کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1823962 | 1027320 | 2012 | 4 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Signal classification for acoustic neutrino detection
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
فیزیک و نجوم
ابزار دقیق
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
This article focuses on signal classification for deep-sea acoustic neutrino detection. In the deep sea, the background of transient signals is very diverse. Approaches like matched filtering are not sufficient to distinguish between neutrino-like signals and other transient signals with similar signature, which are forming the acoustic background for neutrino detection in the deep-sea environment. A classification system based on machine learning algorithms is analysed with the goal to find a robust and effective way to perform this task. For a well-trained model, a testing error on the level of 1% is achieved for strong classifiers like Random Forest and Boosting Trees using the extracted features of the signal as input and utilising dense clusters of sensors instead of single sensors.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment - Volume 662, Supplement 1, 11 January 2012, Pages S242-S245
Journal: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment - Volume 662, Supplement 1, 11 January 2012, Pages S242-S245
نویسندگان
M. Neff, G. Anton, A. Enzenhöfer, K. Graf, J. HöÃl, U. Katz, R. Lahmann, C. Richardt,