from interferometer data requires accurate modeling of the sources in the field of view, which is typically done using the CLEAN algorithm. Given the large number of degrees of freedom in interferometeric images, one…

The Combined Array for Millimeter-wave Astronomy (CARMA) data reduction pipeline (CADRE) has been developed to give investigators a first look at a fully reduced set of their data. It runs automatically on all data produced by the telescope…

We present the results of our investigations into options for the computing platform for the imaging pipeline in the CHILES project, an ultra-deep HI pathfinder for the era of the Square Kilometre Array. CHILES pushes the current computing…

We present the first fully automated pipeline for making images from the interferometric data obtained from the upgraded Giant Metrewave Radio Telescope (uGMRT) called CAsa Pipeline-cum-Toolkit for Upgraded Giant Metrewave Radio Telescope…

Currently, HOPS and AIPS are the primary choices for the time-consuming process of (millimeter) Very Long Baseline Interferometry (VLBI) data calibration. However, for a full end-to-end pipeline, they either lack the ability to perform…

Modelling complex line emission in the interstellar medium (ISM) is a degenerate, high-dimensional problem. Here, we present McFine, a tool for automated multi-component fitting of emission lines with complex hyperfine structure, in a fully…

Written in Python and utilising ParselTongue to interface with the Astronomical Image Processing System (AIPS), the e-MERLIN data reduction pipeline is intended to automate the procedures required in processing and calibrating radio…

(Abridged) The CASA software suite, can now reduce very long baseline interferometry (VLBI) data with the recent addition of a fringe fitter. Here, we present the Radboud PIpeline for the Calibration of high Angular Resolution Data…

New and upgraded radio interferometers produce data at massive rates and will require significant improvements in analysis techniques to reach their promised levels of performance in a routine manner. Until these techniques are fully…

CASA, the Common Astronomy Software Applications package, is the primary data processing software for the Atacama Large Millimeter/submillimeter Array (ALMA) and NSF's Karl G. Jansky Very Large Array (VLA), and is frequently used also for…

High precision time series photometry from space is being used for a number of scientific cases. In this context, the recently launched CHEOPS (ESA) mission promises to bring 20 ppm precision over an exposure time of 6 hours, when targeting…

The Gaia mission will have a profound impact on our understanding of the structure and dynamics of the Milky Way. Gaia is providing an exhaustive census of stellar parallaxes, proper motions, positions, colors and radial velocities, but…

CASA, the Common Astronomy Software Applications, is the primary data processing software for the Atacama Large Millimeter/submillimeter Array (ALMA) and the Karl G. Jansky Very Large Array (VLA), and is often used also for other radio…

The Herschel Common Science System (HCSS) (Ott et al. 2006) (Ott & Science Ground Segment Consortium 2010) is a substantial Java software package, accompanying the development of the Herschel Mission (Pilbratt et al. 2010), supporting all…

CASA, the Common Astronomy Software Applications, is the primary data processing software for the Atacama Large Millimeter/submillimeter Array (ALMA) and the Karl G. Jansky Very Large Array (VLA), and is frequently used also for other radio…

The data produced by the future space-based millihertz gravitational-wave detector LISA will require nontrivial pre-processing, which might affect the science results. It is crucial to demonstrate the feasibility of such processing…

We aim to provide a suite of publicly available spectral data reduction software to facilitate rapid scientific outcomes from time-domain observations. For time resolved observations, an automated pipeline frees astronomers from performance…

Background and Objective: Deep learning enables tremendous progress in medical image analysis. One driving force of this progress are open-source frameworks like TensorFlow and PyTorch. However, these frameworks rarely address issues…

We present zea (pronounced ze-yah), a Python package for cognitive ultrasound imaging that offers a flexible, modular, and differentiable pipeline for ultrasound data processing. Additionally, it includes a collection of pre-defined models…

Predictive simulations and experimental design involving extreme aero-chemo-thermo-mechanical regimes require high-fidelity material representation across diverse physical states. However, data for metals, polymers, and propellants,…