James Marcus Hughes

James Marcus Hughes
Southwest Research Institute · Planetary Science Directorate

BA in Computer Science/Astronomy
Developing PUNCH data reduction pipeline

About

15
Publications
1,156
Reads
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58
Citations
Citations since 2017
14 Research Items
58 Citations
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2017201820192020202120222023051015202530
2017201820192020202120222023051015202530
Education
August 2014 - July 2018
Williams College
Field of study
  • Astronomy and Computer Science

Publications

Publications (15)
Preprint
Full-text available
Heliophysics image data largely relies on a forty-year-old ecosystem built on the venerable Flexible Image Transport System (FITS) data standard. While many in situ measurements use newer standards, they are difficult to integrate with multiple data streams required to develop global understanding. Additionally, most data users still engage with da...
Preprint
Full-text available
COMPLETE is a flagship mission concept combining broadband spectroscopic imaging and comprehensive magnetography from multiple viewpoints around the Sun to enable tomographic reconstruction of 3D coronal magnetic fields and associated dynamic plasma properties, which provide direct diagnostics of energy release. COMPLETE re-imagines the paradigm fo...
Preprint
Full-text available
The coronal magnetic field is the prime driver behind many as-yet unsolved mysteries: solar eruptions, coronal heating, and the solar wind, to name a few. It is, however, still poorly observed and understood. We highlight key questions related to magnetic energy storage, release, and transport in the solar corona, and their relationship to these im...
Article
Full-text available
We describe a rapid and direct method for regularizing, post facto, the point-spread function (PSF) of a telescope or other imaging instrument across its entire field of view (FOV). Imaging instruments in general blur point sources of light by local convolution with a PSF that varies slowly across the FOV, due to coma, spherical aberration, and sim...
Preprint
Full-text available
We describe a method for regularizing, post-facto, the point-spread function of a telescope or other imaging instrument, across its entire field of view. Imaging instruments in general blur point sources of light by local convolution with a point-spread function that varies slowly across the field of view, due to coma, spherical aberration, and sim...
Article
Full-text available
The four Solar Ultraviolet Imagers (SUVI) on board the Geostationary Operational Environmental Satellite (GOES)‐16 and GOES‐17 and the upcoming GOES‐T and GOES‐U weather satellites serve as National Oceanic and Atmospheric Administration's operational solar coronal imagers. These four identically designed solar Extreme UltraViolet instruments are s...
Article
Full-text available
The ‘middle corona’ is a critical transition between the highly disparate physical regimes of the lower and outer solar coronae. Nonetheless, it remains poorly understood due to the difficulty of observing this faint region (1.5–3 R☉). New observations from the Solar Ultraviolet Imager of a Geostationary Operational Environmental Satellite in Augus...
Preprint
Full-text available
The "middle corona" is a critical transition between the highly disparate physical regimes of the lower and outer solar corona. Nonetheless, it remains poorly understood due to the difficulty of observing this faint region (1.5-3 solar radii). New observations from the GOES Solar Ultraviolet Imager in August and September 2018 provide the first com...
Preprint
Full-text available
In order to utilize solar imagery for real-time feature identification and large-scale data science investigations of solar structures, we need maps of the Sun where phenomena, or themes, are labeled. Since solar imagers produce observations every few minutes, it is not feasible to label all images by hand. Here, we compare three machine learning a...
Preprint
Full-text available
In order to utilize solar imagery for real-time feature identification and large-scale data science investigations of solar structures, we need maps of the Sun where phenomena, or themes, are labeled. Since solar imagers produce observations every few minutes, it is not feasible to label all images by hand. Here, we compare three machine learning a...
Article
It is critical to understand the internal processes of galaxies, such as star formation, which occurs in the coldest, densest interstellar clouds. Unlike stars, these clouds are difficult to detect in visible light, but radio and infrared telescopes allow observations of the gas and dust particles they contain. In regions of the galaxy, ambient neu...

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