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Peer-Review Record

Long-Term Timing Analysis of PSR J1741—3016: Efficient Noise Characterization Using PINT

Symmetry 2025, 17(3), 373; https://doi.org/10.3390/sym17030373
by Yirong Wen 1,2,3, Jingbo Wang 3,*, Wenming Yan 1,4,*, Jianping Yuan 1,4, Na Wang 1,4, Yong Xia 1,2,3 and Jing Zou 1,2,3
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Reviewer 3: Anonymous
Symmetry 2025, 17(3), 373; https://doi.org/10.3390/sym17030373
Submission received: 27 December 2024 / Revised: 20 February 2025 / Accepted: 24 February 2025 / Published: 28 February 2025

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Please find the attached report.

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors The study introduces a frequentist framework within the PINT software to analyze timing properties in pulsars, applied to a long-term observation for PSR J1741−3016. They showed that the results obtained from PINT is comparable with ENTERPRISE and TEMPONEST, but a reduced computational cost.

This is very important work. I do not have any scientific suggestions for the authors. I feel the work is suitable for publication.

However, the paper will benefit from language editing.

Comments on the Quality of English Language

Language needs to be improved. There are some typos. Please check for it. If possible, rephrase/re-write the text.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Dear Author,

 

The study presents a detailed timing analysis of the young pulsar PSR J1741−3016, based on 17.5 years of pulse time-of-arrival data obtained with the Nanshan 26-meter radio telescope. The authors employed the PINT software package, a frequentist framework, to model the pulsar's spin parameters, position, and noise characteristics, including both white and red noise components. The Akaike Information Criterion was utilized to determine the optimal noise model, balancing complexity and goodness of fit. The results obtained using PINT were compared to those from two Bayesian-based frameworks, TEMPONEST and ENTERPRISE, demonstrating PINT's comparable accuracy with significantly reduced computational costs.



I have a few comments:

 

  1. The study discusses distinct white noise modeling for data gathered before and after 2010. Could the authors clarify the specific changes made to the backend system and how these affected the timing results?

 

  1. Although the study asserts notable computational efficiency with PINT, can the authors present quantitative metrics that compare computation times between different frameworks?

 

  1. Did the authors conduct any sensitivity tests to evaluate the robustness of spin parameter estimations under various noise model assumptions?

 


Minor comments:

 

  1. The citation issue is in line 68. 

  2. Additionally, section headings are inconsistent in case structure; for instance, “analysis and results” should be formatted as “Analysis and Results” to ensure uniformity.


With the suggested revisions and clarifications, I am confident that the paper will meet the high standards expected in this area of research.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

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