38,622 research outputs found
Detailed Decomposition of Galaxy Images. II. Beyond Axisymmetric Models
We present a two-dimensional (2-D) fitting algorithm (GALFIT, Version 3) with
new capabilities to study the structural components of galaxies and other
astronomical objects in digital images. Our technique improves on previous 2-D
fitting algorithms by allowing for irregular, curved, logarithmic and power-law
spirals, ring and truncated shapes in otherwise traditional parametric
functions like the Sersic, Moffat, King, Ferrer, etc., profiles. One can mix
and match these new shape features freely, with or without constraints, apply
them to an arbitrary number of model components and of numerous profile types,
so as to produce realistic-looking galaxy model images. Yet, despite the
potential for extreme complexity, the meaning of the key parameters like the
Sersic index, effective radius or luminosity remain intuitive and essentially
unchanged. The new features have an interesting potential for use to quantify
the degree of asymmetry of galaxies, to quantify low surface brightness tidal
features beneath and beyond luminous galaxies, to allow more realistic
decompositions of galaxy subcomponents in the presence of strong rings and
spiral arms, and to enable ways to gauge the uncertainties when decomposing
galaxy subcomponents. We illustrate these new features by way of several case
studies that display various levels of complexity.Comment: 41 pages, 22 figures, AJ accepted. Minor changes. Full resolution
version of this paper is available at:
http://users.obs.carnegiescience.edu/peng/work/galfit/galfit3.pd
HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting
Purpose: Magnetic resonance fingerprinting (MRF) methods typically rely on
dictio-nary matching to map the temporal MRF signals to quantitative tissue
parameters. Such approaches suffer from inherent discretization errors, as well
as high computational complexity as the dictionary size grows. To alleviate
these issues, we propose a HYbrid Deep magnetic ResonAnce fingerprinting
approach, referred to as HYDRA.
Methods: HYDRA involves two stages: a model-based signature restoration phase
and a learning-based parameter restoration phase. Signal restoration is
implemented using low-rank based de-aliasing techniques while parameter
restoration is performed using a deep nonlocal residual convolutional neural
network. The designed network is trained on synthesized MRF data simulated with
the Bloch equations and fast imaging with steady state precession (FISP)
sequences. In test mode, it takes a temporal MRF signal as input and produces
the corresponding tissue parameters.
Results: We validated our approach on both synthetic data and anatomical data
generated from a healthy subject. The results demonstrate that, in contrast to
conventional dictionary-matching based MRF techniques, our approach
significantly improves inference speed by eliminating the time-consuming
dictionary matching operation, and alleviates discretization errors by
outputting continuous-valued parameters. We further avoid the need to store a
large dictionary, thus reducing memory requirements.
Conclusions: Our approach demonstrates advantages in terms of inference
speed, accuracy and storage requirements over competing MRF method
Space exploration: The interstellar goal and Titan demonstration
Automated interstellar space exploration is reviewed. The Titan demonstration mission is discussed. Remote sensing and automated modeling are considered. Nuclear electric propulsion, main orbiting spacecraft, lander/rover, subsatellites, atmospheric probes, powered air vehicles, and a surface science network comprise mission component concepts. Machine, intelligence in space exploration is discussed
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