Preamble

Vol. XX, No. X, XXX 20XX
NUCLEAR TECHNIQUES

Study on High-Time-Resolution Beam Loss Monitoring Technology for Hefei Light Source II

LIU Yihong¹, XIAO Yunzhi¹, YU Lingda², YANG Xing³, MA Xiaochao³, LENG Yongbin¹,³
¹(School of Nuclear Science and Technology, University of Science and Technology of China, Hefei 230027, China)
²(Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China)
³(National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei 230029, China)

Abstract

[Background] Beam loss monitoring is essential for the stable operation of Hefei Light Source II (HLS-II). Accurate measurement of bunch-by-bunch beam loss and position information helps diagnose beam instabilities and optimize accelerator performance. [Purpose] This study aims to monitor and analyze beam loss characteristics during HLS-II operation. [Methods] A high-time-resolution beam loss and bunch position synchronization monitoring system was developed using a scintillator detector, a strip-electrode beam position monitor (BPM), and a high-speed oscilloscope. The accuracy of the system's beam loss pulse waveform output was validated by comparing simulated and measured waveforms. Characteristic parameters of beam loss pulses were extracted using an asymmetric Gaussian fitting method. Bunch-by-bunch charge and position information were extracted from strip-electrode signals using the HOTCAP technique. Bunch indexing between beam loss data and BPM data was aligned based on the unique filling pattern of the HLS storage ring. [Results] The system was used to monitor beam losses during both the transient injection process and steady-state operation of HLS-II. The results indicate that during steady-state operation, bunch-by-bunch beam loss exhibits a significant correlation with the charge distribution, consistent with theoretical expectations for random beam loss. During the transient injection process, significant beam loss was observed simultaneously in the injected bunch and the 14th stored bunch following it—a phenomenon observed for the first time. [Conclusions] The measurement system can synchronously acquire bunch-by-bunch charge, transverse position, longitudinal position, and beam loss data from HLS-II, providing strong technical support for diagnosing beam instabilities and optimizing accelerator performance.

Keywords: Beam loss; Bunch-by-bunch; Scintillator detector; Synchrotron radiation source

1. Introduction

Hefei Light Source is a dedicated vacuum ultraviolet and soft X-ray synchrotron radiation facility, consisting primarily of an 800 MeV linear accelerator injector, an 800 MeV electron storage ring, 10 synchrotron radiation beamlines, and multiple experimental stations. The storage ring parameters are: energy 800 MeV, circumference 66.13 m, RF frequency 204 MHz, harmonic number 45, revolution period 220.59 ns, beam lifetime exceeding 10 hours, and average beam current 300 mA.

Hefei Light Source employs the local bump orbit injection method, as shown in [FIGURE:1]. Four kicker magnets are used to create an outward bump in the ideal orbit, with the maximum displacement point serving as the injection point. A pulsed septum magnet is placed at this location. When the bump reaches its maximum amplitude, beam injection occurs. The injected beam is deflected by the septum magnet to travel parallel to the bump orbit into the storage ring acceptance, while the closed orbit contracts according to a specific pattern, allowing the injected electrons to avoid the septum plate and vacuum chamber walls and complete the injection process [1]. Throughout the entire process from injection to stable storage, multiple beam loss mechanisms exist: First, the injected beam may strike the septum magnet and vacuum chamber walls, causing beam loss. Second, the Twiss parameters of the fresh bunch may mismatch those at the storage ring injection point, leading to particle loss. Additionally, the pulsed electromagnetic fields of injection components may disturb stored beams, inducing transverse or longitudinal beam instabilities that cause further losses. If beam loss persists above acceptable levels during injection, it severely limits injection efficiency [2] and impedes beam lifetime improvement. Therefore, monitoring and studying beam dynamics and loss during injection is crucial. Developing a bunch-by-bunch beam loss monitoring system would provide key technical support for analyzing injection transients at Hefei Light Source.

Various detector types are widely used in accelerator facilities worldwide to meet beam diagnostics and machine protection requirements. Proton and heavy-ion accelerators, which are more concerned with loss dosimetry, often choose ionization chambers as beam loss detectors. These offer large energy measurement ranges and strong radiation resistance, as exemplified by CERN's Large Hadron Collider [3] and China's Spallation Neutron Source [4]. However, ionization chambers have microsecond-level response times, making them incapable of resolving bunch-by-bunch structures. The Beijing Electron-Positron Collider [5], Shanghai Synchrotron Radiation Facility [6], and Hefei Light Source [7] use dual PIN diodes to monitor beam losses at different locations. These detectors are compact, sensitive to electrons, and insensitive to gamma ray interference, making them suitable for distributed deployment. However, their dead time of 100 ns only permits average beam loss measurements and cannot meet high-time-resolution requirements. J-PARC [8] and the European Synchrotron Radiation Facility [9] use scintillator detectors for beam loss monitoring. These offer fast temporal response at the nanosecond level and are insensitive to X-rays in background noise, though scintillators are susceptible to radiation damage that reduces light yield. The Australian Synchrotron [10] and Siberian Light Source [11] use Cherenkov-based fiber optic beam loss detectors to locate beam losses. These detectors exhibit extremely fast temporal response (Cherenkov light is instantaneous; overall response time is primarily determined by the photomultiplier tube), are only sensitive to charged particles, but have low light yield and an energy threshold, making them more suitable for high-dose loss monitoring scenarios.

Overall, existing beam loss monitoring technologies primarily focus on spatial localization and relative loss rate evaluation, while bunch-by-bunch beam loss monitoring has rarely been studied in depth. The Large Hadron Collider at CERN uses nanosecond time-resolution, radiation-hard diamond detectors for bunch-by-bunch beam loss monitoring, studying injection, beam loss, instabilities, and dust-induced loss patterns [12]. However, with a bunch spacing of 50 ns, bunch-by-bunch loss resolution is relatively less challenging. In storage rings with denser bunch patterns, achieving bunch-by-bunch beam loss monitoring remains challenging. To address frequent beam loss issues, the Beijing Electron-Positron Collider II developed a bunch-by-bunch beam loss monitoring system based on BPMs, effectively identifying losses caused by RF cavity trips, tune drift, and multibunch instabilities [13]. However, due to limitations in charge resolution, this system's capability for detecting small-dose losses remains limited.

To achieve bunch-by-bunch and turn-by-turn beam loss monitoring and analysis, this study comprehensively evaluated various detectors and ultimately selected a scintillator detector with high radiation sensitivity and nanosecond-level temporal response. Combined with previously developed bunch-by-bunch charge and 3D position measurement techniques [14-16], we designed and built a high-time-resolution beam loss and bunch position synchronization monitoring system to monitor and analyze beam losses during Hefei Light Source operation.

1.1 Basic Working Principle of the Scintillator + PMT Beam Loss Monitoring System

The principle of using a scintillator detector for beam loss monitoring is shown in [FIGURE:2]. When electron beams in the storage ring are lost on the vacuum chamber walls, they produce shower particles, primarily shower electrons and gamma photons [17]. These shower particles deposit energy in the scintillator, causing ionization and excitation of scintillator atoms. When excited atoms de-excite, they emit scintillation photons in the visible wavelength range. The scintillation photons are collected via light guides onto the photocathode of a photomultiplier tube (PMT), generating photoelectrons. These photoelectrons are multiplied stage-by-stage at the PMT's dynodes, ultimately forming an electrical signal in the output circuit as they travel between the anode and the final dynode. Typically, the number of photons produced by the scintillator is proportional to the energy deposited by radiation in the scintillator, so the output signal amplitude is also proportional to the incident radiation energy.

1.2 Structural Design of the HLS-II High-Time-Resolution Beam Loss Monitoring System

This study built the HLS-II high-time-resolution beam loss and bunch position synchronization monitoring system based on a scintillator detector, beam position monitor (BPM), and high-speed oscilloscope. The system structure is shown in [FIGURE:3], with core components including: (1) Scintillator: ELJEN EJ-200 scintillator, 100 mm length, 22 mm diameter, emission wavelength 425 nm, light pulse rise time 0.9 ns, decay time 2.1 ns, full width at half maximum (FWHM) 2.5 ns; (2) Photomultiplier tube: Hamamatsu H10721-110 PMT, detection wavelength range 230-700 nm, peak sensitivity wavelength 400 nm, rise time 0.57 ns, FWHM 1.25 ns; (3) Beam position monitor: Used to pick up bunch-by-bunch beam signals and calculate transverse and longitudinal bunch positions; (4) High-speed oscilloscope: Keysight oscilloscope with 16 GSa/s sampling rate, 6.3 GHz bandwidth, and 10-bit voltage resolution.

After the beam loss signal is collected by the scintillator detector, it is digitized and stored by the high-speed oscilloscope, and offline data processing is performed using MATLAB to extract amplitude and timing information from the beam loss signals. To correlate beam loss with bunch position, the system integrates a beam position monitor, whose horizontally distributed strip electrode signals are synchronously acquired through two additional oscilloscope channels. The BPM signals are processed using the HOTCAP software package [18] (a high-speed oscilloscope-based tool for precise bunch-by-bunch 3D charge and position measurement) developed by our group, ultimately outputting parameters such as bunch-by-bunch charge, transverse horizontal position, and longitudinal position. Through multi-module collaborative operation, the system achieves high-time-resolution monitoring of beam loss events and multi-dimensional parameter analysis.

1.3 Green's Function Simulation of the High-Time-Resolution Beam Loss Monitoring System

When radiation interacts with the scintillator, energy deposition triggers the scintillator to produce a light pulse composed of scintillation photons. Its temporal characteristics can be characterized by a combination of rise time and decay time, with the mathematical expression as follows [19]:

$$
n(t) = n_{ph} \cdot \left(1 - e^{-t/\tau_1}\right) \cdot e^{-t/\tau_0}
$$

where $n_{ph}$ is the total number of photons in the scintillation light pulse, $\tau_0$ is the scintillator's light decay time, and $\tau_1$ is the scintillator's rise time. The PMT's response function for a single photon output current can be expressed by a Gaussian function [20]:

$$
i_{sp}(t) = A \cdot \exp\left(-\frac{(t-t_0)^2}{2\sigma^2}\right)
$$

where $A$ is the PMT amplitude coefficient representing the peak output current pulse for a single photon response, $t_0$ is the electron transit time, and $\sigma$ is the Gaussian pulse width of the current pulse. By convolving the scintillation light pulse function with the PMT single-photon response function, the scintillator detector current pulse expression is obtained:

$$
i(t) = i_{sp}(t) \otimes n(t)
$$

Further, by modeling the PMT current output as a current source, the relationship between the detector output voltage pulse and current pulse is:

$$
V(t) = i(t) \otimes \left(R \cdot e^{-t/RC}\right)
$$

where $R = 50\,\Omega$ and $C = 80\,\text{pF}$ are the equivalent parameters of the voltage readout circuit. Based on this model, MATLAB was used for numerical simulation of the entire process from energy deposition in the scintillator to voltage pulse output. The normalized simulation results are shown in [FIGURE:4], with a pulse rise time of 2.3 ns and FWHM of 7.5 ns, significantly larger than the HLS bunch spacing of 4.9 ns. Therefore, under high beam loss rate conditions, if multiple adjacent bunches simultaneously experience losses at the detector location, their signals will completely overlap and become indistinguishable. However, for non-adjacent bunch loss events, signals can be effectively separated using deconvolution methods, as detailed in Section 1.4.

1.4 Beam Loss Signal Processing Methods

The HLS storage ring revolution period is 220.59 ns. Using a high-speed oscilloscope with 16 GSa/s sampling rate for digitizing beam loss signals, each revolution corresponds to 3529 sampling points. To extract amplitude and timing information from beam loss pulses, the signals are processed turn-by-turn, with 3528 sampling points extracted per revolution to form a continuous turn-by-turn dataset. [FIGURE:5] shows a typical measured beam loss waveform for one revolution after turn-by-turn slicing, with a pulse rise time of 2.8 ns and FWHM of 7.5 ns, which shows high consistency with simulation results in key parameters, with a waveform correlation coefficient of $R = 0.978$, validating the accuracy of the system's beam loss pulse waveform output.

During steady-state operation, beam loss signal amplitudes are small with low signal-to-noise ratios, requiring filtering to enhance signal features. After filtering, an asymmetric Gaussian function is used to fit the beam loss pulses, expressed as:

$$
f(t) = A \cdot \exp\left(-\frac{(t-\mu)^2}{2\sigma^2}\right) \cdot \left[1 + \text{erf}\left(\frac{\alpha(t-\mu)}{\sqrt{2}\sigma}\right)\right] + C
$$

where $A$ is the amplitude determining the peak height, $\mu$ is the peak time, $\sigma$ controls the pulse width, $\alpha$ is the asymmetry factor (right-side broadening when $\alpha > 0$, left-side broadening otherwise), and $C$ is the baseline offset. After fitting, the beam loss amplitude is characterized by $A - C$, and the peak time is determined by $\mu$.

During injection, beam loss frequency increases significantly, causing pulse pile-up phenomena (dashed line in [FIGURE:6]). To more accurately extract amplitude and timing information from piled-up pulses, identification and reconstruction of stacked pulses are required. First, the same low-pass filter is applied to eliminate background noise. Then, MATLAB's peak-finding function is used to identify pulse numbers and segmentation points. After segmentation, the first pulse is fitted with an asymmetric Gaussian function to extract amplitude ($A - C$) and timing ($\mu$) parameters. This fitted pulse component is then subtracted from the original signal, and the process is repeated until all pulses are resolved (solid line in [FIGURE:6]).

1.5 BPM Signal Processing Methods

The raw time-domain waveform of BPM signals measured on the HLS storage ring is shown in [FIGURE:7]. This signal carries multi-dimensional information including bunch charge, bunch length, transverse position, and longitudinal phase. In experiments, two horizontally distributed strip electrode signals are synchronously acquired by the oscilloscope and input into the HOTCAP software package for data processing and analysis, enabling extraction of bunch-by-bunch charge, turn-by-turn transverse horizontal position, and turn-by-turn longitudinal phase information [21]. Typical bunch-by-bunch charge and position data measured during injection are shown in [FIGURE:8].

2 Beam Experiments

During normal HLS operation, beam losses are primarily concentrated in regions downstream of dipole magnets [22]. Therefore, the scintillator detector was installed behind a dipole magnet and adjacent to the BPM at that location, as shown in the on-site installation photo in [FIGURE:9]. In top-up operation mode, beam loss conditions were monitored during both the transient injection process and post-injection steady-state operation. During the experiments, 35 bunches were filled in the storage ring with an average current of 300 mA.

2.1 Beam Loss During Steady-State Operation

Using the beam loss signal processing method described in Section 1.4, a beam loss scatter plot for steady-state operation was generated ([FIGURE:10]), where the horizontal axis represents sampling points (3528 points per revolution), the vertical axis represents turn number, and color maps the relative beam loss amplitude.

The HLS RF frequency is 204 MHz, corresponding to a bunch spacing of 4.9 ns. After sampling at 16 GSa/s, each bunch position corresponds to 78.4 sampling points. Based on this, the 45 bunch positions around the ring are segmented, and the average beam loss per revolution is calculated for each bunch position as the beam loss rate for the corresponding bunch. Since beam loss signals and BPM signals have different origins, there exists a fixed time delay between them, requiring data alignment. Because HLS operation employs a special filling pattern (a continuous train of 35 bunches plus one isolated single bunch, as shown in [FIGURE:8(a)]), this pattern's features can be used for data alignment. The specific method involves identifying the isolated bunch based on the bunch-by-bunch beam loss rate distribution and aligning it with the isolated bunch index in the bunch-by-bunch charge data, thereby achieving correlation between beam loss data and BPM bunch-by-bunch data. [FIGURE:11] shows the aligned bunch-by-bunch beam loss rate distribution, which is consistent with the storage ring filling pattern, with no significant beam loss signals observed at empty bunch positions. This demonstrates that the system possesses bunch-by-bunch beam loss resolution capability under steady-state, low-dose-rate conditions. The weak signals at empty bunch positions can be attributed to detection noise from environmental scattered radiation.

2.2 Beam Loss During Injection

During injection, beam loss frequency is extremely high, making it common for multiple adjacent bunches to simultaneously experience losses near the detector, resulting in severe signal overlap. It is difficult to precisely identify which bunch is losing beam and calculate its loss amplitude within a single revolution; only the approximate region of loss can be determined. Analysis of beam loss monitoring data from four injection cycles at HLS shows that, in addition to significant high-dose losses in the injected bunch, notable losses also occur near the 14th stored bunch following the injected bunch ([FIGURE:12]).

Losses in the injected bunch may result from incomplete matching between the Twiss parameters of the fresh injected bunch and those at the storage ring injection point. These can be minimized by optimizing injector parameters and storage ring injection system parameters to improve matching. The losses near the 14th stored bunch after injection may be caused by beam instabilities induced when the beam deviates significantly from its steady state during injection. Specifically, during injection, the beam deviates substantially from its equilibrium position both transversely and longitudinally, exciting strong transient wakefields. Through nonlinear inter-bunch interactions, these wakefields trigger instabilities in subsequent bunches. The underlying physical mechanism requires more detailed correlation analysis with bunch-by-bunch 3D position data through specifically designed beam experiments for further investigation.

Limited by the temporal resolution of the current scintillator detector, the system cannot yet achieve true bunch-by-bunch beam loss monitoring at HLS. Under steady-state operation conditions, beam loss signals rarely overlap completely, allowing precise identification of specific bunches experiencing losses through correlation analysis with BPM signals. However, during injection or other high-loss-rate conditions, if adjacent bunches simultaneously experience losses near the detector, signals completely overlap, making individual pulses indistinguishable.

To evaluate under what bunch spacing conditions this system can achieve bunch-by-bunch loss resolution, this study performed simulations using asymmetric Gaussian functions fitted from measured pulses to construct beam loss signals. In the simulation, the amplitude of the first pulse was fixed at 1, while the amplitude of the second pulse was increased from 0.1 to 10. For each amplitude ratio, the time interval between the two pulses was gradually increased from 0 to 20 ns to generate stacked signals. When the stacked signal met the set peak discrimination condition and could just resolve two overlapping peaks, the corresponding time interval was defined as the resolvable time for that amplitude ratio. The simulation results are shown in [FIGURE:13]. When the amplitude ratio is approximately 0.51, the minimum resolvable time is only 5.6 ns. Assuming the amplitude ratio of adjacent bunches is typically within the range of 0.3–3, the corresponding maximum resolvable time is about 9.7 ns. It can thus be inferred that this system is capable of bunch-by-bunch beam loss monitoring in storage rings with bunch spacing greater than 9.7 ns.

To expand the system's applicability and achieve bunch-by-bunch beam loss monitoring, future optimizations can be pursued in several directions: First, employ faster beam loss detectors, such as Cherenkov-based fiber optic detectors, to improve temporal resolution. Second, develop advanced signal processing algorithms to shape the original beam loss waveforms into narrower pulses, further enhancing resolution capability. Third, incorporate machine learning or deep learning techniques to optimize pulse deconvolution algorithms for complex signal analysis in high-loss-rate scenarios. These improvements will further enhance the system's diagnostic accuracy and applicability, providing reliable support for beam loss monitoring at more accelerator facilities.

Conclusions

This study designed and successfully implemented a high-time-resolution beam loss and bunch position synchronization monitoring system on the HLS-II storage ring based on a scintillator detector, beam position monitor, and high-speed oscilloscope. Beam experiment results match numerical simulation results perfectly, validating the accuracy of the system's beam loss pulse waveform output. To address pulse pile-up during injection, an asymmetric Gaussian model fitting method was proposed to accurately extract amplitude and timing information from stacked pulses. Experimental results demonstrate that the system can effectively monitor bunch-by-bunch beam loss characteristics in electron storage rings while simultaneously monitoring multi-dimensional parameters including charge, transverse horizontal oscillation, and longitudinal phase. During injection, a phenomenon was observed where the beam loss rate in the 14th stored bunch after the injected bunch significantly exceeded that of other stored bunches, suggesting this effect may be related to coupling instabilities caused by inter-bunch wakefields or perturbations from pulsed magnets. More targeted beam experiments are needed in the future to further investigate the physical mechanism and optimize the injection process to reduce beam loss and improve injection efficiency. The system's capability to synchronously acquire bunch-by-bunch charge, transverse position, longitudinal position, and beam loss data provides excellent technical support for analyzing such complex beam dynamics processes.

Limited by the temporal response characteristics of the scintillator detector, the system's bunch-by-bunch beam loss diagnostic capability has certain limitations in high bunch density or high loss frequency scenarios. In the future, by employing faster detectors and combining them with machine learning techniques to optimize signal analysis algorithms, the system's bunch-by-bunch monitoring capability can be further enhanced, providing broader application support for various types of accelerator facilities.

Author Contributions

LIU Yihong was responsible for numerical simulation, data processing and organization, and drafting and revising the final manuscript. XIAO Yunzhi was responsible for data processing and organization. YU Lingda provided beam loss equipment support. YANG Xing was responsible for BPM data processing. MA Xiaochao was responsible for beam loss detector installation and commissioning. LENG Yongbin proposed the research, guided the methodology, and revised the final manuscript.

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