Pith. sign in

REVIEW 4 major objections 5 minor 58 references

Radio Continuum Studies of Ultra-Compact and Short Orbital Period X-Ray Binaries

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Pith's one-line read The globular clusters that host ultra-compact X-ray binaries are more concentrated and have higher encounter rates, while radio luminosity shows no correlation with orbital period.

desk verdict A careful, honest null-result radio study of UCXBs with a suggestive but selection-biased GC comparison; worth a serious referee. read the letter →

arxiv 2507.00345 v1 pith:7BTLINYE submitted 2025-07-01 astro-ph.HE

classification astro-ph.HE
keywords ultra-compactX-raybinariesglobularclustersradiocontinuumorbitalperiodencounterrateneutronstarsblackholes
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that the globular clusters hosting ultra-compact X-ray binaries (UCXBs) — systems where a neutron star or black hole pulls matter from a hydrogen-poor white dwarf in an orbit shorter than 80 minutes — are not a random subset of Milky Way clusters. Comparing 11 UCXB-hosting clusters with 36 clusters that have none, the authors find the hosts are more concentrated, have smaller core and half-light radii, and have significantly higher stellar encounter rates, while matching in mass and metallicity. The same study adds new ATCA radio observations, literature detections, and archival survey limits, then asks whether radio luminosity tracks orbital period. It finds no clear correlation (Spearman statistic 0.14, p=0.55), so the orbit's compactness does not obviously control the jet radio emission.

What carries the argument

The load-bearing comparison is between two samples: 11 globular clusters known to host a UCXB and 36 well-studied clusters without known UCXBs. Structural parameters (concentration, core radius, half-light radius), mass and metallicity are taken from standard cluster catalogs, and encounter rates come from a published dynamical calculation; the two samples are compared with Anderson-Darling tests on cumulative distributions. The radio part augments the existing catalog with 16 new ATCA observations, 16 literature radio measurements, and archival survey limits, then uses Spearman rank correlation to test whether radio luminosity and orbital period move together.

What would settle it

A uniform X-ray and radio census of all Milky Way globular clusters, with comparable sensitivity to that which discovered the current UCXBs, that finds a similar fraction of UCXBs in low-concentration, low-encounter-rate clusters would falsify the claim that UCXB hosts form a distinct high-density population.

Watch

Extended reading notes

Core claim

On the authors' own terms, the central claim is that UCXB formation is not uniformly distributed across globular clusters: the 11 clusters that host a UCXB are drawn from a distinct population with higher concentration ($p=0.02$), smaller core radius ($p=0.03$) and half-light radius ($p=0.02$), and dramatically higher encounter rates ($p=0.001$) than the 36-cluster comparison set, with no significant difference in metallicity or mass. The companion claim is that radio luminosity is decoupled from orbital period in the 20 UCXBs with period measurements, with a Spearman rank correlation of 0.14 and p-value 0.55; even detections alone give 0.31 with p=0.38. The paper therefore proposes that dense, dynamically active clusters enhance stable UCXB formation, while orbital period is not a governing parameter for jet radio emission.

Load-bearing premise

The comparison assumes that the 36 clusters without known UCXBs are a complete census, so if undiscovered UCXBs exist in less dense or less studied clusters, the reported differences in concentration and encounter rate could be a selection artifact rather than a physical distinction.

Editorial extensions

If this is right

  • If the population distinction holds, then globular cluster concentration and encounter rate are effective predictors of where ultra-compact X-ray binaries form, and future searches can target dense, high-encounter clusters.
  • Because no correlation between radio luminosity and orbital period is found, the orbital period should not be treated as a proxy for jet power in UCXBs; inclination, accretion state, and neutron-star magnetic field become the more promising controls.
  • The quiescent radio upper limits from the new ATCA observations provide a baseline for identifying future outbursts in these transient systems.
  • The claim that only high-encounter-rate clusters host UCXBs strengthens the dynamical formation channel, where collisions between neutron stars and red giants create these binaries.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A hidden selection effect is the main threat to the cluster claim: if low-density clusters harbor undiscovered UCXBs, the distinct-population result would weaken; a uniform all-cluster X-ray survey would test this.
  • The null period-luminosity correlation may simply mean the radio luminosity is set by transient accretion state rather than the binary's geometry; simultaneous X-ray and radio monitoring across an outburst cycle would separate these.
  • If encounter rate really is the controlling factor, then cluster simulations predicting the number and type of UCXBs per cluster could be directly compared with the 11 host clusters to calibrate dynamical formation rates.
  • Given that only 5 of 11 host clusters are core-collapsed, high concentration may matter more than actual core collapse; testing concentration as a continuous variable against UCXB probability could sharpen the trigger.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This paper presents a radio continuum study of ultra-compact X-ray binaries (UCXBs) drawn from the UltraCompCAT catalog, adding 16 new ATCA observations, 16 literature radio observations, and archival RACS, VLASS, and MAVERIC searches. The new ATCA observations yield no detections, and the authors report 3σ upper limits at 7.25 GHz for 16 sources. Combining new and archival data, they test for a correlation between radio luminosity and orbital period in 20 UCXBs and short-period LMXBs, finding no significant correlation. They also compare 11 globular clusters hosting UCXBs with 36 MAVERIC non-host clusters using Anderson-Darling tests, reporting that host clusters differ in concentration, core radius, half-light radius, and encounter rate, but not in mass or metallicity.

Significance. The radio data products—new upper limits, compiled literature fluxes, and archival non-detections—are a useful community resource, and the paper carefully documents the CASA reduction, calibration, and imaging procedures. The null correlation between radio luminosity and orbital period, if confirmed with a proper treatment of upper limits, would support the view that jet properties in UCXBs are governed by accretion state and system geometry rather than orbital period alone. The globular cluster comparison addresses an important dynamical-formation question and is framed with appropriate small-sample caveats in the text. However, the headline 'distinct population' claim currently rests on a control sample whose X-ray completeness is not demonstrated, so the significance of that claim is contingent on the selection test requested below.

major comments (4)
  1. [§3.4, Table 5] The comparison sample of 36 non-UCXB MAVERIC clusters is described as 'a complete set of GCs without known UCXBs,' but MAVERIC is a radio survey, whereas UCXBs are discovered in X-rays. A cluster enters the host sample only if it has been observed deeply enough in X-rays to reveal a quiescent or transient UCXB, and dense, high-encounter clusters are historically the targets of such observations. The observed differences in concentration, core radius, and encounter rate could therefore reflect X-ray selection rather than formation physics. The paper needs a sensitivity analysis: for example, restrict both samples to clusters with comparable Chandra or XMM coverage, or show that the non-host sample has X-ray exposures sufficient to detect UCXBs of the type found in hosts. Without this, the abstract's 'distinct population' claim is not fully supported.
  2. [§3.3] The Spearman rank correlation includes upper limits as if they were exact luminosity measurements in the 'entire sample' of 20 sources. This is a censored-data problem: treating a <5×10^28 erg/s limit as 5×10^28 erg/s biases the rank toward no correlation and makes the reported p-value of 0.55 unreliable. The authors should use survival-analysis methods (e.g., generalized Spearman, Akritas-Theil-Sen, or a log-rank-type test), or present detection-only results with the explicit caveat that upper limits are not independent measurements. As written, the conclusion that 'there is not a clear connection' is not justified by the test actually performed.
  3. [§3.4, Table 5] Six Anderson-Darling tests are performed on parameters that are physically correlated: concentration, core radius, half-light radius, and encounter rate all trace cluster density, and the p-values for concentration and half-light radius (0.02) are only marginally significant. No multiple-comparison correction is applied. The authors should report adjusted p-values or a permutation-based test, and should test whether the differences persist after excluding the five core-collapsed clusters or after controlling for cluster distance and reddening selection.
  4. [§2.1, Table 3] Radio luminosities are computed as L_R = 4πνSνd², using distances that carry substantial systematic uncertainty (especially helium-burst distances), but no distance or luminosity uncertainties are propagated into Table 3 or into the correlation tests. At minimum, the authors should quote luminosity ranges or demonstrate that the Spearman result is robust to plausible distance errors; otherwise the ranking of sources may partly reflect distance measurement systematics rather than intrinsic radio luminosity.
minor comments (5)
  1. [Table 3 caption] The word 'assuminfg' should be 'assuming'.
  2. [Acknowledgments] The word 'commends' should be 'comments'.
  3. [§3.4] The text refers to 'Table 3.1' when presenting the Anderson-Darling p-values, but the relevant table is Table 5.
  4. [Abstract and §4] Section 4 appropriately cautions about the small population and incomplete knowledge of GC UCXBs, but the abstract states the distinct-population result without that caveat; the abstract should carry the same qualification as the body text.
  5. [§2.1] The paper uses 'Brigg's robustness parameter'; the standard name is 'Briggs robustness parameter'.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the GC-population comparison and radio/period null result rest on external catalogs and independent measurements, not on fitted inputs or self-citations.

full rationale

The paper's two central claims are derived from external data, not from self-defined quantities. The radio-luminosity versus orbital-period analysis compiles literature fluxes, new ATCA upper limits, and distances from Armas Padilla et al. (2023), then computes a Spearman rank correlation; the null result is a direct test of the data, not a prediction forced by a fitted parameter. The globular-cluster-population comparison uses structural parameters from Harris (1996, 2010), masses and metallicities from Baumgardt & Hilker (2018), and encounter rates from Bahramian et al. (2013), all published external catalogs. The Anderson-Darling tests compare the 11 UCXB-hosting clusters against the 36 MAVERIC non-host clusters, and the paper explicitly acknowledges in Section 4 that its knowledge of GC UCXBs may be incomplete. That is a selection/completeness caveat about the control sample, not a circular reduction: the paper does not define UCXB hosts in terms of the tested cluster properties, nor does it fit a parameter and then rename it as a prediction. The cited works with overlapping authorship (Bahramian et al. 2013; Armas Padilla et al. 2023; MAVERIC survey papers) provide independent measured quantities and are not used to assert the paper's conclusions. No equation reduces to an input by construction, and no load-bearing premise is justified solely by a self-citation. The main identifiable weakness, the possible X-ray selection bias in which clusters are known to host UCXBs, is a confounding/observational-completeness issue that belongs in a correctness or robustness discussion, not a circularity finding.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new free parameters or entities. Its results rest on literature distances, cluster parameters, and an assumed flat radio spectrum, plus the completeness assumption for the MAVERIC comparison sample.

assumptions (4)
  • domain assumption Assumed flat radio spectrum (Sν ∝ ν^0) when converting flux density to luminosity
    Used for all sources in §2.1; may introduce systematic luminosity uncertainties for sources with steep spectra.
  • domain assumption Distances taken from Armas Padilla et al. (2023) compilation, with He-triggered burst distances preferred
    Distance uncertainties propagate into the luminosity and correlation analysis; the paper notes a possible systematic uncertainty.
  • domain assumption The 36 MAVERIC clusters form a complete set of GCs without known UCXBs
    Explicitly assumed in §3.4 to avoid poorly studied GCs; if incomplete, the comparison population is biased.
  • domain assumption Orbital periods and classifications of UCXBs from UltraCompCAT are reliable
    The sample is drawn from Armas Padilla et al. (2023), including candidate UCXBs selected by accretion signatures.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Radio Continuum Studies of Ultra-Compact and Short Orbital Period X-Ray Binaries." pith.science (2026). https://pith.science/paper/7BTLINYE

@misc{pith2026250700345,
  author       = {Pith},
  title        = {Pith review of: Radio Continuum Studies of Ultra-Compact and Short Orbital Period X-Ray Binaries},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7BTLINYE}},
  note         = {Machine review of arXiv:2507.00345}
}
abstract

We present the radio continuum counterparts to the enigmatic ultra-compact X-ray binaries (UCXBs); a black hole or neutron star accreting from a hydrogen-deficient white dwarf donor star, with short orbital periods ($<$ 80 minutes). For the sample of UCXBs hosted by globular clusters (GCs), we search for whether certain GC properties are more likely to enhance UCXB formation. We determine that GCs which host UCXBs are drawn from a distinct population in terms of cluster concentration, core radius and half-light radius, but are similar to other well-studied GCs in metallicity and cluster mass. In particular, UCXB-hosting GCs tend to be on average more compact, with a higher concentration than other GCs, with significantly higher encounter rates. We investigate whether a correlation exists between radio luminosity and orbital period, using new and archival observations. We determine that there is not a clear connection between the two observable quantities.

Figures

Figures reproduced from arXiv: 2507.00345 by the authors.

Figure 1
Figure 1. Only transient UCXBs with X-ray and radio taken simultaneously during outburst, or persistently accreting sources are displayed here. Left: Radio luminosity versus orbital period. Each system is color coded by accretor (NS, AMXP, BHC), and sources located in a GC are circled. Right: X-ray versus radio luminosity for all UCXBs. We do not see a strong correlation between radio, X-ray and orbital period for this sample… view at source ↗
Figure 2
Figure 2. Cumulative distribution function of GC properties (structural parameters, metallicity and cluster mass) with (pink solid line) and without (blue dashed line) detected UCXBs [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Encounter rate for UCXB-hosting clusters com￾pared to non-UCXB (MAVERIC) GCs. The UCXB-hosting clusters had significantly higher encounter rates than other (non-UCXB) clusters. may be certain aspects of the host cluster which are more likely to enhance stable UCXB formation. How￾ever, given that UCXBs are most often discovered by their transient events, and that BHs are incredibly dif￾ficult to observe/classify, we … view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

58 extracted references · 20 canonical work pages

  1. [1]

    2023, Living Reviews in Relativity, 26, 2, doi: 10.1007/s41114-022-00041-y Armas Padilla, M., Corral-Santana, J

    Amaro-Seoane, P., Andrews, J., Arca Sedda, M., et al. 2023, Living Reviews in Relativity, 26, 2, doi: 10.1007/s41114-022-00041-y Armas Padilla, M., Corral-Santana, J. M., Borghese, A., et al. 2023, A&A, 677, A186, doi: 10.1051/0004-6361/202346797

  2. [2]

    Gladstone, J. C. 2013, ApJ, 766, 136, doi: 10.1088/0004-637X/766/2/136

  3. [3]

    Bahramian, A., Strader, J., Miller-Jones, J. C. A., et al. 2020, ApJ, 901, 57, doi: 10.3847/1538-4357/aba51d

  4. [4]

    2018, MNRAS, 478, 1520, doi: 10.1093/mnras/sty1057

    Baumgardt, H., & Hilker, M. 2018, MNRAS, 478, 1520, doi: 10.1093/mnras/sty1057

  5. [5]

    B., & Church, R

    Bobrick, A., Davies, M. B., & Church, R. P. 2017, MNRAS, 467, 3556, doi: 10.1093/mnras/stx312 CASA Team, Bean, B., Bhatnagar, S., et al. 2022, PASP, 134, 114501, doi: 10.1088/1538-3873/ac9642

  6. [6]

    M., Han, Z., & Chen, X

    Chen, H.-L., Tauris, T. M., Han, Z., & Chen, X. 2021, MNRAS, 503, 3540, doi: 10.1093/mnras/stab670

  7. [7]

    2021, CARTA: The Cube Analysis and Rendering Tool for Astronomy, 2.0.0, Zenodo, doi: 10.5281/zenodo.4905459

    Comrie, A., Wang, K.-S., Hsu, S.-C., et al. 2021, CARTA: The Cube Analysis and Rendering Tool for Astronomy, 2.0.0, Zenodo, doi: 10.5281/zenodo.4905459

  8. [8]

    2008, MNRAS, 389, 1697, doi: 10.1111/j.1365-2966.2008.13542.x Coti Zelati, F., de Ugarte Postigo, A., Russell, T

    Corbel, S., Koerding, E., & Kaaret, P. 2008, MNRAS, 389, 1697, doi: 10.1111/j.1365-2966.2008.13542.x Coti Zelati, F., de Ugarte Postigo, A., Russell, T. D., et al. 2021, A&A, 650, A69, doi: 10.1051/0004-6361/202140573 D ´ ıaz Trigo, M., Migliari, S., Miller-Jones, J. C. A., et al. 2017, A&A, 600, A8, doi: 10.1051/0004-6361/201629472

Show all 58 references
  1. [9]

    W., Thomson, A

    Duchesne, S. W., Thomson, A. J. M., Pritchard, J., et al. 2023, PASA, 40, e034, doi: 10.1017/pasa.2023.31

  2. [10]

    2004, A&A, 414, 895, doi: 10.1051/0004-6361:20031683

    Falcke, H., K¨ ording, E., & Markoff, S. 2004, A&A, 414, 895, doi: 10.1051/0004-6361:20031683

  3. [11]

    2024, A&A, 684, A124, doi: 10.1051/0004-6361/202347908

    Chaty, S. 2024, A&A, 684, A124, doi: 10.1051/0004-6361/202347908

  4. [12]

    2018, MNRAS, 478, L132, doi: 10.1093/mnrasl/sly083

    Gallo, E., Degenaar, N., & van den Eijnden, J. 2018, MNRAS, 478, L132, doi: 10.1093/mnrasl/sly083

  5. [13]

    M., et al

    Gallo, E., Teague, R., Plotkin, R. M., et al. 2019, MNRAS, 488, 191, doi: 10.1093/mnras/stz1634

  6. [14]

    E., & Seaquist, E

    Grindlay, J. E., & Seaquist, E. R. 1986, ApJ, 310, 172, doi: 10.1086/164673

  7. [15]

    V., Deller, A

    Gusinskaia, N. V., Deller, A. T., Hessels, J. W. T., et al. 2017, MNRAS, 470, 1871, doi: 10.1093/mnras/stx1235

  8. [16]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2

  9. [17]

    Harris, W. E. 1996, AJ, 112, 1487 —. 2010, ArXiv e-prints. https://arxiv.org/abs/1012.3224

  10. [18]

    O., Ivanova, N., Engel, M

    Heinke, C. O., Ivanova, N., Engel, M. C., et al. 2013, ApJ, 768, 184, doi: 10.1088/0004-637X/768/2/184

  11. [19]

    O., Zheng, J., Maccarone, T

    Heinke, C. O., Zheng, J., Maccarone, T. J., et al. 2024, arXiv e-prints, arXiv:2407.18867, doi: 10.48550/arXiv.2407.18867

  12. [20]

    W., Bunton, J

    Hotan, A. W., Bunton, J. D., Chippendale, A. P., et al. 2021, PASA, 38, e009, doi: 10.1017/pasa.2021.1

  13. [21]

    Hunter, J. D. 2007, Computing In Science & Engineering, 9, 90, doi: 10.1109/MCSE.2007.55

  14. [22]

    I., Robinson, E

    Hynes, R. I., Robinson, E. L., & Jeffery, E. 2004, ApJL, 608, L101, doi: 10.1086/422471

  15. [23]

    2010, A&A, 519, A13, doi: 10.1051/0004-6361/201014025 in’t Zand, J

    Salvo, T. 2010, A&A, 519, A13, doi: 10.1051/0004-6361/201014025 in’t Zand, J. J. M., Jonker, P. G., & Markwardt, C. B. 2007, A&A, 465, 953, doi: 10.1051/0004-6361:20066678

  16. [24]

    A., Lombardi, Jr., J

    Ivanova, N., Rasio, F. A., Lombardi, Jr., J. C., Dooley, K. L., & Proulx, Z. F. 2005, ApJL, 621, L109, doi: 10.1086/429220

  17. [25]

    2007, PASA, 24, 174, doi: 10.1071/AS07033

    Johnston, S., Bailes, M., Bartel, N., et al. 2007, PASA, 24, 174, doi: 10.1071/AS07033

  18. [26]

    S., Kıro˘ glu, F., et al

    Kremer, K., Ye, C. S., Kıro˘ glu, F., et al. 2022, ApJL, 934, L1, doi: 10.3847/2041-8213/ac7ec4

  19. [27]

    A., Chandler, C

    Lacy, M., Baum, S. A., Chandler, C. J., et al. 2020, PASP, 132, 035001, doi: 10.1088/1538-3873/ab63eb

  20. [28]

    J., Machin, G., McHardy, I

    Lehto, H. J., Machin, G., McHardy, I. M., & Callanan, P. 1990, Nature, 347, 49, doi: 10.1038/347049a0

  21. [29]

    M., Shishkovsky, L., Bult, P

    Ludlam, R. M., Shishkovsky, L., Bult, P. M., et al. 2019, ApJ, 883, 39, doi: 10.3847/1538-4357/ab3806

  22. [30]

    J., McHardy, I

    Machin, G., Lehto, H. J., McHardy, I. M., Callanan, P. J., & Charles, P. A. 1990, MNRAS, 246, 237

  23. [31]

    L., Lenc, E., et al

    McConnell, D., Hale, C. L., Lenc, E., et al. 2020, PASA, 37, e048, doi: 10.1017/pasa.2020.41

  24. [32]

    2010, in Proceedings of the 9th Python in Science Conference, ed

    McKinney, W. 2010, in Proceedings of the 9th Python in Science Conference, ed. S. van der Walt & J. Millman, 51 – 56

  25. [33]

    2003, MNRAS, 345, 1057, doi: 10.1046/j.1365-2966.2003.07017.x

    Merloni, A., Heinz, S., & di Matteo, T. 2003, MNRAS, 345, 1057, doi: 10.1046/j.1365-2966.2003.07017.x

  26. [35]

    A., Miller-Jones, J

    Migliari, S., Tomsick, J. A., Miller-Jones, J. C. A., et al. 2010, ApJ, 710, 117, doi: 10.1088/0004-637X/710/1/117

  27. [36]

    Miller-Jones, J. C. A., Sivakoff, G. R., Heinke, C. O., et al. 2011, The Astronomer’s Telegram, 3378, 1

  28. [37]

    Miller-Jones, J. C. A., Strader, J., Heinke, C. O., et al. 2015, MNRAS, 453, 3918, doi: 10.1093/mnras/stv1869

  29. [38]

    Nelemans, G., & Jonker, P. G. 2010, NewAR, 54, 87, doi: 10.1016/j.newar.2010.09.021

  30. [39]

    A., Rappaport, S

    Nelson, L. A., Rappaport, S. A., & Joss, P. C. 1986, ApJ, 304, 231, doi: 10.1086/164156 10 Dage et al

  31. [40]

    Y., Kim, S., & Giersz, M

    Oh, K., Hong, J., Hui, C. Y., Kim, S., & Giersz, M. 2024, MNRAS, 532, 259, doi: 10.1093/mnras/stae1355

  32. [41]

    Paduano, A., Bahramian, A., Miller-Jones, J. C. A., et al. 2021, MNRAS, doi: 10.1093/mnras/stab1928

  33. [42]

    2021, ApJ, 923, 88, doi: 10.3847/1538-4357/ac2c6b

    Panurach, T., Strader, J., Bahramian, A., et al. 2021, ApJ, 923, 88, doi: 10.3847/1538-4357/ac2c6b

  34. [43]

    2023, ApJ, 946, 88, doi: 10.3847/1538-4357/acc4bf

    Panurach, T., Urquhart, R., Strader, J., et al. 2023, ApJ, 946, 88, doi: 10.3847/1538-4357/acc4bf

  35. [44]

    A., et al

    Partridge, B., L´ opez-Caniego, M., Perley, R. A., et al. 2016, ApJ, 821, 61, doi: 10.3847/0004-637X/821/1/61

  36. [45]

    A., Chandler, C

    Perley, R. A., Chandler, C. J., Butler, B. J., & Wrobel, J. M. 2011, ApJL, 739, L1, doi: 10.1088/2041-8205/739/1/L1

  37. [46]

    M., Bahramian, A., Miller-Jones, J

    Plotkin, R. M., Bahramian, A., Miller-Jones, J. C. A., et al. 2021, MNRAS, 503, 3784, doi: 10.1093/mnras/stab644 R Core Team. 2024, R: A Language and Environment for Statistical Computing, R Foundation for Statistical

  38. [47]

    P., Dhawan, V., & Mioduszewski, A

    Rupen, M. P., Dhawan, V., & Mioduszewski, A. J. 2002, IAUC, 7893, 2

  39. [48]

    P., Mioduszewski, A

    Rupen, M. P., Mioduszewski, A. J., & Dhawan, V. 2005, The Astronomer’s Telegram, 530, 1

  40. [49]

    D., Degenaar, N., van den Eijnden, J., et al

    Russell, T. D., Degenaar, N., van den Eijnden, J., et al. 2021, MNRAS, 508, L6, doi: 10.1093/mnrasl/slab087

  41. [50]

    2020, ApJ, 903, 73, doi: 10.3847/1538-4357/abb880

    Shishkovsky, L., Strader, J., Chomiuk, L., et al. 2020, ApJ, 903, 73, doi: 10.3847/1538-4357/abb880

  42. [51]

    2021, MNRAS, 507, 330, doi: 10.1093/mnras/stab2127

    Stoop, M., van den Eijnden, J., Degenaar, N., et al. 2021, MNRAS, 507, 330, doi: 10.1093/mnras/stab2127

  43. [52]

    Suvorov, A. G. 2021, MNRAS, 503, 5495, doi: 10.1093/mnras/stab825

  44. [53]

    J., Bahramian, A., Wijnands, R., et al

    Tetarenko, A. J., Bahramian, A., Wijnands, R., et al. 2018, ApJ, 854, 125, doi: 10.3847/1538-4357/aaa95a

  45. [54]

    P., et al

    Tremou, E., Corbel, S., Fender, R. P., et al. 2020, MNRAS, 493, L132, doi: 10.1093/mnrasl/slaa019

  46. [55]

    Tudor, V., Miller-Jones, J. C. A., Patruno, A., et al. 2017, MNRAS, 470, 324, doi: 10.1093/mnras/stx1168

  47. [56]

    Tudor, V., Miller-Jones, J. C. A., Strader, J., et al. 2022, MNRAS, doi: 10.1093/mnras/stac1034

  48. [57]

    P., Linares, M., Maitra, D., & van der Klis, M

    Tudose, V., Fender, R. P., Linares, M., Maitra, D., & van der Klis, M. 2009, MNRAS, 400, 2111, doi: 10.1111/j.1365-2966.2009.15604.x van den Eijnden, J., Degenaar, N., Russell, T. D., et al. 2018, Nature, 562, 233–235, doi: 10.1038/s41586-018-0524-1 van den Eijnden, J., Degena...

  49. [58]

    2025, arXiv e-prints, arXiv:2501.06037, doi: 10.48550/arXiv.2501.06037

    Helstrom, L. 2025, arXiv e-prints, arXiv:2501.06037, doi: 10.48550/arXiv.2501.06037

  50. [59]

    E., Ferris, R

    Wilson, W. E., Ferris, R. H., Axtens, P., et al. 2011, MNRAS, 416, 832, doi: 10.1111/j.1365-2966.2011.19054.x

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.