arXiv subjects

Zhisen Lai

Publications and source records attributed to Zhisen Lai.

9 recordsLinked to original sources

arXiv2026

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.

astro-ph.IM↗

arXiv2026

Deep-Learning Denoising of Radio Signals for Ultra-High-Energy Cosmic-Ray Detection

Extensive air showers initiated by ultra-high-energy cosmic rays (UHECRs) produce broadband radio pulses detectable over large areas, but Galactic and instrumental backgrounds limit sensitivity near threshold. The Giant Radio Array for Neutrino Detection (GRAND) will use large arrays of autonomous radio antennas to detect these air showers, requiring reliable recovery of weak pulses from background fluctuations. We develop a convolutional denoiser for GRAND-like antenna traces that combines the time-domain waveform with the magnitude and phase of its Fourier transform. The model is trained on $4.1\times10^5$ simulated traces constructed from ZHAireS air-shower emission, a reduced GRAND-like RF-chain response, and broadband noise normalized to the expected Galactic-noise scale. In these simulations, denoising increases the detection probability at fixed false-positive rate and improves pulse-time recovery near threshold. The reconstructed peak amplitude is attenuated in the noise-dominated regime. This bias decreases as the pulse becomes better resolved, but a smaller channel-dependent bias persists at higher SNR. Near threshold, denoising therefore primarily improves candidate recovery and timing, while the measured amplitude response provides a basis for calibrating amplitude-sensitive observables.

hep-ex↗

arXiv2026

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@Nançay 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.

astro-ph.IM↗

arXiv2025

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@Nançay, 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.

astro-ph.IM↗

arXiv2025

Denoising radio pulses from air showers using machine-learning methods

The Giant Radio Array for Neutrino Detection (GRAND) aims to detect radio signals from extensive air showers (EAS) caused by ultra-high-energy (UHE) cosmic particles. Galactic, hardware-like, and anthropogenic noise are expected to contaminate these signals. To address this problem, we propose training a supervised convolutional network known as an encoder-decoder. This network is used to learn a coded representation of the data and remove specific features from it. This denoiser is trained using high-fidelity air shower simulations specifically tailored to replicate the characteristics of signals detected by GRAND. In this contribution, we describe our machine-learning model and report initial results demonstrating the sensitivity enhancement resulting from our denoising algorithm when applied to realistically simulated GRAND signals with varying signal-to-noise ratios.

astro-ph.IM↗

arXiv2024

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.

astro-ph.IM↗

arXiv2024

Reconstruction of highly inclined extensive air showers in GRAND

The Giant Radio Array for Neutrino Detection (GRAND) aims to detect highly inclined extensive air showers (EAS) with down-going and up-going trajectories. Several working groups in the GRAND collaboration are developing methods to reconstruct the incoming direction, core position, primary energy, and composition of the showers. The reconstruction pipeline -- currently under development in the France/IAP working group -- relies on a model of spherical wavefront emission for arrival times, which is possible because the radio signals are generated far away from the antenna stations. The amplitude distribution of the signals at the antenna level is described by an Angular Distribution Function that considers various asymmetries in the data, including geomagnetic effects. In this contribution, we present preliminary results from testing our EAS reconstruction procedure using realistic mock observations.

astro-ph.HE↗

arXiv2022

Supernova 2020wnt: An Atypical Superluminous Supernova with a Hidden Central Engine

We present observations of a peculiar hydrogen- and helium-poor stripped-envelope (SE) supernova (SN) 2020wnt, primarily in the optical and near-infrared (near-IR). Its peak absolute bolometric magnitude of -20.9 mag and a rise time of 69~days are reminiscent of hydrogen-poor superluminous SNe (SLSNe~I), luminous transients potentially powered by spinning-down magnetars. Before the main peak, there is a brief peak lasting <10 days post-explosion, likely caused by interaction with circumstellar medium (CSM) ejected ~years before the SN explosion. The optical spectra near peak lack a hot continuum and OII absorptions, which are signs of heating from a central engine; they quantitatively resemble those of radioactivity-powered H/He-poor Type Ic SESNe. At ~1 year after peak, nebular spectra reveal a blue pseudo-continuum and narrow OI recombination lines associated with magnetar heating. Radio observations rule out strong CSM interactions as the dominant energy source at +266 days post peak. Near-IR observations at +200-300 day reveal carbon monoxide and dust formation, which causes a dramatic optical light curve dip. Pair-instability explosion models predict slow light curve and spectral features incompatible with observations. SN 2020wnt is best explained as a magnetar-powered core-collapse explosion of a 28 Msun pre-SN star. The explosion kinetic energy is significantly larger than the magnetar energy at peak, effectively concealing the magnetar-heated inner ejecta until well after peak. SN 2020wnt falls into a continuum between normal SNe Ic and SLSNe I and demonstrates that optical spectra at peak alone cannot rule out the presence of a central engine.

astro-ph.HE↗

arXiv2022

The Circumstellar Environments of Double-Peaked, Calcium-strong Supernovae 2021gno and 2021inl

We present panchromatic observations and modeling of calcium-strong supernovae (SNe) 2021gno in the star-forming host galaxy NGC 4165 (D = 30.5 Mpc) and 2021inl in the outskirts of elliptical galaxy NGC 4923 (D = 80 Mpc), both monitored through the Young Supernova Experiment (YSE) transient survey. The multi-color light curves of both SNe show two peaks, the former peak being derived from shock cooling emission (SCE) and/or shock interaction with circumstellar material (CSM). The primary peak in SN 2021gno is coincident with luminous, rapidly decaying X-ray emission ($L_x = 5 \times 10^{41}$ erg s$^{-1}$) detected by Swift-XRT at $δt = 1$ day after explosion, this observation being the second ever detection of X-rays from a calcium-strong transient. We interpret the X-ray emission from SN 2021gno in the context of shock interaction with dense CSM that extends to $r < 3 \times 10^{14}$ cm. Based on modeling of the SN 2021gno X-ray spectrum, we calculate a CSM mass range of $M_{\rm CSM} = (0.3 - 1.6) \times 10^{-3}$ M$_{\odot}$ and particle densities of $n = (1-4) \times 10^{10}$ cm$^{-3}$. Radio non-detections of SN 2021gno indicate a low-density environment at larger radii ($r > 10^{16}$ cm) and a progenitor mass loss rate of $\dot{M} < 10^{-4}$ M$_{\odot}$ yr$^{-1}$, for $v_w = 500$ km s$^{-1}$. For radiation derived from SCE, modeling of the primary light curve peak in both SNe indicates an extended progenitor envelope mass and radius of $M_e = 0.02 - 0.05$ M$_{\odot}$ and $R_e = 30 - 230$ R$_{\odot}$. The explosion properties of SNe 2021gno and 2021inl suggest progenitor systems containing either a low-mass massive star or a white dwarf (WD), the former being unlikely for either object given the lack of star formation at both explosion sites. Furthermore, the progenitor environments of both SNe are consistent with explosion models for low-mass hybrid He/C/O WD + C/O WD binaries.

astro-ph.HE↗