scholarly journals Orbital Differential Imaging: a new high-contrast post-processing technique for direct imaging of exoplanets

Author(s):  
Jared R. Males ◽  
Ruslan Belikov ◽  
Eduardo Bendek
2016 ◽  
Vol 139 ◽  
pp. 120-129 ◽  
Author(s):  
Sumedh M. Joshi ◽  
Peter J. Diamessis ◽  
Derek T. Steinmoeller ◽  
Marek Stastna ◽  
Greg N. Thomsen

2015 ◽  
Vol 8 (3-4) ◽  
pp. 124-129 ◽  
Author(s):  
Chase M. Pfeifer ◽  
Judith M. Burnfield ◽  
Guilherme M. Cesar ◽  
Max H. Twedt ◽  
Jeff A. Hawks

2018 ◽  
Vol 56 (1) ◽  
pp. 315-355 ◽  
Author(s):  
Olivier Guyon

Over the last two decades, several thousand exoplanets have been identified, and their study has become a high scientific priority. Direct imaging of nearby exoplanets and the circumstellar disks in which they form and evolve is challenging due to the high contrast ratio and small angular separation relative to the central star. Exoplanets are typically within 1 arcsec of, and between 4 and 10 orders of magnitude fainter than, the stars they orbit. To meet these challenges, ground-based telescopes must be equipped with extreme adaptive optics (ExAO) systems optimized to acquire high-contrast images of the immediate surrounding of nearby bright stars. Current ExAO systems have the sensitivity to image thermal emission from young massive planets in near-IR, while future systems deployed on Giant Segmented Mirror Telescopes will image starlight reflected by lower-mass rocky planets. Thanks to rapid progress in optical coronagraphy, wavefront control, and data analysis techniques, direct imaging and spectroscopic characterization of habitable exoplanets will be within reach of the next generation of large ground-based telescopes.


2020 ◽  
Author(s):  
Poomipat Boonyakitanont ◽  
Apiwat Lek-uthai ◽  
Jitkomut Songsiri

AbstractThis article aims to design an automatic detection algorithm of epileptic seizure onsets and offsets in scalp EEGs. A proposed scheme consists of two sequential steps: the detection of seizure episodes, and the determination of seizure onsets and offsets in long EEG recordings. We introduce a neural network-based model called ScoreNet as a post-processing technique to determine the seizure onsets and offsets in EEGs. A cost function called a log-dice loss that has an analogous meaning to F1 is proposed to handle an imbalanced data problem. In combination with several classifiers including random forest, CNN, and logistic regression, the ScoreNet is then verified on the CHB-MIT Scalp EEG database. As a result, in seizure detection, the ScoreNet can significantly improve F1 to 70.15% and can considerably reduce false positive rate per hour to 0.05 on average. In addition, we propose detection delay metric, an effective latency index as a summation of the exponential of delays, that includes undetected events into account. The index can provide a better insight into onset and offset detection than conventional time-based metrics.


Author(s):  
M. Houllé ◽  
A. Vigan ◽  
A. Carlotti ◽  
É. Choquet ◽  
F. Cantalloube ◽  
...  

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