Searching for air showers with RNO-G
J. Henrichs*,
J.A. Aguilar,
P. Allison,
D.Z. Besson,
A. Bishop,
O. Botner,
S. Bouma, S. Buitink, M. Cataldo, B.A. Clark, K. Couberly, Z. Curtis-Ginsberg, P. Dasgupta, S. De Kockere, K.D. de Vries, C. Deaconu, M.A. DuVernois, A. Eimer, C. Glaser, A. Hallgren, S. Hallmann, J.C. Hanson, B.L. Hendricks, N. Heyer, C. Hornhuber, K. Hughes, T. Karg, A. Karle, J.L. Kelley, M. Korntheuer, M. Kowalski, I. Kravchenko, R. Krebs, R. Lahmann, U.A. Latif, J. Mammo, M.J. Marsee, Z.S. Meyers, K. Michaels, K. Mulrey, M.S. Muzio, A. Nelles, A. Novikov, A. Nozdrina, E. Oberla, B. Oeyen, I. Plaisier, N. Punsuebsay, L. Pyras, D. Ryckbosch, O. Scholten, D. Seckel, M.F.H. Seikh, D. Smith, J. Stoffels, D. Southall, K. Terveer, S. Toscano, D. Tosi, D.J. Van Den Broeck, N. van Eijndhoven, A.G. Vieregg, J.Z. Vischer, C. Welling, D.R. Williams, S. Wissel, R. Young and A. Zinket al. (click to show)*: corresponding author
Pre-published on:
June 12, 2023
Published on:
October 25, 2023
Abstract
The Radio Neutrino Observatory – Greenland (RNO-G) is an in-ice neutrino detector, using radio emission to target the first measurement of neutrinos beyond PeV energies. In total 35 stations are planned for the detector, resulting in a detection volume of around 100 $\mathrm{km}^3$. Each of these stations is equipped with deep antennas embedded $\sim$ 100 m into the ice and downward-pointing log-periodic dipole antennas (LPDA) buried $\sim$ 3 m into the snow. At each station, three additional buried LPDA are pointing towards the sky and thus can be used to look for cosmic-ray induced air-showers. These air showers are a background for the RNO-G detector and therefore important to understand, but they also can be used as a calibration tool. In order to find the air-shower signals, we apply an analysis based on template matching to the data.
We present the current status of the analysis targeting the detection of cosmic-rays induced air showers. This includes the presentation of a method to create a complete template set and a first look at RNO-G data.
DOI: https://doi.org/10.22323/1.424.0007
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