arXiv subjects

Simon Strähnz

Publications and source records attributed to Simon Strähnz.

4 recordsLinked to original sources

arXiv2026

A Bayesian Method for Air-Shower Reconstruction using Information Field Theory

The radio detection of extensive air showers provides a powerful method for studying the origin of high-energy cosmic rays. The Low-Frequency Array (LOFAR) offers unprecedentedly detailed measurements of the radio emission footprint. However, fully exploiting this information requires advanced reconstruction techniques. In this paper, we introduce a novel framework for air shower reconstruction based on Bayesian inference and Information Field Theory (IFT). Our method is built on a fully differentiable forward model of the radio signal, which incorporates a physical emission parameterization and a precise wavefront model. Additionally, we augment this physical model with Gaussian processes to account for systematic uncertainties in both the signal fluence and arrival timing. By leveraging gradient information, our approach enables efficient (three orders of magnitude acceleration w.r.t.\ the legacy method) and robust inference of the underlying physical shower parameters, such as primary energy and the depth of shower maximum, $X_\text{max}$. This work provides not only point estimates but also a rigorous quantification of uncertainties. We achieve a resolution in $X_\text{max}$ of $25\,\mathrm{g/cm^2}$ and a radiation energy resolution of $12\%$ on simulations for LOFAR.

astro-ph.HE↗

arXiv2025

Information Field Theory based Event Reconstruction for Cosmic Ray Radio Detectors

Detection of extensive air showers with radio antennas is an appealing technique in cosmic ray physics. However, because of the high level of measurement noise, current reconstruction methods still leave room for improvement. Furthermore, reconstruction efforts typically focus only on a single aspect of the signal, such as the energy fluence or arrival time. Bayesian inference is then a natural choice for a holistic approach to reconstruction, yet, this problem would be ill-posed, since the electric field is a continuous quantity. Information Field Theory provides the solution for this by providing a statistical framework to deal with discretised fields in the continuum limit. We are currently developing models for this novel approach to reconstructing extensive air showers. The model described here is based on the best current understanding of the emission mechanisms: It uses parametrisations of the lateral signal strength distribution, charge-excess contribution and spectral shape. Shower-to-shower fluctuations and narrowband RFI are modelled using Gaussian processes. Combined with a detailed detector description, this model can infer not only the electric field, but also the shower geometry, electromagnetic energy and position of shower maximum. Another big achievement of this approach is its ability to naturally provide uncertainties for the reconstruction, which has been shown to be difficult in more traditional methods. With such an open framework and robust computational methods based in Information Field Theory, it will also be easy to incorporate new insights and additional data, such as timing distributions or particle detector data, in the future. This approach has a high potential to exploit the full information content of a complex detector with rigorous statistical methods, in a way that directly includes domain knowledge.

astro-ph.IM↗

arXiv2025

Event reconstruction with the Radio detector of the Pierre Auger Observatory

The surface detector of the Pierre Auger Observatory has recently been upgraded with the addition of radio antennas, forming the radio detector (RD). This contribution outlines the standard methods for reconstructing extensive air showers using the RD, along with recent developments. The reconstruction pipeline is based on a robust understanding of the detector itself. The entire instrument, including the antenna pattern and analog chain, has been meticulously characterized within the Offline software framework, based on measurements in the laboratory as well as in the field. To ensure data integrity, stations identified as unreliable through monitoring are excluded before event reconstruction. Absolute calibration is achieved at the 5 percent level by analyzing the diffuse galactic radio emission. Next, the electric field that induced voltages in the antenna is calculated by unfolding the antenna response pattern. Key observables, such as the energy fluence (the energy deposited in the ground per unit area) and the arrival time of the pulse, are then determined. With these quantities, shower parameters can be reconstructed with very good accuracy in two chi-square-minimization fits: one to determine the shower's arrival direction via a spherical wavefront fit (predicted within 0.2 degree), and the other to estimate the distance to the shower maximum and the electromagnetic cascade energy (predicted within 5 percent) using a lateral density function

astro-ph.IM↗

arXiv2024

Electric Field Reconstruction with Information Field Theory

Reconstructing the electric field from the measured voltages in an antenna, unfolding the antenna response, comes with several problems. Due to the noisiness of the signal it is often necessary to disregard part of the bandwidth of the antenna. It is also not guaranteed, that this system of equations can be inverted at all. In any case, the noise of the measurement will be converted into noise on the electric field. This could be solved by Bayesian inference, however, the electric field is continuous, which would lead to an infinite-dimensional latent space. Information field theory (IFT) has been developed to deal with this problem and allow for Bayesian reasoning on fields. It provides a theoretical backbone and effective tools to approach the inference as a discrete problem in the continuum limit, taking the continuous nature of fields into account. We will present a first working signal model that can be used with IFT-based inference algorithms, which can successfully reconstruct the electric field. The model is based on the current understanding of air shower emission physics, modelling geomagnetic and charge-excess emission and their respective polarisation and spectra separately. Since Bayesian inference provides the posterior distribution, this method also gives an estimate on the uncertainty of the measured field. The performance of this method will be demonstrated with Monte-Carlo simulations of air shower radio signals.

physics.data-an↗