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Xishui Tian

Publications and source records attributed to Xishui Tian.

9 recordsLinked to original sources

Noise Suppression and Radio Frequency Interference Rejection for Self-Triggered Radio Detectors of Extensive Air Showers

Self-triggered radio detection of ultra-high-energy cosmic rays and neutrinos offers a scalable and cost-effective approach for next-generation astroparticle observatories, but remains challenging under realistic radio-frequency interference (RFI) conditions. In the classical air-shower radio band, the achievable sensitivity and trigger reliability are critically limited by the balance between external sky background noise and internal detector-unit noise, as well as by non-stationary anthropogenic interference. In this work, we present an end-to-end design and experimental characterization of a self-triggered radio detector unit explicitly optimized to operate in a galactic-noise-dominated regime. Rather than focusing on a single hardware component or trigger algorithm, we adopt a system-level methodology that coherently integrates sky-noise modeling, RF-chain noise budgeting, electromagnetic compatibility (EMC) mitigation, and measurement-driven validation. By using the galactic radio background as a quantitative reference, we assess the internal noise performance of the detector unit and demonstrate conditions under which extensive air shower (EAS) radio signals can be distinguished from anthropogenic interference at the system-response level. We further introduce an indirect noise-quantification method to estimate the low-noise amplifier contribution within the complete RF chain based on differential internal-noise measurements evaluated at the ADC level. The proposed detector unit is validated through laboratory and on-site measurements, demonstrating operation close to the galactic-noise limit in the core frequency band. These results provide a practical and transferable methodology for the design and deployment of large-scale self-triggered radio arrays such as GRAND.

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Towards the Giant Radio Array for Neutrino Detection (GRAND): the GRANDProto300 and GRAND@Auger prototypes

The Giant Radio Array for Neutrino Detection (GRAND) is a proposed multi-messenger observatory of Ultra-High-Energy (UHE) particles of cosmic origin. Its main goal is to find the long-sought origin of UHE cosmic rays by detecting large numbers of them and the secondary particles created by their interactions like gamma rays and neutrinos. The GRAND Collaboration plans to achieve this using large arrays of radio antennas that look for the radio signals emitted by the air showers initiated by the interactions of the UHE particles in the atmosphere. Since 2023, three small-scale prototype GRAND arrays have been in operation: GRAND@Nan\c{c}ay in France, GRAND@Auger in Argentina, and GRANDProto300 in China. Together, their goal is to validate the detection principle of GRAND under prolonged field conditions, achieving efficient, autonomous radio-detection of air showers. We describe the hardware, software, layout, and operation of the GRAND prototypes. Using their data, we show a first characterization of the local electromagnetic environment of each site and a measurement of the Galactic synchrotron emission. Despite challenges, the successful operation of the prototypes confirms that the GRAND instrumentation is apt to address the goals of the experiment and lays the groundwork for its ensuing stages.

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The Giant Radio Array for Neutrino Detection (GRAND) Collaboration -- Contributions to the 39th International Cosmic Ray Conference (ICRC 2025)

The Giant Radio Array for Neutrino Detection (GRAND) is an envisioned observatory of ultra-high-energy particles of cosmic origin, with energies in excess of 100 PeV. GRAND uses large surface arrays of antennas to look for the radio emission from extensive air showers that are triggered by the interaction of ultra-high-energy cosmic rays, gamma rays, and neutrinos in the atmosphere or underground. In particular, for ultra-high-energy neutrinos, the future final phase of GRAND aims to be sensitive enough to detect them in spite of their plausibly tiny flux. Three prototype GRAND radio arrays have been in operation since 2023: GRANDProto300, in China, GRAND@Auger, in Argentina, and GRAND@Nan\c{c}ay, in France. Their goals are to field-test the GRAND detection units, understand the radio background to which they are exposed, and develop tools for diagnostic, data gathering, and data analysis. This list of contributions to the 39th International Cosmic Ray Conference (ICRC 2025) presents an overview of GRAND, in its present and future incarnations, and a first look at data collected by GRANDProto300 and GRAND@Auger, including the first cosmic-ray candidates detected by them.

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Search for cosmic rays in GRANDProto300

GRANDProto300 (GP300) is a prototype array of the GRAND experiment, designed to validate the technique of autonomous radio-detection of astroparticles by detecting cosmic rays with energies between 10$^{17}$-10$^{18.5}$ eV. This observation will further enable the study of the Galactic-to-extragalactic source transition region. Between November 2024 u to May 2025, 46 out of 300 antennas have been operational and collecting data stably. We present here our cosmic-ray search pipeline, which involves several filtering steps: (1) coincidence search for signals triggering multiple antennas within a time window, (2) directional reconstruction of events, (3) exclusion of clustered (in time and space) noise events, (4) polarization cut, (5) selection based on the size of the footprint, and (6) other less mature cuts in this preliminary stage, including visual cuts. The efficiency of the pipeline is evaluated and applied to the first batch of data, yielding a set of cosmic-ray candidate events, which we present.

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Observe Gamma-Rays and Neutrinos Associated with Ultra-High Energy Cosmic Rays

IceCube measures a diffuse neutrino flux comparable to the Waxman-Bahcall bound, which suggests the possibility that the ultra-high energy cosmic rays (UHECRs) have a common origin with diffuse high energy neutrinos. We propose high energy gamma-ray and/or neutrino observations toward the arrival directions of UHECRs to search for the sources and test this possibility. We calculate the detection probability of gamma-ray/neutrino sources, and find that the average probability per UHECR of >10 EeV is $\sim$10% if the sensitivity of the gamma-ray or neutrino telescope is $\sim$10$^{-12}$ erg cm$^{-2}$s$^{-1}$ and the source number density is $\sim$10$^{-5}$ Mpc$^{-3}$. Future gamma-ray and neutrino observations toward UHECRs, e.g., by LHAASO-WCDA, CTA, IceCube/Gen2, are encouraged to constrain the density of UHECR sources or even identify the sources of UHECRs.

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The Giant Radio Array for Neutrino Detection (GRAND) Collaboration -- Contributions to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024)

This is an index of the contributions by the Giant Radio Array for Neutrino Detection (GRAND) Collaboration to the 10th International Workshop on Acoustic and Radio EeV Neutrino Detection Activities (ARENA 2024, University of Chicago, June 11-14, 2024). The contributions include an overview of GRAND in its present and future incarnations, methods of radio-detection that are being developed for them, and ongoing joint work between the GRAND and BEACON experiments.

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Development of an Autonomous Detection-Unit Self-Trigger for GRAND

One of the major challenges for the radio detection of extensive air showers, as encountered by the Giant Radio Array for Neutrino Detection (GRAND), is the requirement of an autonomous radio self-trigger. This work presents the current development of self-triggering techniques at the detection-unit level -- the so-called first-level trigger (FLT) -- in the context of the NUTRIG project. A second-level trigger (SLT) at the array level is described in a separate contribution. Two FLT methods are described, based on a template-fitting algorithm and a convolutional neural network (CNN). In this work, we compare the preliminary offline performance of both FLT methods in terms of signal selection efficiency and background rejection efficiency. We find that for both methods, ${\gtrsim}40\%$ of the background can be rejected if a signal selection efficiency of 90\% is required at the $5σ$ level.

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GRANDlib: A simulation pipeline for the Giant Radio Array for Neutrino Detection (GRAND)

The operation of upcoming ultra-high-energy cosmic-ray, gamma-ray, and neutrino radio-detection experiments, like the Giant Radio Array for Neutrino Detection (GRAND), poses significant computational challenges involving the production of numerous simulations of particle showers and their detection, and a high data throughput. GRANDlib is an open-source software tool designed to meet these challenges. Its primary goal is to perform end-to-end simulations of the detector operation, from the interaction of ultra-high-energy particles, through -- by interfacing with external air-shower simulations -- the ensuing particle shower development and its radio emission, to its detection by antenna arrays and its processing by data-acquisition systems. Additionally, GRANDlib manages the visualization, storage, and retrieval of experimental and simulated data. We present an overview of GRANDlib to serve as the basis of future GRAND analyses.

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Approach for composition measurement of cosmic rays using the muon-to-electron ratio observed by LHAASO-KM2A

Composition measurement of cosmic rays (CRs) around the knee of the CR energy spectrum is crucial for studying the processes of particle acceleration and propagation of Galactic CRs. The Square Kilometer Array (KM2A) of Large High Altitude Air Shower Observatory (LHAASO) can provide precise measurement of the muonic and electromagnetic (em.) components in CR-induced extensive air showers, and hence a good chance to disentangle the CR composition. Here we propose an approach of decomposing CR compositions with the number ratio between muons and em. particles ($N_μ$/$N_{\rm e}$) observed by LHAASO-KM2A: we reconstruct the energy spectra of individual CR compositions by fitting $N_μ$/$N_{\rm e}$ distributions in each reconstructed energy bin using the template shapes of $N_μ$/$N_{\rm e}$ distributions of individual CR compositions based on Monte Carlo (MC) simulation. We evaluate the performance of this approach with MC tests where mock data of LHAASO-KM2A are generated by MC simulation. We show that the input composition model can be well recovered in this approach, independent of the CR composition model adopted in the MC simulation for the template distributions. The uncertainties of the reconstructed spectra at < 20 PeV, mainly limited by simulation statistics, are $\le$ 7% for proton, He, and Fe groups, and $\le$ 8% and $\le$ 16% for CNO and MgAlSi groups, respectively.

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