Auger Engineering Radio Array (AERA) is situated in the Argentinian Pampa Amarilla, a location far away from large human settlements. Nevertheless, a strong background of pulsed radio-frequency interference (RFI) exists on site, which not only makes radio self-triggering challenging but also poses a problem for an effcient and pure reconstruction of air-shower measurements. We present how our standard event reconstruction exploi s several strategies to identify and suppress pulsed noise, and quantify the effciency and purity of our algorithms. These strategies can be employed by any experiment taking radio data in the presence of pulsed RFI

Huege, Tim

Pierre Auger Collaboration

Welling, Christoph

arXiv

Welling, Christoph B.

KITopen

© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).EPJ Web of Conferences 216, 03007 (2019) https://doi.org/10.1051/epjconf/201921603007ARENA 2018Reconstruction of air-shower measurements with AERA in thepresence of pulsed radio-frequency interferenceTim Huege1,4,, Christoph B. Welling2 for the Pierre Auger Collaboration3,1Karlsruhe Institute of Technology, Institute for Nuclear Physics, Karlsruhe, Germany2RWTH Aachen University, Aachen, Germany3Observatorio Pierre Auger, Av. San Martín Norte 304, 5613 Malargüe, Argentina4also at: Vrije Universiteit Brussel, Brussels, BelgiumAbstract. The Auger Engineering Radio Array (AERA) is situated in the ArgentinianPampa Amarilla, a location far away from large human settlements. Nevertheless, astrong background of pulsed radio-frequency interference (RFI) exists on site, which notonly makes radio self-triggering challenging but also poses a problem for an efficientand pure reconstruction of air-shower measurements. We present how our standard eventreconstruction exploits several strategies to identify and suppress pulsed noise, and quan-tify the efficiency and purity of our algorithms. These strategies can be employed by anyexperiment taking radio data in the presence of pulsed RFI.1 IntroductionAERA consists of 153 radio-detector stations covering a total area of 17 km2 in the Pampa Amarillanear Malargüe, Argentina. Although situated in a rural area, a strong background of pulsed RFI ispresent at the site. Investigations of its origin were not entirely conclusive, but it is clear that the pulsesarise from multiple sources: power lines, faulty transformers in a nearby village, communicationtowers, oil rigs, and others. The average rate of background pulses above a signal-to-noise thresholdof 10 amounts to 15 kHz, and the pulse characteristics are generally similar to signals emitted byextensive air showers, making, in particular, the self-triggering on radio signals very challenging.While the exploitation of external triggering from other detector components of the Pierre AugerObservatory provides an efficient and pure trigger, the pulsed RFI also poses a challenge for successfulevent reconstruction. In the following, we first describe how we exploit the hybrid nature of ourmeasurements in the event reconstruction to mitigate the adverse effects of pulsed RFI. Afterwards,we describe our strategies to identify and reject pulsed RFI disturbing the measurements in individualAERA antenna stations, which improves both the efficiency and purity of our reconstruction.2 Hybrid reconstruction strategyThe Pierre Auger Observatory is a hybrid detector measuring individual air showers with complemen-tary detection techniques [1]. The AERA events triggered by the Auger surface detector (SD) alsoe-mail: tim.huege@kit.eduFull author list at http://www.auger.org/archive/authors_2018_06.html2EPJ Web of Conferences 216, 03007 (2019) https://doi.org/10.1051/epjconf/201921603007ARENA 20180.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0false rejection rate [%]020406080100correct rejection rate [%] Fe p Fep< 60combinedpulse shapepolarizationstation clusteringstation timing> 60combinedpulse shapepolarizationstation clusteringstation timingFigure 1. Performance of the individual rejection strategies for air showers with zenith angles below (filledsymbols) and above 60◦ (open symbols). The black points illustrate the performance of all strategies combinedin the same reconstruction.have an associated SD measurement. Reconstruction of the SD data provides the event geometry, i.e.,the orientation and impact point of the air-shower axis. Using this information early on in the radioreconstruction, we calculate for each AERA antenna station the expected time of arrival of the radiopulse. Uncertainties in the SD-reconstructed event geometry are propagated to set the width of the“signal-search window”, which typically amounts to ∼ 1000 ns.This strategy is vastly superior to searching for radio pulses in the whole recorded radio trace oflength 10 µs, which for 15 kHz of RFI pulses has a probability of 15% to harbor an RFI pulse in eachindividual antenna station. As typically more than 100 antenna stations are read out on an SD trigger,this on average would yield ∼ 15 stations with RFI pulses, while cosmic-ray pulses are typically onlypresent in 3-5 AERA stations [2].3 Station-level suppression of RFI pulsesWith a signal search window of ∼ 1000 ns, the chance probability of picking up an RFI pulse shrinksto ∼ 1.5%. For more than 100 stations read out, typically one or two RFI pulses remain in the dataused for reconstruction. These pulses deteriorate the reconstruction efficiency and purity. We havethus developed several strategies to identify and suppress these RFI pulses.The strategies have been evaluated by performing a standard event reconstruction on CoREASsimulations [3] which have undergone a complete detector simulation [4] and have been added tomeasured noise. An RFI pulse correctly identified as RFI is denoted a “correct rejection” in thefollowing. Correspondingly, a “false rejection” characterizes a cosmic-ray pulse identified as RFI.For each strategy discussed in the following, we evaluate the fraction of correct versus false rejectionsindividually before combining all algorithms and evaluating the overall performance.Pulse shape: Radio pulses from air showers measured in the 30 to 80 MHz band are typicallybandwidth-limited with a characteristic width and shape, oscillating with a frequency of 55 MHz. We3EPJ Web of Conferences 216, 03007 (2019) https://doi.org/10.1051/epjconf/201921603007ARENA 20180.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0false rejection rate [%]020406080100correct rejection rate [%] Fe p Fep< 60combinedpulse shapepolarizationstation clusteringstation timing> 60combinedpulse shapepolarizationstation clusteringstation timingFigure 1. Performance of the individual rejection strategies for air showers with zenith angles below (filledsymbols) and above 60◦ (open symbols). The black points illustrate the performance of all strategies combinedin the same reconstruction.have an associated SD measurement. Reconstruction of the SD data provides the event geometry, i.e.,the orientation and impact point of the air-shower axis. Using this information early on in the radioreconstruction, we calculate for each AERA antenna station the expected time of arrival of the radiopulse. Uncertainties in the SD-reconstructed event geometry are propagated to set the width of the“signal-search window”, which typically amounts to ∼ 1000 ns.This strategy is vastly superior to searching for radio pulses in the whole recorded radio trace oflength 10 µs, which for 15 kHz of RFI pulses has a probability of 15% to harbor an RFI pulse in eachindividual antenna station. As typically more than 100 antenna stations are read out on an SD trigger,this on average would yield ∼ 15 stations with RFI pulses, while cosmic-ray pulses are typically onlypresent in 3-5 AERA stations [2].3 Station-level suppression of RFI pulsesWith a signal search window of ∼ 1000 ns, the chance probability of picking up an RFI pulse shrinksto ∼ 1.5%. For more than 100 stations read out, typically one or two RFI pulses remain in the dataused for reconstruction. These pulses deteriorate the reconstruction efficiency and purity. We havethus developed several strategies to identify and suppress these RFI pulses.The strategies have been evaluated by performing a standard event reconstruction on CoREASsimulations [3] which have undergone a complete detector simulation [4] and have been added tomeasured noise. An RFI pulse correctly identified as RFI is denoted a “correct rejection” in thefollowing. Correspondingly, a “false rejection” characterizes a cosmic-ray pulse identified as RFI.For each strategy discussed in the following, we evaluate the fraction of correct versus false rejectionsindividually before combining all algorithms and evaluating the overall performance.Pulse shape: Radio pulses from air showers measured in the 30 to 80 MHz band are typicallybandwidth-limited with a characteristic width and shape, oscillating with a frequency of 55 MHz. Wedefine two thresholds: a fraction a of the peak amplitude in the signal-search window, and a multiple bof the trace RMS in a noise window before the arrival of the pulse. The algorithm counts the upwardcrossings N of these two thresholds in a given time window ∆t centered on the pulse. Pulses withmore crossings than a threshold Nmax are too long and thus rejected as RFI. After optimization of theparameters a, b,∆t and Nmax, the performance is shown in the red triangles in Fig. (1).Signal polarization: Radio pulses from air showers have specific polarization characteristicsarising from the superposition of geomagnetic and charge-excess radiation (see, e.g., [5]). Usingthe event geometry provided by the SD reconstruction (including its uncertainties), the actual pulsepolarization at each antenna station is compared with the expected polarization. If the deviation islarger than a defined threshold, the pulse is considered RFI and is rejected. The performance isdemonstrated by the yellow squares in Fig. (1).Station clustering: The radio-emission footprint covers a contiguous area. Consequently, isolatedantenna stations with a detected pulse have likely measured RFI and are rejected from the reconstruc-tion. The performance of this algorithm is illustrated by the green triangles in Fig. (1).Station timing: The arrival times of radio pulses from an air shower form a wavefront. Weperform a bottom-up reconstruction which first fits the radio-pulse arrival times of the three antennastations nearest the SD core with a plane wave and then subsequently adds antenna stations further outto the fit. If adding a particular antenna station decreases the χ2 probability of the fit below 5%, thatstation is rejected – its pulse arrival time does not match that of the other pulses. The performance isillustrated by the blue diamonds in Fig. (1).Overall performance: We now combine all of these strategies in the reconstruction and applythem to a data set of simulations with both proton and iron primaries. The overall performance isshown in the black points in Fig. (1). Approximately 90% of the RFI pulses are rejected while only∼ 1.5% of the radio pulses from vertical and ∼ 2.5% of the radio pulses from inclined air showersare rejected. There are only small differences in the performance for proton- and iron-induced airshowers, i.e., the RFI-rejection algorithms do not introduce a significant selection bias.Validation with measured data: We have validated the performance of the combination of thesealgorithms also with measured data. For each measured air shower, we determine the probability forsuccessful reconstruction using the SD-reconstructed geometry and energy as input for an analyticalmodel [6]. We find that for events for which no successful detection is expected, the fraction ofreconstructed versus triggered events drops by a factor of ∼ 6 when applying the RFI rejection. Thisillustrates that in particular the purity of our reconstruction is significantly increased.4 ConclusionsWe have presented howwe achieve efficient and pure reconstruction of radio data taken in the presenceof strong pulsed RFI by exploiting hybrid measurements. The strategies employed here can also be ofuse to other radio-detection projects measuring in noisy environments.References[1] A. Aab et al. (Pierre Auger Collaboration), Nucl. Instrum. Meth. A 798, 172 (2015)[2] A. Aab et al. (Pierre Auger Collaboration), Phys. Rev. D 93, 122005 (2016)[3] T. Huege, M. Ludwig, C.W. James, AIP Conf. Proc. pp. 128–132 (2013)[4] P. Abreu et al. (Pierre Auger Collaboration), Nucl. Instr. Meth. A 635, 92 (2011)[5] T. Huege, Physics Reports 620, 1 (2016)[6] C. Glaser, S. de Jong, M. Erdmann, J.R. Hörandel, Astropart. Phys., arXiv:1806.03620

Reconstruction of air-shower measurements with AERA in the presence of pulsed radio-frequency interference

Tim Huege

Christoph B. Welling

EDP Sciences OAI-PMH repository (1.2.0)

Publikationsserver der RWTH Aachen University

https://publikationen.bibliothek.kit.edu/1000098534/41882868

Reconstruction of air-shower measurements with AERA in the presence of pulsed radio-frequency interference

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