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Title: Dominance of Bursty over Steady Heating of the 4–8 MK Coronal Plasma in a Solar Active Region: Quantification Using Maps of Minimum, Maximum, and Average Brightness
Abstract A challenge in characterizing active region (AR) coronal heating is in separating transient (bursty) loop heating from the diffuse background (steady) heating. We present a method of quantifying coronal heating’s bursty and steady components in ARs, applying it to Fe xviii (hot 94) emission of an AR observed by the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory. The maximum-, minimum-, and average-brightness values for each pixel, over a 24 hr period, yield a maximum-brightness map, a minimum-brightness map, and an average-brightness map of the AR. Running sets of such three maps come from repeating this process for each time step of running windows of 20, 16, 12, 8, 5, 3, 1, and 0.5 hr. From each running window’s set of three maps, we obtain the AR’s three corresponding luminosity light curves. We find (1) the time-averaged ratio of minimum-brightness-map luminosity to average-brightness-map luminosity increases as the time window decreases, and the time-averaged ratio of maximum-brightness-map luminosity to average-brightness-map luminosity decreases as the window decreases; (2) for the 24 hr window, the minimum-brightness map’s luminosity is 5% of the average-brightness map’s luminosity, indicating that at most 5% of the AR’s hot 94 luminosity is from heating that is steady for 24 hr; (3) this upper limit on the fraction of the hot 94 luminosity from steady heating increases to 33% for the 30 minute running window. This requires that the heating of the 4–8 MK plasma in this AR is mostly in bursts lasting less than 30 minutes: at most a third of the heating is steady for 30 minutes.  more » « less
Award ID(s):
1950831
NSF-PAR ID:
10429050
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
The Astrophysical Journal
Volume:
942
Issue:
1
ISSN:
0004-637X
Page Range / eLocation ID:
2
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9. 
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