difference image analysis
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2021 ◽  
Vol 7 (2) ◽  
pp. 276-278
Author(s):  
Rongqing Chen ◽  
András Lovas ◽  
Balázs Benyó ◽  
Knut Moeller

Abstract COVID-19 induced acute respiratory distress syndrome (ARDS) could have two different phenotypes, which might have different response and outcome to the traditional ARDS positive end-expiration pressure (PEEP) treatment. The identification of the different phenotypes in terms of the PEEP recruitment can help improve the patients’ outcome. In this contribution we reported a COVID-19 patient with seven-day electrical impedance tomography monitoring. From the conductivity distribution difference image analysis of the data, a clear course developing trend can be observed in addition to the phenotype identification. This case might suggest that EIT can be a practical tool to identify phenotypes and to provide progressive information of COVID-19 pneumonia.


2020 ◽  
Vol 634 ◽  
pp. A132
Author(s):  
David Modiano ◽  
Aastha S. Parikh ◽  
Rudy Wijnands

We investigate near-ultraviolet variability in the Galactic globular cluster (GC) 47 Tucanae (47 Tuc). This work was undertaken within the GC sub-project of the Transient UV Objects project, a programme which aims to find and study transient and strongly variable UV sources. Globular clusters are ideal targets for transient searches because of their high stellar densities and large populations of variable systems. Using all 75 archival observations of 47 Tuc obtained with the UV/optical telescope (UVOT) aboard the Neil Gehrels Swift observatory with the uvm2 filter, we searched for UV variability using a specialised pipeline which utilises difference image analysis. We found four clear transients, hereafter SW1–4, with positions consistent with those of known cataclysmic variables (CVs) or CV candidates identified previously using Hubble Space Telescope observations. All four sources exhibit significant outbursts, likely brightening by several orders of magnitude. Based on the inferred outburst properties and the association with known CVs, we tentatively identify the UV transients as CV-dwarf novae (DNe). Two DNe have been previously observed in 47 Tuc: V2, which has a position consistent with that of SW4; and AKO 9, which was not in outburst during any of the UVOT observations. We thus increase the known number of DNe in 47 Tuc to 5 and the total number of detected DNe in all Galactic GCs combined from 14 to 17. We discuss our results in the context of the apparent scarcity of DNe in GCs. We suggest that the likely cause is observational biases, such as limited sensitivity due to the high background from unresolved stars in the GC and limited angular resolution of the telescopes used. We additionally detected one strongly variable source in 47 Tuc, which could be identified as the known RR Lyrae star HV 810. We found its period to have significantly increased with respect to that measured from data taken in 1988.


2019 ◽  
Vol 28 ◽  
pp. 100284 ◽  
Author(s):  
B. Sánchez ◽  
M.J. Domínguez R. ◽  
M. Lares ◽  
M. Beroiz ◽  
J.B. Cabral ◽  
...  

2016 ◽  
Vol 457 (1) ◽  
pp. 542-574 ◽  
Author(s):  
D. M. Bramich ◽  
Keith Horne ◽  
K. A. Alsubai ◽  
E. Bachelet ◽  
D. Mislis ◽  
...  

2015 ◽  
Vol 577 ◽  
pp. A108 ◽  
Author(s):  
D. M. Bramich ◽  
E. Bachelet ◽  
K. A. Alsubai ◽  
D. Mislis ◽  
N. Parley

2015 ◽  
Vol 149 (2) ◽  
pp. 50 ◽  
Author(s):  
Ryan J. Oelkers ◽  
Lucas M. Macri ◽  
Lifan Wang ◽  
Michael C. B. Ashley ◽  
Xiangqun Cui ◽  
...  

2012 ◽  
Vol 428 (3) ◽  
pp. 2275-2289 ◽  
Author(s):  
D. M. Bramich ◽  
K. Horne ◽  
M. D. Albrow ◽  
Y. Tsapras ◽  
C. Snodgrass ◽  
...  

2011 ◽  
Vol 35 (1-2) ◽  
pp. 319-327
Author(s):  
Fedor I. Getman ◽  
Silvio Leccia ◽  
Aniello Grado ◽  
Roberto Silvotti

2011 ◽  
Vol 7 (S285) ◽  
pp. 291-293 ◽  
Author(s):  
Seo-Won Chang ◽  
Yong-Ik Byun ◽  
Dae-Won Kim

AbstractWe present a new photometric reduction method for precise time-series photometry of non-crowded fields that does not need to involve relatively complicated and CPU intensive techniques such as point-spread-function (PSF) fitting or difference image analysis. This method, which combines multi-aperture index photometry and a spatio-temporal de-trending algorithm, gives much superior performance in data recovery and light-curve precision. In practice, the brutal filtering that is often applied to remove outlying data points can result in the loss of vital data, with seriously negative impacts on short-term variations such as flares. Our method utilizes nearly 100% of available data and reduces the rms scatter to several times smaller than that for archived light curves for brighter stars. We outline the details of our new method, and apply it to cases of sample data from the MMT survey of the M37 field, and the HAT-South survey.


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