Speaker
Description
The detection of ultra-high energy neutrinos (UHE-$\nu$, $E_\nu \gtrsim 10$ PeV) via radio-frequency (RF) instrumentation in polar ice relies on precise modeling of the Askaryan effect. While existing analytic descriptions successfully capture the intrinsic electromagnetic field generated by charge-excess development in particle cascades, propagation through polar ice and detector response are typically treated numerically. We present a fully analytic framework that extends classical Askaryan field models to include signal propagation effects and RF channel response, yielding closed-form expressions for the observed voltage trace and its Hilbert envelope. We validate this model against 100 PeV UHE-$\nu$ simulations generated with NuRadioMC. The analytic envelope correlates strongly with Monte Carlo waveforms, achieving correlation coefficients exceeding 0.94, with 99.99% of simulated neutrino events surpassing a threshold of $\rho \geq 0.4$. Thermal noise studies demonstrate strong background rejection, with only 0.2 false correlations expected over 5 years at a 1 Hz trigger rate. These results indicate that analytic correlation metrics can provide powerful signal identification and background suppression in next-generation Askaryan detectors. We further apply the model to ultra-high energy cosmic ray (UHECR) events detected by Askaryan Radio Array. When UHECR-induced air showers penetrate RF-transparent Antarctic ice, the Askaryan component of the cascade can be observed in-ice. We model the Askaryan emission including detector response and compare to coherently summed waveforms from 13 UHECR candidates. Correlation coefficients between 0.69 and 0.86 are obtained with minimal fractional power discrepancies, confirming the Askaryan origin of the observed pulses. This unified analytic framework provides a fast, physically transparent alternative to full Monte Carlo templates, enabling efficient signal identification, event classification, and future inference of cascade energy through analytic scaling of waveform observables. Such tools are directly applicable to current and upcoming radio UHE-$\nu$ experiments and offer a pathway toward robust, low-latency event reconstruction in large-scale autonomous arrays.From these fits, we isolate and extract the Askaryan component of the measured signals.