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eRO-ExTra: eROSITA extragalactic non-AGN X-ray transients and variables in eRASS1 and eRASS2

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read eROSITA finds 304 X-ray flares outside known active galaxies

desk verdict The largest clean-ish sample of extragalactic non-AGN X-ray transients from eROSITA, with rich data products; the variability selection has a real systematic that should be stress-tested before the catalog is used for precision population statistics. read the letter →

arxiv 2501.04208 v1 pith:WIDQQELN submitted 2025-01-08 astro-ph.HE

Iuliia Grotova , Arne Rau , Mara Salvato , Johannes Buchner , Adelle J. Goodwin , Zhu Liu , Adam Malyali , Andrea Merloni
show 14 more authors
Dusán Tubín-Arenas David Homan Mirko Krumpe Kirpal Nandra Gemma E. Anderson Riccardo Arcodia Sabina Bahic Pietro Baldini David A.H. Buckley Stefano Ciroi Adela Kawka Megan Masterson James C.A. Miller-Jones Francesco Di Mille
This is my paper · ORCID
classification astro-ph.HE
keywords X-raytransientseROSITAtidaldisruptioneventssupermassiveblackholesactivegalacticnucleivariabilityall-skysurveysextragalacticcatalogs
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

Most extragalactic X-ray variability is the ordinary flickering of active galactic nuclei (AGN), galaxies whose central black hole is steadily accreting, but a small fraction of X-ray flares come from rarer events: stars torn apart by supermassive black holes, quasi-periodic eruptions, and other short-lived accretion phenomena. This paper argues that, after stripping away stars, galaxy clusters, and everything previously classified as an AGN, the first two eROSITA all-sky surveys still contain 304 genuinely extragalactic X-ray transients and variables. The resulting eRO-ExTra catalog is presented as a relatively clean parent sample of non-AGN variability associated with massive black holes, with optical counterparts for over 90% of sources, reliable redshifts for over 80%, peak-spectrum fits, light-curve classes, and radio identifications for 31 sources. More than 95% of the sources were discovered in X-rays for the first time. If the selection is right, this is the largest systematically selected sample of its kind and a direct resource for measuring how often otherwise quiet black holes flare.

What carries the argument

The load-bearing machinery is a two-epoch variability selection expressed by two numbers per source. The fractional amplitude is $A = F_{\mathrm{max}}/F_{\mathrm{min}}$, and the variability significance is $S = (F_{\mathrm{max}}-F_{\mathrm{min}})/\sqrt{F_{\mathrm{err,max}}^2+F_{\mathrm{err,min}}^2}$, with both required to exceed four; for sources detected in only one survey, the missing flux is a $3\sigma$ upper limit computed from aperture photometry under an assumed absorbed power-law spectrum ($\Gamma=2$, $N_{\mathrm{H}}=3\times10^{20}\,\mathrm{cm}^{-2}$). Everything downstream, including the final 304-source catalog and its derived luminosity function and rate, inherits these cuts. The second half of the machinery is the cleaning cascade: optical counterpart association, exclusion of stars by parallax, morphology, and catalog classifications, exclusion of extended cluster emission, a mid-infrared color cut against AGN, exclusion of known AGN and quasars from pre-eROSITA catalogs, visual inspection of archival optical spectra, and exclusion of sources with archival X-ray variability. The catalog is the intersection of this selection with a large optical survey footprint covering about three-quarters of the surveyed sky.

What would settle it

Recompute fluxes, errors, and upper limits for all 2331 pre-cleaning variable candidates using independent forced photometry on the eRASS1 and eRASS2 images, then reapply the $S>4$ and $A>4$ cuts; if the 304-source catalog does not reproduce within a few percent, its completeness and derived rate are not robust. A complementary check is to search archival X-ray images for the 296 sources listed as not detected, looking for any at or above the claimed $3\sigma$ upper limits; finding a significant number would break the claim that most eRO-ExTra sources are genuinely new X-ray transients.

Watch

Extended reading notes

Core claim

The central discovery is the catalog itself: 304 X-ray sources that change by more than a factor of four in flux between the first two eROSITA all-sky surveys in the 0.2-2.3 keV band, with variability significance $S>4$ and fractional amplitude $A>4$, and that survive a deliberate campaign to exclude anything with a known AGN signature. The selection starts from the full eRASS1 and eRASS2 source lists, requires a detection-likelihood of at least 15 for the brighter detection of each pair, removes extended and spurious sources, assigns $3\sigma$ upper limits to sources detected in only one survey, and then removes stars, galaxy clusters, mid-infrared-selected AGN, objects with pre-eROSITA active-galaxy classifications, broad-line AGN spectra, blazars, and sources with archival X-ray variability. The paper reports the resulting sample's properties: more than 90% have reliable optical counterparts, more than 80% have spectroscopic or photometric redshifts, each source has a peak-epoch power-law spectral fit and a light-curve class (flare, decline, brightening, or other), and 31 sources are radio detected. More than 95% of the sources have no prior X-ray detection, so they are new discoveries. The paper concludes that eRO-ExTra constitutes a relatively clean parent sample of non-AGN variability phenomena associated with massive black holes, suitable for population studies of tidal disruption events and related transients.

Load-bearing premise

The two variability numbers per source depend on estimated flux errors, and for sources not detected in one of the two surveys they depend on a $3\sigma$ upper limit computed from an assumed spectral model; if those substituted errors or upper limits are systematically wrong, the set of sources crossing the $S>4$ and $A>4$ thresholds changes, taking the whole catalog and its derived statistics with it.

Editorial extensions

If this is right

  • The catalog gives a homogeneous parent population for studying rare nuclear transients such as tidal disruption events and quasi-periodic eruptions, with a known selection function rather than a set of serendipitous discoveries.
  • Population statistics follow directly: a sky density of about 0.03 sources per square degree per year, a double-power-law X-ray luminosity function, and an integrated volumetric rate of $1.8^{+0.5}_{-0.4}\times10^{-7}\,\mathrm{Mpc}^{-3}\,\mathrm{yr}^{-1}$ for this selection.
  • Individual follow-up can now be targeted: the peak photon-index distribution, light-curve class, and radio data separate subpopulations worth multiwavelength study.
  • Because more than 95% of the sources are new X-ray discoveries, previous X-ray surveys lacked the sensitivity or cadence to catch this population, and future all-sky surveys can apply the same cuts directly.
  • The 31 radio-detected sources provide a sample for testing whether compact jets accompany these non-AGN nuclear flares.

Reading between the lines

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

  • Editorial inference: the integrated rate of about $1.8\times10^{-7}\,\mathrm{Mpc}^{-3}\,\mathrm{yr}^{-1}$ for this mixed population is higher than canonical tidal-disruption-event rates, suggesting the sample includes a broader class of low-luminosity nuclear accretion flares whose true occurrence rate in quiescent galaxies may have been underestimated.
  • Editorial inference: because the selection compares only eRASS1 with eRASS2, events that rise and fade within one six-month survey or peak outside that window are missed; applying the same cuts to the later all-sky surveys should multiply the sample and constrain the duty cycle of these events.
  • Editorial inference: the cleaning strategy predicts that the remaining sources should not preferentially sit in massive, actively growing host galaxies; measuring host stellar masses could test whether the parent population is genuinely inactive black holes.
  • Editorial inference: the radio-detected sources with luminosities above the star-formation expectation may include a systematically selected sample of radio-emitting nuclear transients; comparing radio brightness across the two radio epochs already available could separate newly launched jets from persistent low-level AGN activity.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. This paper presents the eRO-ExTra catalog, a systematically selected sample of 304 extragalactic X-ray transients and variables in the eROSITA all-sky surveys eRASS1 and eRASS2, selected to have variability significance S>4 and fractional amplitude A>4 between the two surveys, located in the Legacy Survey DR10 footprint, and cleaned of Galactic stars and known AGN using multiwavelength criteria. The catalog provides, for each source, eROSITA light curves over eRASS1-4(5) with a classification into decline, flare, brightening, or other; peak-epoch X-ray spectral fits with an absorbed power law; archival X-ray detections and upper limits from Swift, ROSAT, and XMM-Newton; optical/near-infrared counterparts and redshifts (spectroscopic or photometric) for more than 80% of sources; and radio detections from RACS and VLASS for 31 sources. The authors propose the catalog as a 'relatively clean parent sample of non-AGN variability phenomena associated with massive black holes' and use it to compute an X-ray luminosity function and an integrated volumetric rate in Appendix E.

Significance. If the selection is robust, this is the largest systematically selected sample of extragalactic X-ray transients and variables without known AGN association, with a level of multiwavelength characterization that previous samples lack. The paper is transparent about methodology: selection steps are documented with flowcharts, thresholds are stated, purity/completeness of the p_any counterpart probability is quantified, upper limits and archival constraints are described in detail, and spectral fitting uses Bayesian methods (BXA/UltraNest). The authors also explicitly acknowledge that residual AGN contamination remains (Sect. 7), which is appropriate for a 'relatively clean' rather than pure sample. The catalog will be a useful resource for TDE, QPE, and other nuclear-transient population studies, and the paper helps bridge the flux gap between deep pencil-beam surveys and shallower all-sky variability studies.

major comments (3)
  1. [Sect. 2.1, Eqs. (1)-(2)] For the 404 sources detected in only one of eRASS1/eRASS2 (C1 and C2), Fmin is a 3-sigma upper limit and F_ERR_min is set to zero. Thus S = (Fmax - 3sigma_UL)/F_ERR_max is not a significance in the usual sense; it is a ratio in which the lower measurement is treated as exact. The choice of a 3-sigma confidence, the fixed absorbed power law (Gamma=2, NH=3e20) used to convert counts to flux for the upper limit (Appendix A), and the zeroing of F_ERR_min all directly control which sources pass the S>4 cut. The paper does not report how many of the 2331 initial candidates, or of the final 304 sources, would survive if the upper-limit error were propagated, if a different confidence level were used, or if the spectral model were varied to, e.g., Gamma=1.5 or 2.5. Because the entire catalog and all downstream statistics (light-curve class fractions, spectral index distribution, and the Appendix E rate) inherit this membership, a robustness test of these choices is load-bearing and should be added.
  2. [Appendix E] The 1/Vmax XLF and the integrated rate are computed using sensitivity maps that account for the DET_LIKE>15 cut and the A>4 amplitude cut, but not for the S>4 significance cut. Since S depends on the flux errors, which scale with the number of detected counts, S is not equivalent to A>4 and will impose an additional distance-dependent constraint on the detectable volume. Omitting this criterion from the selection function can bias the maximum volume Vmax and hence the XLF and the reported rate of 1.8e-7 Mpc^-3 yr^-1. The authors should either include the S>4 condition in the sensitivity calculation or demonstrate that it is always less constraining than the amplitude cut for the sample under consideration.
  3. [Sect. 2.1 (median error substitution)] For catalog detections without published errors, the paper assigns the median count error of sources within 10% in counts and exposure time. This is a reasonable first-order approximation, but the paper does not report how many of the 2331 sources are affected, nor does it test the sensitivity of the final catalog to the width of the matching bin (e.g., 5% or 20%) or to the use of a different percentile (e.g., 84th instead of 50th). If the true errors for faint, slightly extended, or crowded sources are larger than the adopted median, S will be overestimated and sources may enter the catalog spuriously. A brief robustness test showing the number of final sources under alternative error-assignment choices would strengthen the central claim.
minor comments (5)
  1. [Sect. 2.2] The optimal p_any=0.17 threshold is determined as the intersection of purity and completeness functions calculated 'for our sample after the NW AY match.' This is a self-calibration on the same sample used to build the catalog; it would be helpful to state whether the purity/completeness curves were validated on an independent set or via cross-validation, so that the reported <5% chance-coincidence rate is not circular.
  2. [Sect. 2.5, Table 1] Table 1 sums to 440 sources (144 detected, 296 not detected), which matches the number of sources entering the archival variability step, not the final 304. The caption should state that the table refers to the sample before the exclusion of the 136 archival-variable sources, to avoid confusion with the final catalog.
  3. [Fig. 2 and throughout] The projection name 'Aito ff' in the caption of Fig. 2 should be 'Aitoff'.
  4. [Appendix E] The text 'the function should have been corrected by a factor of 2.6' is unclear. Please specify that the factor accounts for both the LS10 areal coverage (76% of eROSITA_DE) and the fact that eROSITA_DE covers approximately half the sky, and clarify whether the resulting XLF is per unit volume of the full sky.
  5. [Sect. 3.1] The light-curve classification uses the same S definition (Eq. 2) for comparisons involving later eRASS nondetections, so the F_ERR_min=0 issue also affects the 'decline' and 'flare' classes when upper limits are involved. A brief note acknowledging this, or a consistency check, would be appropriate.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation: the catalog is defined by explicit cuts and the Appendix E statistics are measured from the sample itself, with only minor reliance on author-team software and unpublished catalogs.

full rationale

The paper's central claim is a catalog and its characterization, not a model prediction. The eRO-ExTra sample is produced by explicit cuts: crossmatch of eRASS1/eRASS2 catalogs, A>4 and S>4 from Eqs. (1)-(2), LS10 counterpart requirements, and removal of stars, known AGN, and archivally variable sources. None of these steps fits a parameter to the final 304 sources and then re-predicts a quantity defined by that fit; catalog membership is the direct output of the stated criteria. The XLF and volumetric rate in Appendix E are 1/Vmax measurements over the selected sample, with detection limits computed from sensitivity maps and A*Fmin; this is a measurement of the sample's properties, not a circular prediction. The upper-limit treatment (F_ERR_min=0, median error substitution, fixed absorbed power-law ECF) is an assumption that affects robustness and could be questioned, but it does not make any equation reduce to its inputs by construction. Self-citations (NWAY, UltraNest, eRASS:5, Salvato et al. in prep.) provide software, counterpart-matching methodology, and X-ray positions; they are not used to justify the non-AGN interpretation or to forbid alternative explanations. No uniqueness theorem or fitted ansatz is imported as load-bearing. The caveat that residual AGN may remain is explicitly stated in Sects. 2.4 and 7. Overall, the derivation chain is self-contained; the minor reliance on unpublished in-team catalogs does not constitute circularity.

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

The catalog claim rests on hand-chosen selection thresholds and standard astrophysical assumptions. No new physical entities are introduced. The XLF fit parameters are derived from the sample and are not used to define the catalog.

free parameters (5)
  • Variability thresholds A and S = A > 4, S > 4
    Hand-chosen cuts defining the variability sample (Sect. 2.1). The catalog size would change with different thresholds.
  • p_any counterpart threshold = 0.17
    Determined as the intersection of purity and completeness for this sample (Sect. 2.2, Fig. 4). Sources below this are still kept in the catalog but flagged.
  • W1-W2 AGN color cut = W1-W2 < 0.8 mag (Vega)
    Adopted from Stern et al. (2012) to remove AGN-dominated sources (Sect. 2.4).
  • XLF break luminosity = L_br = (1.6 +/- 0.4) e43 erg/s
    Fitted to the eRO-ExTra XLF (Appendix E, Eq. E.1). Used only for the rate estimate, not for catalog selection.
  • XLF power-law slopes = faint slope index -0.25 +/- 0.18, bright slope index 1.47 +/- 0.05
    Fitted to the same XLF (Appendix E, Eq. E.1).
assumptions (5)
  • domain assumption Flat LambdaCDM cosmology with H0=67.7 km/s/Mpc and Omega_m=0.309
    Adopted for luminosity distances and rate calculations (Sect. 1).
  • domain assumption Flux conversion and upper limits assume an absorbed power-law spectrum with Gamma=2 and NH=3e20 cm^-2
    Used in ECF and upper limit computation (Sect. 2.1, Appendix A); same model as eROSITA source catalogs.
  • domain assumption Sources with archival X-ray variability (arch_flag=-1, 136 sources) are likely AGN and are excluded
    Section 2.5; the paper states long-term variability is more likely AGN, but notes it could exclude repeating transients (Malyali et al. 2023).
  • domain assumption The NWAY training sample for counterpart identification is representative for X-ray transients
    Section 2.2 quotes Salvato et al. (in prep); the training sample was for typical X-ray emitters, not specifically transients.
  • standard math The 1/Vmax method assumes the sample is representative after selection-function corrections
    Appendix E applies the classical 1/Vmax approach (Schmidt 1968) with sensitivity maps to estimate the XLF and rate.

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Cite this review

Pith. "Pith review of eRO-ExTra: eROSITA extragalactic non-AGN X-ray transients and variables in eRASS1 and eRASS2." pith.science (2026). https://pith.science/paper/WIDQQELN

@misc{pith2026250104208,
  author       = {Pith},
  title        = {Pith review of: eRO-ExTra: eROSITA extragalactic non-AGN X-ray transients and variables in eRASS1 and eRASS2},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WIDQQELN}},
  note         = {Machine review of arXiv:2501.04208}
}
read the original abstract

(Abridged) While previous X-ray studies showed the dominance of regular active galactic nuclei (AGN) variability, a small fraction of sources arise from more exotic phenomena such as tidal disruption events (TDEs), quasi-periodic eruptions, or other short-lived events associated with supermassive black hole accretion. This paper describes the systematic selection of X-ray extragalactic transients found in the first two eROSITA all-sky surveys (eRASS) that are not associated with known AGN prior to eROSITA observations. We generated a variability sample from eRASS1 and eRASS2 (Dec. 2019-Dec. 2020), which includes sources with a variability significance and a fractional amplitude larger than four, located in the Legacy Survey DR10 (LS10) footprint. The properties of LS10 counterparts were used to exclude stars and known AGN. The sample was additionally cleaned using pre-eROSITA classifications, archival optical spectra, and archival X-ray data. The final catalog eRO-ExTra includes 304 extragalactic eROSITA transients and variables not associated with known AGN. More than 90% of sources have reliable LS10 optical counterparts. For each source, we provide archival X-ray data from Swift, ROSAT, and XMM-Newton; the eROSITA long-term light curve (2-2.5 years) with a light curve classification; as well as the best power law fit spectral results at the peak eROSITA epoch. Reliable spectroscopic and photometric redshifts are provided for more than 80% of the sample. Several sources in the catalog are known TDE candidates discovered by eROSITA. In addition, 31 sources are radio detected. The eRO-ExTra transients constitute a relatively clean parent sample of non-AGN variability phenomena associated with massive black holes. More than 95% of eRO-ExTra sources were discovered in X-rays with eROSITA for the first time, which makes it a valuable resource for studying unique nuclear transients.

Figures

Figures reproduced from arXiv: 2501.04208 by the authors.

Figure 1
Figure 1. eRASS1-eRASS2 variability sample selection steps. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Representation of eROSITA_DE sky in Aito [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Functions of purity and completeness vs. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figures from the paper (10 more)
Figure 3
Figure 3. Figure 3: Selection of extragalactic transients from variability sam [PITH_FULL_IMAGE:figures/full_fig_p005_3.png]
Figure 5
Figure 5. Figure 5: Decision tree of light curve classification into four [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Light curve class examples: decline (dark blue), flare [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: eRO-ExTra catalog in Aitoff projection in Galactic coordinates. Left: sources color coded by light curve type: dark blue - decline, light blue - flare, red - brightening, orange - other (see [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: Example BXA fit to eRASS2 data of 1eRASS J143045.4- [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 9
Figure 9. Figure 9: Distribution of best fit photon indices for peak X-ray [PITH_FULL_IMAGE:figures/full_fig_p009_9.png]
Figure 10
Figure 10. Figure 10: Distribution of reliable spectroscopic and photometric [PITH_FULL_IMAGE:figures/full_fig_p009_10.png]
Figure 12
Figure 12. Figure 12: Distribution of 226 eRO-ExTra sources with reliable [PITH_FULL_IMAGE:figures/full_fig_p010_12.png]
Figure 13
Figure 13. Figure 13: Comparison of 0.2–2.3 keV flux distributions of eRO [PITH_FULL_IMAGE:figures/full_fig_p011_13.png]
Figure 14
Figure 14. Figure 14: Luminosity 0.2–2.3 keV vs. redshift for all eRO-ExTra [PITH_FULL_IMAGE:figures/full_fig_p012_14.png]

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Forward citations

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