Asset Details
Do you wish to reserve the book?
Classification and Recovery of Radio Signals from Cosmic Ray Induced Air Showers with Deep Learning
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Classification and Recovery of Radio Signals from Cosmic Ray Induced Air Showers with Deep Learning
Do you wish to request the book?
Classification and Recovery of Radio Signals from Cosmic Ray Induced Air Showers with Deep Learning
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Paper
Classification and Recovery of Radio Signals from Cosmic Ray Induced Air Showers with Deep Learning
2019
Overview
Radio emission from air showers enables measurements of cosmic particle kinematics and identity. The radio signals are detected in broadband Megahertz antennas among continuous background noise. We present two deep learning concepts and their performance when applied to simulated data. The first network classifies time traces as signal or background. We achieve a true positive rate of about 90% for signal-to-noise ratios larger than three with a false positive rate below 0.2%. The other network is used to clean the time trace from background and to recover the radio time trace originating from an air shower. Here we achieve a resolution in the energy contained in the trace of about 20% without a bias for \\(80\\%\\) of the traces with a signal. The obtained frequency spectrum is cleaned from signals of radio frequency interference and shows the expected shape.
Publisher
Cornell University Library, arXiv.org
