Nanosecond-level time synchronization of autonomous radio detector stations for extensive air showers

To exploit the full potential of radio measurements of cosmic-ray air showers at MHz frequencies, a detector timing synchronization within 1 ns is needed. Large distributed radio detector arrays such as the Auger Engineering Radio Array (AERA) rely on timing via the Global Positioning System (GPS) for the synchronization of individual detector station clocks. Unfortunately, GPS timing is expected to have an accuracy no better than about 5 ns. In practice, in particular in AERA, the GPS clocks exhibit drifts on the order of tens of ns. We developed a technique to correct for the GPS drifts, and an independent method is used to cross-check that indeed we reach a nanosecond-scale timing accuracy by this correction. First, we operate a "beacon transmitter" which emits defined sine waves detected by AERA antennas recorded within the physics data. The relative phasing of these sine waves can be used to correct for GPS clock drifts. In addition to this, we observe radio pulses emitted by commercial airplanes, the position of which we determine in real time from Automatic Dependent Surveillance Broadcasts intercepted with a software-defined radio. From the known source location and the measured arrival times of the pulses we determine relative timing offsets between radio detector stations. We demonstrate with a combined analysis that the two methods give a consistent timing calibration with an accuracy of 2 ns or better. Consequently, the beacon method alone can be used in the future to continuously determine and correct for GPS clock drifts in each individual event measured by AERA.

Synchronization of Heteroclinic Circuits Through Learning in Chains of Neural Motifs

The synchronization of oscillatory activity in networks of neural networks is usually implemented through coupling the state variables describing neuronal dynamics. In this study we discuss another but complementary mechanism based on a learning process with memory. A driver network motif, acting as a teacher, exhibits winner-less competition (WLC) dynamics, while a driven motif, a learner, tunes its internal couplings according to the oscillations observed in the teacher. We show that under appropriate training the learner motif can dynamically copy the coupling pattern of the teacher and thus synchronize oscillations with the teacher. Then, we demonstrate that the replication of the WLC dynamics occurs for intermediate memory lengths only. In a unidirectional chain of N motifs coupled through teacher-learner paradigm the time interval required for pattern replication grows linearly with the chain size, hence the learning process does not blow up and at the end we observe phase synchronized oscillations along the chain. We also show that in a learning chain closed into a ring the network motifs come to a consensus, i.e. to a state with the same connectivity pattern corresponding to the mean initial pattern averaged over all network motifs.