Pith. sign in

REVIEW 4 major objections 6 minor 112 references

New X-ray Supernova Remnants in NGC 7793

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Pith's one-line read Five X-ray sources in NGC 7793 are supernova remnants, four of them newly identified, with two further candidates and a refined X-ray position for SN 2008bk.

desk verdict Four new X-ray SNR candidates in NGC 7793, but three rest on sub-3σ detections; the paper is solid and worth refereeing with modest revisions. read the letter →

arxiv 2506.09120 v1 pith:MCLNSXQY submitted 2025-06-10 astro-ph.HE astro-ph.GA

classification astro-ph.HEastro-ph.GA
keywords X-raysupernovaremnantsNGC7793ChandraobservationsXMM-NewtonspectroscopyhardnessratiosopticalSNRcataloguesSN2008bkSculptorGroup
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 tries to establish that the nearby spiral galaxy NGC 7793, previously known to contain a single X-ray supernova remnant, actually contains at least five. Combining 229.9 ks of archival Chandra observations taken over nineteen years with recent optical supernova-remnant catalogues, the authors identify four new X-ray counterparts to optically known remnants plus the one previously known remnant. The identification is carried by positional coincidences within 1.3 arcseconds, uniformly soft X-ray colours, and the absence of short-term or long-term variability. For two of the remnants, XMM-Newton spectra show hot thermal plasma with oxygen and neon emission lines, so if the claim holds the galaxy's known X-ray remnant population quadruples and the method demonstrates how larger optical catalogues can unlock faint extragalactic X-ray remnants.

What carries the argument

The argument is carried by a cross-matching and screening chain rather than by a single identity. Chandra's sub-arcsecond positions allow X-ray sources to be matched to optical supernova remnants at offsets below 1.3 arcseconds, while X-ray colours—ratios of net counts in soft (0.5–1.2 keV), medium (1.2–2.0 keV), and hard (2.0–7.0 keV) bands, computed with the BEHR Bayesian method—separate soft thermal emitters from hard X-ray binaries and active galactic nuclei. Variability screening with the Gregory–Loredo algorithm (glvary indices below 3) removes compact accreting sources, and for the two brightest remnants combined XMM-Newton MOS spectra fitted with collisionally ionised plasma models (vapec and vpshock) supply the decisive thermal signature: O VII, O VIII, and Ne IX lines from plasma above roughly 2.5 million K.

What would settle it

A deeper or longer X-ray observation that resolves X13, X25, or X38 into a point-like source, or reveals variability or a hard power-law component, would break the SNR interpretation; so would a positional-randomisation test showing that several of the five optical coincidences are expected by chance.

Watch

Extended reading notes

Core claim

The paper's central claim is that five Chandra-detected sources in NGC 7793—X11, X13, X15, X25, and X38—are X-ray supernova remnants, with X15 the previously known remnant and the other four new identifications. The supporting argument has three legs: each source lies within 1.3 arcseconds of an optical supernova remnant from recent catalogues; all five show soft or super-soft X-ray colours with emission concentrated below about 1.2 keV; and none varies on short or long timescales, with variability indices between 0 and 2. For X11 and X15, combined XMM-Newton EPIC MOS spectra (about 1.1 Ms of clean exposure) are thermal and require plasma temperatures above $2.5\times10^6$ K, with strong O VII, O VIII, and Ne IX K-shell lines; for the fainter X13, X25, and X38 the classification rests on the optical coincidence plus the soft, non-variable colours. The paper also proposes X23 and X42 as candidate X-ray SNRs without optical counterparts, and reports faint X-ray emission from SN 2008bk with a position refined to about 1.7 arcseconds from the earlier reported value.

Load-bearing premise

The identification of X13, X25, and X38 as X-ray supernova remnants rests on the assumption that the optical objects they coincide with are genuine supernova remnants and that the faint X-ray sources are not unrelated foreground stars, X-ray binaries, or background active galactic nuclei projected by chance onto those remnants.

Editorial extensions

If this is right

  • The known X-ray supernova remnant population of NGC 7793 grows from one to five, giving a same-galaxy sample for comparing X-ray and optical remnant properties.
  • Because the four new remnants are soft (below 1.2 keV) and non-variable, colour and variability screening can be used to find more X-ray supernova remnants in other galaxies that have optical SNR catalogues.
  • The XMM-Newton spectra of X11 and X15 place these remnants in an old, hot-plasma phase, with temperatures above $2.5\times10^6$ K and strong oxygen and neon lines, showing that their X-ray emission is thermal rather than powered by an embedded compact object.
  • The reported tendency for higher-density remnants to be more X-ray luminous and softer, if confirmed on a larger sample, would tie remnant X-ray output to the local interstellar medium density.
  • The refined X-ray position of SN 2008bk, about 1.7 arcseconds from the published position, gives a target for future late-time monitoring of the supernova's interaction with its circumstellar medium.

Reading between the lines

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

  • Beyond the paper: re-running the same optical-catalogue cross-match on archival X-ray images of other Sculptor Group spirals could reveal a similarly hidden population of X-ray supernova remnants.
  • A testable extension would be to obtain deeper X-ray spectra of the two candidates without optical counterparts, X23 and X42; oxygen and neon lines like those in X11 and X15 would strengthen the SNR interpretation, while a power-law or iron-rich spectrum would point to a binary or background active nucleus.
  • If the density–luminosity relation hinted at by four remnants is real and follows the quadratic shock-emissivity scaling, X-ray supernova remnants could serve as interstellar-medium density probes, but the paper's own p-values (0.34 and 0.14) show that a much larger sample would be needed before using them that way.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper analyzes all archival Chandra observations of NGC 7793 (229.9 ks across 2003–2022), detects 58 X-ray sources, and identifies five X-ray counterparts to optical SNRs (X11, X13, X15, X25, X38), four of which are claimed as newly identified X-ray SNRs. The authors use XMM-Newton combined EPIC MOS spectra to model three sources (X11, X15, X42), reporting soft thermal plasma emission with O VII, O VIII, and Ne IX lines, and they propose two candidate X-ray SNRs without optical counterparts (X23, X42). The paper also reports X-ray emission from SN 2008bk, refines its position, and explores correlations between X-ray luminosity, softness, and optical densities, comparing the results with other nearby galaxies.

Significance. If the identifications are secure, the paper would meaningfully expand the small sample of extragalactic X-ray SNRs with optical counterparts, taking advantage of a large optical SNR catalog (Kopsacheili et al. 2021, 2024) and all available archival X-ray data. Strengths include the systematic reduction of six Chandra observations, the use of BEHR for low-count Bayesian hardness ratios, the public presentation of a complete source catalog, and the extraction of XMM-Newton spectra for two sources—an important step beyond color selection. The detection of X-ray emission from SN 2008bk and the refined astrometry are also useful contributions. However, the central claim hinges on three sources with broad-band S/N below 3, a cross-match without a chance-coincidence estimate, and a spectral interpretation that is overstated for at least one source, so the significance of the five-SNR claim is not yet fully established.

major comments (4)
  1. [Table A.1 and §4.3] Three of the four newly claimed X-ray SNRs (X13, X25, X38) have broad-band S/N below 3 (1.23, 2.43, 2.14, respectively), and X13 is also marginal in the soft band (S/N = 1.12). The classification in §4.3 rests on positional coincidence, soft colors, and non-variability, but a source that is not a secure X-ray detection cannot be robustly classified as an X-ray SNR. Please either provide a stacked or combined-analysis detection significance for these sources, or reclassify them as candidate X-ray SNRs and adjust the abstract and conclusions (which currently state 'five X-ray SNRs') accordingly.
  2. [§4.1.1 and §4.3] The paper does not compute the probability of chance coincidence for the 0.4–1.3 arcsec matches between the 58 X-ray sources and the ~238-candidate optical SNR catalog. Given the surface density of X-ray sources and optical SNRs in the field, the expected number of random alignments within 1.3 arcsec should be quantified. Without this estimate, the three marginal detections could be unrelated foreground stars, X-ray binaries, or background AGN projected near optical SNRs. Please add a quantitative chance-coincidence calculation (e.g., using Poisson or binomial statistics based on the local source densities) and discuss its implications for the identification of X13, X25, and X38.
  3. [Abstract, §4.5, Table 3] The claim that the X-ray spectra of X11 and X15 show temperatures 'exceeding 2.5 million K' is inconsistent with the best-fit parameters: X11 has kT = 0.13 ± 0.04 keV, which corresponds to ~1.5 × 10^6 K, and X15 requires a soft component <0.12 keV plus a harder component at 0.78 keV, so no single fitted temperature exceeds 2.5 × 10^6 K. The statement presumably derives from the presence of O VII/Ne IX line emission, but as written it overstates the spectral fits. Please revise the abstract, §4.5, and the conclusions to quote the fitted temperatures and clarify the line-based temperature constraint.
  4. [§4.6 and Table 4] The luminosities and the density–luminosity/softness correlations for X13, X25, and X38 are computed assuming kT = 0.5 keV and NH = 0.504 × 10^22 cm^-2, values that are not constrained for these sources, which are too faint for spectral fitting as stated in §4.5. The resulting correlation analysis (Table 5) has p-values of 0.40 and 0.14, so the abstract's statement that 'a correlation between density, X-ray luminosity, and source softness was observed' is overstated. Please present these luminosities with the model-assumption caveat and describe the correlations as tentative.
minor comments (6)
  1. [Table B.1] The entries for X43 and X44 are identical, including the coordinates and the 'Other Surveys' column; please check whether X44 is a genuinely new source and correct the table.
  2. [Table 6] The optical SNR count for NGC 7793 is listed as '551d'; given that note (1d) refers to Kopsacheili et al. (2021), this appears to be a typo for '55', and the text elsewhere cites ~238 SNRs from Kopsacheili et al. (2024). Please reconcile the count.
  3. [§4.6] The sentence 'We chose only X11 and X15 (and not X42) because they exhibit optical emission, and we expect similar properties for the also optically detected X13, X25, and X38' is confusing because X13, X25, and X38 also exhibit optical emission; the intended point is that only X11 and X15 have spectral fits. Please rephrase.
  4. [§4.5] There is a typo: 'an two models' should read 'with two models'.
  5. [§4.6] The description of the weighted Pearson test contains a grammatical error: 'and weighted' should be 'which is weighted'.
  6. [§4.3] The phrase 'new, X-ray SNRs' has an unnecessary comma; please rephrase.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; X-ray SNR claims rest on independent X-ray data, with prior optical catalogs used only as non-load-bearing cross-matching inputs.

full rationale

The derivation chain is self-contained. The X-ray source catalog is built from Chandra archival data using standard tools (wavdetect, dmextract, BEHR), and SNR classification is then checked by positional coincidence with optical SNR catalogs. Those catalogs (Kopsacheili et al. 2021, 2024) are prior publications by the first author, so there is self-citation, but they are constructed from optical emission-line diagnostics independent of the X-ray data, and the paper shows optical spectra for three counterparts (Fig. 6). The X-ray sources are independently detected, and their soft colors, non-variability, and, for X11/X15/X42, XMM-Newton spectra are measured from the X-ray data themselves; no equation or fitting step defines the X-ray detection or classification in terms of the claimed result, and no fitted parameter is renamed as a prediction. The abstract's 'exceeding 2.5 million K' is inconsistent with the Table 3 best-fit kT = 0.13 keV for X11, but that is a correctness/consistency concern, not a circularity.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim rests on the accuracy of the optical SNR catalogs (the authors' own prior work) and on the assumption that positional coincidence implies physical association. Flux and luminosity values for three of the five SNRs depend on assumed kT and NH values, and the spectral fits for X11, X15, and X42 include several fitted parameters. The paper introduces no new physical entities.

free parameters (4)
  • Assumed plasma temperature for flux conversion = kT=0.5 keV
    Used in Section 4.6 to convert Chandra count rates to fluxes for X13, X25, and X38, which lack spectral fits. This is a typical SNR value from Leonidaki et al. 2010, but it directly affects the quoted luminosities and the density-luminosity correlation.
  • Assumed absorbing column for flux conversion = NH=0.504e22 cm^-2
    Average of X11 and X15 total NH values, applied to X13, X25, and X38 in Section 4.6. The systematic uncertainty from this assumption is not propagated into the Table 4 error bars.
  • Local absorption NH in spectral fits = X11: 0.48e22, X15: 0.46e22, X42: 0.21e22 cm^-2
    Free parameters in the tbabs x phabs x vapec models fitted in Section 4.5. They constrain local ISM absorption toward each remnant but are not independently predicted.
  • X42 Ne and Mg abundances and Gaussian line = Ne=2.21, Mg=2.94, LineE=1.03 keV
    Abundances of Ne and Mg were thawed and a Gaussian line was added to improve the fit for X42 (Table 3). These are fitted parameters, not predictions.
assumptions (5)
  • domain assumption The optical SNR catalogs of Kopsacheili et al. (2021, 2024) used for counterpart matching are correct, e.g., that [SII]/Halpha>0.4 and multi-line diagnostics identify true SNRs.
    The X-ray SNR classification depends on the optical identifications. If some optical SNRs are misclassified H II regions or super-bubbles, the X-ray counterparts would not be SNRs. Invoked in Sections 1 and 4.1.1.
  • domain assumption X-ray emission within 1.3 arcsec of an optical SNR originates from the SNR itself, not from a chance-aligned foreground star, X-ray binary, or background AGN.
    Used to assign X11, X13, X25, and X38 as X-ray SNRs in Section 4.3. No chance-coincidence probability is computed, and the Chandra PSF is about 1 arcsec.
  • domain assumption Soft X-ray colors and lack of variability are sufficient to classify a Chandra source as an SNR when an optical counterpart exists.
    Standard SNR diagnostics used in Sections 4.2 and 4.3. Some X-ray binaries and foreground stars can also be soft and non-variable.
  • domain assumption The tbabs x phabs absorption model and the vapec/vpshock plasma models describe the SNR emission.
    Used in the XSPEC spectral fits in Section 4.5. These are standard in the field but model-dependent.
  • domain assumption The Galactic column density toward NGC 7793 is 3.4e20 cm^-2.
    Fixed in the spectral fits based on the weighted average absorption toward NGC 7793, as stated in Section 4.5.

how reviews work

0 comments
Cite this review

Pith. "Pith review of New X-ray Supernova Remnants in NGC 7793." pith.science (2026). https://pith.science/paper/MCLNSXQY

@misc{pith2026250609120,
  author       = {Pith},
  title        = {Pith review of: New X-ray Supernova Remnants in NGC 7793},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MCLNSXQY}},
  note         = {Machine review of arXiv:2506.09120}
}
abstract

This work focuses on the detection of X-ray Supernova Remnants (SNRs) in the galaxy NGC 7793 and the study of their properties. X-ray SNRs in galaxies beyond the Local Group are rare, mainly due to the limited sensitivity of current X-ray instruments. Additionally, their identification requires an optical counterpart, making incomplete optical identification methods an extra challenge. Detecting X-ray SNRs in other galaxies is crucial for understanding their feedback in different evolutionary phases and gaining insights into their local interstellar medium. In NGC 7793, only one X-ray SNR was previously known, while a recent study reported nearly 240 optical SNRs. The discovery of a new, larger optical SNR sample motivated a re-examination of the X-ray SNR population by comparing optical SNRs with X-ray sources. To identify X-ray SNRs, we utilised Chandra's spatial resolution and analysed all available archival data of NGC 7793, totaling 229.9 ks over 19 years. After data reduction, we performed source detection and analysis, searching for X-ray sources coinciding with optical SNRs. We also used XMM-Newton for spectral analysis of the confirmed and candidate SNRs. We detected 58 X-ray sources down to an observed luminosity of $\sim 1.5\times 10^{36}\, erg\, s^{-1}$. Among them, five X-ray counterparts to optical SNRs were identified, all presenting soft emission (<1.2 keV) with no short- or long-term variability. One corresponds to the previously known X-ray SNR, while four are newly detected. Spectral modeling of two SNRs shows thermal spectra exceeding 2.5 million K, with strong OVII, OVIII, and NeIX emission lines. A correlation between density, X-ray luminosity, and source softness was observed. We also report X-ray emission from supernova 2008bk, refining its position, and suggest two candidate X-ray SNRs with soft, non-variable spectra, one resembling the identified X-ray SNRs.

Figures

Figures reproduced from arXiv: 2506.09120 by the authors.

Figure 1
Figure 1. Composite X-ray optical image of NGC 7793 consisting of: Hα + [N II] (red), soft X-ray (0.5 - 1.2 keV; green), and medium + hard X-ray (1.2 - 7.0 keV; blue) of the OBSID 3954. All circles indicate the X-ray detected sources. The orange circles show the X-ray sources that coincide with optical SNRs that have been identified in Kopsacheili et al. (2021) and Kopsacheili et al. (2024) [PITH_FULL_IMAGE:figures/full_fig_… view at source ↗
Figure 2
Figure 2. The source X20 is the Supernova 2008bk. In the left and middle panels we see the Chandra images from 2003 (OBSID: 3954) and 2011 (OBSID: 14231) respectively in the broad band (0.5 - 7 keV) and on the right the continuum subtracted Hα+[N ii] image from Blanco 4m telescope (MOSAIC II camera) obtained in 2011. In all figures the radius of the circle is 3.5 arcsec. 4.1.1. X-ray sources known or suspected as Supernova Re… view at source ↗
Figure 3
Figure 3. The optical counterpart of the source X14 in Hα + [N ii] (left) and [S ii] (right). The optical images were obtained from Blanco 4m telescope (MOSAIC II camera) in 2011. SAIC II camera in Blanco 4 meter telescope (Chile) are pre￾sented. The [S ii]/Hα ratio is ∼ 0.15 (calculated using the pho￾tometry presented in Kopsacheili et al. 2021) and it does not satisfy the traditional diagnostic for being a SNR, according to… view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: The log10(S/M)−log10(S/H), log10(M/H)−log10(S/M), and log10(M/H)−log10(S/H) X-ray colors for all the X-ray detected sources with S/N > 1, detected in this work. The orange squares correspond to the X-ray sources that coincide with optical SNRs. 4.4. Candidate X-ray SNR…
Figure 5
Figure 5. Figure 5: Combined EPIC MOS spectra and best-fit models for sources X11, X15 and X42. The spectra of X11 and X42 are fitted with one temperature thermal plasma model. The spectrum of X15 is fitted with two temperature thermal plasma components shown with red dashed and blue dott…
Figure 6
Figure 6. Figure 6: The optical spectra of the NGC7793_SNR_91, NGC7793_SNR_126, NGC7793_SNR_136, optical SNRs that spatially coincide with the X-ray sources X11, X13, X15 of this study [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: Left: The X-ray count rate in the broad band (0.5-7 keV), of the X-ray SNRs X11, X13, X15, and X38, as function of their density, calculated using the [S ii]6717/31 emission line ratio. Right: The log10(S/H) color of the same sources, as function of the density. In bot…
Figure 8
Figure 8. Figure 8: The absorption corrected Chandra X-ray luminosity in the broad band (0.5-7 keV), of the X-ray SNRs suggested in this study, as a func￾tion of their Hα luminosity. The purpose of this comparison is to estimate the expected number of X-ray SNRs in NGC 7793 and to identif…
Figure 9
Figure 9. Figure 9: Number of X-ray SNRs, counterparts of optical SNRs with [S ii]/Hα > 0.4, as a function of their Hα star formation rate (SFR). The colorbar indicates the distance of the galaxies. Article number, page 10 of 16 [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]
Figure 10
Figure 10. Figure 10: Number of X-ray SNRs, counterparts of optical SNRs with [S ii]/Hα > 0.4, versus the number of optical SNRs 5. Conclusions In this study, we analyze all archival Chandra observations of the galaxy NGC 7793 to detect X-ray supernova remnants (SNRs), in addition to the s…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

112 extracted references · 43 canonical work pages

  1. [1]

    V., 2022, MNRAS, 514, 728

    Albert C., Dwarkadas V. V., 2022, MNRAS, 514, 728

  2. [2]

    C., Locatelli N., Haberl F., Morris M

    Anastasopoulou K., Ponti G., Sormani M. C., Locatelli N., Haberl F., Morris M. R., Churazov E. M., et al., 2023, A&A, 671, A55. doi:10.1051/0004-6361/202245001

  3. [3]

    A., 1996, ASPC, 101, 17

    Arnaud K. A., 1996, ASPC, 101, 17

  4. [4]

    P., & Long, K

    Blair, W. P., & Long, K. S.\ 1997, , 108, 261

  5. [5]

    doi:10.1093/mnras/stac412

    Boumis P., Chiotellis A., Fragkou V., Akras S., Derlopa S., Kopsacheili M., Leonidaki I., et al., 2022, MNRAS, 512, 1658. doi:10.1093/mnras/stac412

  6. [6]

    D., Bruni G., Cann J

    Brightman M., Hameury J.-M., Lasota J.-P., Baldi R. D., Bruni G., Cann J. M., Earnshaw H., et al., 2023, ApJ, 951, 51. doi:10.3847/1538-4357/acd18a

  7. [7]

    M., Filipovi \'c M

    Bozzetto L. M., Filipovi \'c M. D., Vukoti \'c B., Pavlovi \'c M. Z., Uro s evi \'c D., Kavanagh P. J., Arbutina B., et al., 2017, ApJS, 230, 2

  8. [8]

    D., Sun M., Li J.-

    Chen Y., Seward F. D., Sun M., Li J.-. tao ., 2008, ApJ, 676, 1040. doi:10.1086/525240

Show all 112 references
  1. [9]

    A., 1975, ApJ, 200, 698

    Chevalier R. A., 1975, ApJ, 200, 698. doi:10.1086/153840

  2. [10]

    A., 1982, ApJ, 259, 302

    Chevalier R. A., 1982, ApJ, 259, 302

  3. [11]

    Clementini G., Contreras Ramos R., Federici L., Macario G., Beccari G., Testa V., Cignoni M., et al., 2011, ApJ, 743, 19

  4. [12]

    F., McKee C

    Cioffi D. F., McKee C. F., Bertschinger E., 1988, ApJ, 334, 252. doi:10.1086/166834

  5. [13]

    M.-A., 2024, MNRAS, 531, 5109

    Chiotellis A., Zapartas E., Meyer D. M.-A., 2024, MNRAS, 531, 5109

  6. [14]

    M., Khabibullin I

    Churazov E. M., Khabibullin I. I., Bykov A. M., Chugai N. N., Sunyaev R. A., Zinchenko I. I., 2021, MNRAS, 507, 971. doi:10.1093/mnras/stab2125

  7. [15]

    M., Khabibullin I

    Churazov E. M., Khabibullin I. I., Bykov A. M., Chugai N. N., Sunyaev R. A., Zinchenko I. I., 2022, MNRAS, 513, L83. doi:10.1093/mnrasl/slac039

  8. [16]

    S., S \'a nchez S

    Cid Fernandes R., Carvalho M. S., S \'a nchez S. F., de Amorim A., Ruschel-Dutra D., 2021, MNRAS, 502, 1386. doi:10.1093/mnras/stab059

  9. [17]

    P., Shelton R

    Cox D. P., Shelton R. L., Maciejewski W., Smith R. K., Plewa T., Pawl A., R \'o \.z yczka M., 1999, ApJ, 524, 179. doi:10.1086/307781

  10. [18]

    doi:10.1086/158441

    Davoust E., de Vaucouleurs G., 1980, ApJ, 242, 30. doi:10.1086/158441

  11. [19]

    Della Bruna L., Adamo A., Bik A., Fumagalli M., Walterbos R., \"O stlin G., Bruzual G., et al., 2020, A&A, 635, A134

  12. [20]

    P., Kuncarayakti H., Fox O

    Dessart L., Guti \'e rrez C. P., Kuncarayakti H., Fox O. D., Filippenko A. V., 2023, A&A, 675, A33

  13. [21]

    A., Payne J

    Dopita M. A., Payne J. L., Filipovi \'c M. D., Pannuti T. G., 2012, MNRAS, 427, 956. doi:10.1111/j.1365-2966.2012.21947.x

  14. [22]

    T., 2011, piim.book

    Draine B. T., 2011, piim.book

  15. [23]

    V., 2005, ApJ, 630, 892

    Dwarkadas V. V., 2005, ApJ, 630, 892. doi:10.1086/432109

  16. [24]

    N., Primini F

    Evans I. N., Primini F. A., Glotfelty K. J., Anderson C. S., Bonaventura N. R., Chen J. C., Davis J. E., et al., 2010, ApJS, 189, 37. doi:10.1088/0067-0049/189/1/37

  17. [25]

    A., Drechsler M., Weil K

    Fesen R. A., Drechsler M., Weil K. E., Strottner X., Raymond J. C., Rupert J., Milisavljevic D., et al., 2021, ApJ, 920, 90. doi:10.3847/1538-4357/ac0ada

  18. [26]

    V., 1997, ARA&A, 35, 309

    Filippenko A. V., 1997, ARA&A, 35, 309

  19. [27]

    E., Kashyap V., Rosner R., Lamb D

    Freeman P. E., Kashyap V., Rosner R., Lamb D. Q., 2002, ApJS, 138, 185. doi:10.1086/324017

  20. [28]

    C., Allen G

    Fruscione A., McDowell J. C., Allen G. E., Brickhouse N. S., Burke D. J., Davis J. E., Durham N., et al., 2006, SPIE, 6270, 62701V. doi:10.1117/12.671760

  21. [29]

    D., Haberl F., Winkler P

    Filipovi \'c M. D., Haberl F., Winkler P. F., Pietsch W., Payne J. L., Crawford E. J., de Horta A. Y., et al., 2008, A&A, 485, 63. doi:10.1051/0004-6361:200809642

  22. [30]

    Galiullin I., Gilfanov M., 2021, A&A, 646, A85

  23. [31]

    J., Filipovic M

    Galvin T. J., Filipovic M. D., 2014, SerAJ, 189, 15

  24. [32]

    P., 1997, AAS

    Garmire G. P., 1997, AAS

  25. [33]

    F., Plucinsky P

    Garofali K., Williams B. F., Plucinsky P. P., Gaetz T. J., Wold B., Haberl F., Long K. S., et al., 2017, MNRAS, 472, 308

  26. [34]

    Groenewegen M. A. T., 2013, A&A, 550, A70

  27. [35]

    V., Kniazev A

    Gvaramadze V. V., Kniazev A. Y., Gallagher J. S., Oskinova L. M., Chu Y.-H., Gruendl R. A., Katkov I. Y., 2021, MNRAS, 503, 3856. doi:10.1093/mnras/stab679

  28. [36]

    J., Buckley D

    Haberl F., Sturm R., Ballet J., Bomans D. J., Buckley D. A. H., Coe M. J., Corbet R., et al., 2012, A&A, 545, A128. doi:10.1051/0004-6361/201219758

  29. [37]

    doi:10.1051/0004-6361/201629178

    HI4PI Collaboration, Ben Bekhti N., Flöer L., Keller R., Kerp J., Lenz D., Winkel B., et al., 2016, A&A, 594, A116. doi:10.1051/0004-6361/201629178

  30. [38]

    W., 1969, ApJS, 18, 73

    Hodge P. W., 1969, ApJS, 18, 73. doi:10.1086/190185

  31. [39]

    C., Lee J

    Kennicutt R. C., Lee J. C., Funes J. G., J. S., Sakai S., Akiyama S., 2008, ApJS, 178, 247

  32. [40]

    doi:10.1093/mnras/stz2594

    Kopsacheili M., Zezas A., Leonidaki I., 2020, MNRAS, 491, 889. doi:10.1093/mnras/stz2594

  33. [41]

    doi:10.1093/mnras/stab2395

    Kopsacheili M., Zezas A., Leonidaki I., Boumis P., 2021, MNRAS, 507, 6020. doi:10.1093/mnras/stab2395

  34. [42]

    doi:10.1093/mnras/stac1415

    Kopsacheili M., Zezas A., Leonidaki I., 2022, MNRAS, 514, 3260. doi:10.1093/mnras/stac1415

  35. [43]

    doi:10.1093/mnras/stae874

    Kopsacheili M., Jim \'e nez-Palau C., Galbany L., Boumis P., Gonz \'a lez-D \' az R., 2024, MNRAS, 530, 1078. doi:10.1093/mnras/stae874

  36. [44]

    A., Kruijssen J

    Kreckel K., Groves B., Bigiel F., Blanc G. A., Kruijssen J. M. D., Hughes A., Schruba A., et al., 2017, ApJ, 834, 174

  37. [45]

    G., Kim M., Sarajedini A., Geisler D., Gieren W., 2002, ApJ, 565, 959

    Lee M. G., Kim M., Sarajedini A., Geisler D., Gieren W., 2002, ApJ, 565, 959

  38. [46]

    C., Gil de Paz A., Tremonti C., Kennicutt R

    Lee J. C., Gil de Paz A., Tremonti C., Kennicutt R. C., Salim S., Bothwell M., Calzetti D., et al., 2009, ApJ, 706, 599. doi:10.1088/0004-637X/706/1/599

  39. [47]

    H., Lee M

    Lee J. H., Lee M. G., 2014, ApJ, 786, 130

  40. [48]

    H., Lee M

    Lee J. H., Lee M. G., 2014, ApJ, 793, 134

  41. [49]

    Lelli F., Verheijen M., Fraternali F., 2014, A&A, 566, A71

  42. [50]

    doi:10.1088/0004-637X/725/1/842

    Leonidaki I., Zezas A., Boumis P., 2010, ApJ, 725, 842. doi:10.1088/0004-637X/725/1/842

  43. [51]

    doi:10.1093/mnras/sts324

    Leonidaki I., Boumis P., Zezas A., 2013, MNRAS, 429, 189. doi:10.1093/mnras/sts324

  44. [52]

    D., Filippenko A

    Li W., van Dyk S. D., Filippenko A. V., Foley R. J., Pignata G., Hamuy M., Moza J., et al., 2008, ATel, 1448, 1

  45. [53]

    S., Blair W

    Long K. S., Blair W. P., Winkler P. F., Becker R. H., Gaetz T. J., Ghavamian P., Helfand D. J., et al., 2010, ApJS, 187, 495

  46. [54]

    S., Kuntz K

    Long K. S., Kuntz K. D., Blair W. P., Godfrey L., Plucinsky P. P., Soria R., Stockdale C., et al., 2014, ApJS, 212, 21

  47. [55]

    S., Winkler P

    Long K. S., Winkler P. F., Blair W. P., 2019, ApJ, 875, 85

  48. [56]

    D., Vukoti \'c B., Ballet J., Haberl F., Maitra C., Kavanagh P., et al., 2019, A&A, 631, A127

    Maggi P., Filipovi \'c M. D., Vukoti \'c B., Ballet J., Haberl F., Maitra C., Kavanagh P., et al., 2019, A&A, 631, A127

  49. [57]

    J., Fransson C., Pastorello A., Benetti S., Valenti S., et al., 2012, MNRAS, 420, 3451

    Maguire K., Jerkstrand A., Smartt S. J., Fransson C., Pastorello A., Benetti S., Valenti S., et al., 2012, MNRAS, 420, 3451

  50. [58]

    M., Fesen R

    Matonick D. M., Fesen R. A., 1997, ApJS, 112, 49

  51. [59]

    R., Mattila S., Ramirez-Ruiz E., Eldridge J

    Maund J. R., Mattila S., Ramirez-Ruiz E., Eldridge J. J., 2014, MNRAS, 438, 1577. doi:10.1093/mnras/stt2296

  52. [60]

    S., & Clarke, J

    Mathewson, D. S., & Clarke, J. N.\ 1973, , 180, 725

  53. [61]

    J., Eldridge J

    Mattila S., Smartt S. J., Eldridge J. J., Maund J. R., Crockett R. M., Danziger I. J., 2008, ApJL, 688, L91

  54. [62]

    McQuinn K. B. W., Skillman E. D., Dolphin A. E., Berg D., Kennicutt R., 2017, AJ, 154, 51

  55. [63]

    A., 2013, ApJ, 772, 134

    Milisavljevic D., Fesen R. A., 2013, ApJ, 772, 134

  56. [64]

    doi:10.1111/j.1365-2966.2011.19862.x

    Mineo S., Gilfanov M., Sunyaev R., 2012, MNRAS, 419, 2095. doi:10.1111/j.1365-2966.2011.19862.x

  57. [65]

    K., Sagar R., Lewin W

    Misra K., Pooley D., Chandra P., Bhattacharya D., Ray A. K., Sagar R., Lewin W. H. G., 2007, MNRAS, 381, 280

  58. [66]

    G., Levine S

    Monet D. G., Levine S. E., Canzian B., Ables H. D., Bird A. R., Dahn C. C., Guetter H. H., et al., 2003, AJ, 125, 984. doi:10.1086/345888

  59. [67]

    W., Gris \'e F., Soria R., 2011, AN, 332, 367

    Motch C., Pakull M. W., Gris \'e F., Soria R., 2011, AN, 332, 367. doi:10.1002/asna.201011501

  60. [68]

    L., Swartz D

    O'Dell S. L., Swartz D. A., Tice N. W., Plucinsky P. P., Grant C. E., Marshall H. L., Vikhlinin A. A., et al., 2015, SPIE, 9601, 960107. doi:10.1117/12.2188396

  61. [69]

    E., Ferland G

    Osterbrock D. E., Ferland G. J., 2006, agna.book

  62. [70]

    W., Soria R., Motch C., 2010, Natur, 466, 209

    Pakull M. W., Soria R., Motch C., 2010, Natur, 466, 209. doi:10.1038/nature09168

  63. [71]

    V., Leonidaki I., Kopsacheili M., 2022, MNRAS.tmp

    Palaiologou E. V., Leonidaki I., Kopsacheili M., 2022, MNRAS.tmp. doi:10.1093/mnras/stac1599

  64. [72]

    G., Duric N., Lacey C

    Pannuti T. G., Duric N., Lacey C. K., Ferguson A. M. N., Magnor M. A., Mendelowitz C., 2002, ApJ, 565, 966

  65. [73]

    G., Schlegel E

    Pannuti T. G., Schlegel E. M., Lacey C. K., 2007, AJ, 133, 1361. doi:10.1086/510718

  66. [74]

    K., Staggs W

    Pannuti T., Chomiuk L., Grimes C. K., Staggs W. D., Tussey J. M., Laine S., Schlegel E., 2011, AAS

  67. [75]

    G., Schlegel E

    Pannuti T. G., Schlegel E. M., Filipovi \'c M. D., Payne J. L., Petre R., Harrus I. M., Staggs W. D., et al., 2011, AJ, 142, 20

  68. [76]

    L., Siemiginowska A., van Dyk D

    Park T., Kashyap V. L., Siemiginowska A., van Dyk D. A., Zezas A., Heinke C., Wargelin B. J., 2006, ApJ, 652, 610. doi:10.1086/507406

  69. [77]

    B., Pilecki B., Karczmarek P., et al., 2019, Natur, 567, 200

    Pietrzy \'n ski G., Graczyk D., Gallenne A., Gieren W., Thompson I. B., Pilecki B., Karczmarek P., et al., 2019, Natur, 567, 200

  70. [78]

    H., Irwin J

    Prestwich A. H., Irwin J. A., Kilgard R. E., Krauss M. I., Zezas A., Primini F., Kaaret P., et al., 2003, ApJ, 595, 719

  71. [79]

    Pooley D., Lewin W. H. G., Fox D. W., Miller J. M., Lacey C. K., Van Dyk S. D., Weiler K. W., et al., 2002, ApJ, 572, 932

  72. [80]

    doi:10.1086/114698

    Puche D., Carignan C., 1988, AJ, 95, 1025. doi:10.1086/114698

  73. [81]

    J., de Jong R

    Radburn-Smith D. J., de Jong R. S., Seth A. C., Bailin J., Bell E. F., Brown T. M., Bullock J. S., et al., 2011, ApJS, 195, 18

  74. [82]

    C., 1979, ApJS, 39, 1

    Raymond J. C., 1979, ApJS, 39, 1

  75. [83]

    M., Pietsch W., 1999, A&A, 341, 8

    Read A. M., Pietsch W., 1999, A&A, 341, 8

  76. [84]

    M., Hester J

    Rho J., Petre R., Schlegel E. M., Hester J. J., 1994, ApJ, 430, 757. doi:10.1086/174446

  77. [85]

    Rodr \' guez \'O ., Clocchiatti A., Hamuy M., 2014, AJ, 148, 107

  78. [86]

    D., White R

    Russell T. D., White R. L., Long K. S., Blair W. P., Soria R., Winkler P. F., 2020, MNRAS, 495, 479. doi:10.1093/mnras/staa1177

  79. [87]

    Sabbi E., Calzetti D., Ubeda L., Adamo A., Cignoni M., Thilker D., Aloisi A., et al., 2018, ApJS, 235, 23

  80. [88]

    doi:10.1086/306803

    Safi-Harb S., Petre R., 1999, ApJ, 512, 784. doi:10.1086/306803

  81. [89]

    Sasaki M., Zangrandi F., Filipovi \'c M., Alsaberi R. Z. E., Collier J. D., Haberl F., Heywood I., et al., 2025, A&A, 693, L15

  82. [90]

    J., Blair W

    Sasaki M., Gaetz T. J., Blair W. P., Edgar R. J., Morse J. A., Plucinsky P. P., Smith R. K., 2006, ApJ, 642, 260. doi:10.1086/500789

  83. [91]

    Sasaki M., Pietsch W., Haberl F., Hatzidimitriou D., Stiele H., Williams B., Kong A., et al., 2012, A&A, 544, A144

  84. [92]

    L., Cox D

    Shelton R. L., Cox D. P., Maciejewski W., Smith R. K., Plewa T., Pawl A., R \'o \.z yczka M., 1999, ApJ, 524, 192. doi:10.1086/307799

  85. [93]

    K., Brickhouse N

    Smith R. K., Brickhouse N. S., Liedahl D. A., Raymond J. C., 2001, ApJL, 556, L91. doi:10.1086/322992

  86. [94]

    u z A., Balman S ., \

    Sonba s E., Aky \"u z A., Balman S ., \"O zel M. E., 2010, A&A, 517, A91

  87. [95]

    W., Broderick J

    Soria R., Pakull M. W., Broderick J. W., Corbel S., Motch C., 2010, MNRAS, 409, 541. doi:10.1111/j.1365-2966.2010.17360.x

  88. [96]

    A., Sandage A., Reindl B., 2008, A&ARv, 15, 289

    Tammann G. A., Sandage A., Reindl B., 2008, A&ARv, 15, 289

  89. [97]

    C., Patnaude D., Murray E., Ghavamian P., Renzo M., et al., 2022, ApJ, 932, 26

    Temim T., Slane P., Raymond J. C., Patnaude D., Murray E., Ghavamian P., Renzo M., et al., 2022, ApJ, 932, 26. doi:10.3847/1538-4357/ac6bf4

  90. [98]

    B., Courtois H

    Tully R. B., Courtois H. M., Dolphin A. E., Fisher J. R., H \'e raudeau P., Jacobs B. A., Karachentsev I. D., et al., 2013, AJ, 146, 86

  91. [99]

    D., Davidge T

    Van Dyk S. D., Davidge T. J., Elias-Rosa N., Taubenberger S., Li W., Levesque E. M., Howerton S., et al., 2012, AJ, 143, 19. doi:10.1088/0004-6256/143/1/19

  92. [100]

    D., 2013, AJ, 146, 24

    Van Dyk S. D., 2013, AJ, 146, 24. doi:10.1088/0004-6256/146/2/24

  93. [101]

    doi:10.1007/s00159-011-0049-1

    Vink J., 2012, A&ARv, 20, 49. doi:10.1007/s00159-011-0049-1

  94. [102]

    Vink J., 2020, pesr.book

  95. [103]

    C., Tananbaum H

    Weisskopf M. C., Tananbaum H. D., Van Speybroeck L. P., O'Dell S. L., 2000, SPIE, 4012, 2. doi:10.1117/12.391545

  96. [104]

    L., Long K

    White R. L., Long K. S., 1991, ApJ, 373, 543. doi:10.1086/170073

  97. [105]

    J., Katsuda S., Cumbee R., Petre R., Raymond J

    Williams B. J., Katsuda S., Cumbee R., Petre R., Raymond J. C., Uchida H., 2020, ApJL, 898, L51

  98. [106]

    doi:10.1086/317016

    Wilms J., Allen A., McCray R., 2000, ApJ, 542, 914. doi:10.1086/317016

  99. [107]

    F., Coffin S

    Winkler P. F., Coffin S. C., Blair W. P., Long K. S., Kuntz K. D., 2021, ApJ, 908, 80

  100. [108]

    D., Stupar M., Points S

    Yew M., Filipovi \'c M. D., Stupar M., Points S. D., Sasaki M., Maggi P., Haberl F., et al., 2021, MNRAS, 500, 2336

  101. [109]

    D., Haberl F., Kavanagh P., et al., 2024, A&A, 692, A237

    Zangrandi F., Jurk K., Sasaki M., Knies J., Filipovi \'c M. D., Haberl F., Kavanagh P., et al., 2024, A&A, 692, A237

  102. [110]

    G., Zhou P., 2015, ApJ, 799, 103

    Zhang G.-Y., Chen Y., Su Y., Zhou X., Pannuti T. G., Zhou P., 2015, ApJ, 799, 103. doi:10.1088/0004-637X/799/1/103

  103. [111]

    , " * write output.state after.block = add.period write newline

    ENTRY address archiveprefix author booktitle chapter edition editor howpublished institution eprint journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sent...

  104. [112]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.