| 1. |
- Arendt, Richard G., et al.
(författare)
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JWST NIRCam Observations of SN 1987A : Spitzer Comparison and Spectral Decomposition
- 2023
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Ingår i: Astrophysical Journal. - : American Astronomical Society. - 0004-637X .- 1538-4357. ; 959:2
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Tidskriftsartikel (refereegranskat)abstract
- JWST Near Infrared Camera (NIRCam) observations at 1.5–4.5 μm have provided broadband and narrowband imaging of the evolving remnant of SN 1987A with unparalleled sensitivity and spatial resolution. Comparing with previous marginally spatially resolved Spitzer Infrared Array Camera (IRAC) observations from 2004 to 2019 confirms that the emission arises from the circumstellar equatorial ring (ER), and the current brightness at 3.6 and 4.5 μm was accurately predicted by extrapolation of the declining brightness tracked by IRAC. Despite the regular light curve, the NIRCam observations clearly reveal that much of this emission is from a newly developing outer portion of the ER. Spots in the outer ER tend to lie at position angles in between the well-known ER hotspots. We show that the bulk of the emission in the field can be represented by five standard spectral energy distributions, each with a distinct origin and spatial distribution. This spectral decomposition provides a powerful technique for distinguishing overlapping emission from the circumstellar medium and the supernova ejecta, excited by the forward and reverse shocks, respectively.
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| 2. |
- Sánchez-Cano, Beatriz, et al.
(författare)
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Solar Energetic Particle Events Detected in the Housekeeping Data of the European Space Agency's Spacecraft Flotilla in the Solar System
- 2023
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Ingår i: Space Weather. - : American Geophysical Union (AGU). - 1542-7390. ; 21:8
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Tidskriftsartikel (refereegranskat)abstract
- Despite the growing importance of planetary Space Weather forecasting and radiation protection for science and robotic exploration and the need for accurate Space Weather monitoring and predictions, only a limited number of spacecraft have dedicated instrumentation for this purpose. However, every spacecraft (planetary or astronomical) has hundreds of housekeeping sensors distributed across the spacecraft, some of which can be useful to detect radiation hazards produced by solar particle events. In particular, energetic particles that impact detectors and subsystems on a spacecraft can be identified by certain housekeeping sensors, such as the Error Detection and Correction (EDAC) memory counters, and their effects can be assessed. These counters typically have a sudden large increase in a short time in their error counts that generally match the arrival of energetic particles to the spacecraft. We investigate these engineering datasets for scientific purposes and perform a feasibility study of solar energetic particle event detections using EDAC counters from seven European Space Agency Solar System missions: Venus Express, Mars Express, ExoMars-Trace Gas Orbiter, Rosetta, BepiColombo, Solar Orbiter, and Gaia. Six cases studies, in which the same event was observed by different missions at different locations in the inner Solar System are analyzed. The results of this study show how engineering sensors, for example, EDAC counters, can be used to infer information about the solar particle environment at each spacecraft location. Therefore, we demonstrate the potential of the various EDAC to provide a network of solar particle detections at locations where no scientific observations of this kind are available.
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| 3. |
- Tabassian, Mahdi, et al.
(författare)
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Machine learning of the spatio-temporal characteristics of echocardiographic deformation curves for infarct classification
- 2017
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Ingår i: The International Journal of Cardiovascular Imaging. - : SPRINGER. - 1569-5794 .- 1875-8312 .- 1573-0743. ; 33:8, s. 1159-1167
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this study was to analyze the whole temporal profiles of the segmental deformation curves of the left ventricle (LV) and describe their interrelations to obtain more detailed information concerning global LV function in order to be able to identify abnormal changes in LV mechanics. The temporal characteristics of the segmental LV deformation curves were compactly described using an efficient decomposition into major patterns of variation through a statistical method, called Principal Component Analysis (PCA). In order to describe the spatial relations between the segmental traces, the PCA-derived temporal features of all LV segments were concatenated. The obtained set of features was then used to build an automatic classification system. The proposed methodology was applied to a group of 60 MRI-delayed enhancement confirmed infarct patients and 60 controls in order to detect myocardial infarction. An average classification accuracy of 87% with corresponding sensitivity and specificity rates of 89% and 85%, respectively was obtained by the proposed methodology applied on the strain rate curves. This classification performance was better than that obtained with the same methodology applied on the strain curves, reading of two expert cardiologists as well as comparative classification systems using only the spatial distribution of the end-systolic strain and peak-systolic strain rate values. This study shows the potential of machine learning in the field of cardiac deformation imaging where an efficient representation of the spatio-temporal characteristics of the segmental deformation curves allowed automatic classification of infarcted from control hearts with high accuracy.
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