The Study and Development of BPM Noise Monitoring at the Siam Photon Source

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This manuscript reports on the implementation of a noise monitoring system at the Siam Photon Source. Based on frequency analysis, four types of noises are categorized for the diagnosis of this complex facility. Employing a deep learning system, it achieved accurate identification of noise patterns. This will be useful if clear explanations are provided.

1. It would be better to provide a discussion or suggestion on how to secure operational stability with this noise monitoring system.

2. The authors classified four types of noise patterns. Are there any other types of noise patterns? What if unexpected types happen in this facility? Providing this discussion would be great.

3. From the perspective of signal analysis, what is the cause of each noise type?

4. Some figures are unclear to read texts or numbers.

Author Response

Thank you for your valuable comments and corrections. The manuscript has been revised accordingly as detailed below.

Comment 1: It would be better to discuss how operational stability is secured with this noise monitoring system.

Response 1: We agree with the comment. A discussion on how operational stability is ensured by the noise monitoring system has been added to the Discussion section (page 13, line 376). Currently, the RF voltage is reduced during machine operation to suppress the noise and improve beam stability.

 

Comment 2: Are there other types of noise patterns beyond the four classified? What if unexpected types occur?

Response 2: Thank you for highlighting this. A discussion on potential additional noise types and ongoing monitoring has been added to the Materials and Methods section (page 9, line 282). In the paper, we have focused on only four types of noise, as they are easier to identify. However, based on our records, these noise patterns may be further subdivided into more detailed categories in the future.

 

Comment 3: What is the cause of each noise type from a signal analysis perspective?

Response 3: The causes of the noise are currently under investigation. Potential sources include the RF system, utilities, or the beam itself. Data from all subsystems are being recorded for further analysis. This explanation has been added to the Discussion section (page 13, line 371).

 

Comment 4: Some figures are unclear.

Response 4: We have enlarged the text and numbers in the following figures for clarity:

• Figure 2: Introduction section (page 2, line 58)
• Figure 5: Materials and Methods (page 5, line 146)
• Figure 6: Materials and Methods (page 6, line 166)
• Figure 7: Materials and Methods (page 6, line 185)
• Figure 10: Results and Discussion (page 9, line 262)
• Figure 15: Results and Discussion (page 11, line 319)

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The study presents a real-time noise monitoring system for the Siam Photon Source (SPS) storage ring, integrating machine learning tools to classify beam noise patterns. The goal of the study is to facilitate the long-term classification and study of the data collected.   These tasks,  as an accelerator-based facility, generate a huge amount of data, are particularly complicated. For these reasons, it is really important to study of possible  applications of machine learning and other advanced strategies to these aims

The method to collect the data, the noise classification used, the Machine Learning Models, and the System Integration are well described. Particularly in they describe an interesting alert system integrating mobile phone applications too. This system is restricted to the monitoring of the data acquisition and storage system itself.  It should be interesting to discuss the extension of this alert system to the whole machine.

In the abstract, the authors claim, “This approach highlights the potential of combining beam diagnostics with machine learning to enhance operational stability and optimize synchrotron radiation performance for user experiments”. In the conclusions, the quote simply “Ultimately, the goal is to enhance beam quality and operational stability for the benefit of users and experiments”. Can the authors clarify these points a little more in the conclusions? What are, according to the authors, some of the methods and systems that could be used to achieve these aims? Maybe some of the data collected could be analyzed with a model independent analysis? Please add some elements to the discussion of the possible uses of the data classification and analysis to improve the machine's performance and stability.

I found  a problem is the following sentence:

“The Beam Position Monitor (BPM) is one of the essential beam diagnostic tools used to characterize the beam profile and can also be employed to analyze noise in the electron beam position [3].”   

 BPM monitor and measure positions and orbit/trajectory.

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Author Response

Thank you for your valuable comments and corrections. The manuscript has been revised accordingly as detailed below.

Comment 1: Discuss the extension of the alert system to the whole machine.

Response 1: We have added an explanation on the potential extension of the alert system to the entire facility in the Materials and Methods section (page 7, line 224).

 

Comment 2: Discuss the possible use of data classification and analysis to improve machine performance and stability.

Response 2: A discussion on how noise and parameter classification can support improved machine performance has been added to the Discussion section (page 14, line 384).

 

Comment 3: Correct the sentence describing BPM function.

Response 3: The sentence describing the BPM function in the Introduction section has been revised for clarity (page 2, line 47).

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This is a nice application of machine learning to an accelerator. The specific machine parameter studied is the electron beam noise as measured on a beam position monitor (BPM). The signal may exhibit various types of noise, each of which might be caused by a different problem in the. machine. The paper is quite clear and shows the results of measurements and the application of the machine learning algorithm to identify the type of noise and display it as part of the control system. The chosen system is quite accurate and it is not clear that even a human operation could do any better since many of the measurements have some level of ambiguity in them. 

One thing that might be useful would be to describe one case where the indicated noise lead to the discovery of some specific problem in the. machine. This would clearly show how useful the system is. 

There were some problems with the figures and table. In figure 2, either the two parts of the figure are shown incorrectly or the text describing he figure is wrong. According to the text, figure 2a should be the BPM noise and 2b should be the BL4 spectrum. That is not what is shown. Also, in figures 12-14 and Table 1, the word board is used instead of broad. These minor problems should be fixed before publication.

Author Response

Thank you for your valuable comments and corrections. The manuscript has been revised accordingly as detailed below.

Comment 1: Describe a case where the indicated noise revealed a specific machine problem.

Response 1: A discussion of how the noise monitoring system helped identify specific machine issues has been added to the Discussion section (page 13, line 361).

 

Comment 2: Figures and tables have inconsistencies and typographical errors.

Response 2: The following figures and table have been corrected as suggested:

• Figure 2: Introduction section (page 2, line 58)
• Figure 12: Results and Discussion (page 10, line 301)
• Figure 13: Results and Discussion (page 10, line 303)
• Figure 14: Results and Discussion (page 11, line 305)
• Table 1: Results and Discussion (page 12, line 323)

Author Response File: Author Response.pdf