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Systematic KMTNet Planetary Anomaly Search. XII. Complete Sample of 2017 Subprime Field Planets

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read The 2017 subprime-field analysis completes a 112-planet KMTNet sample that confirms the 'sub-Saturn desert' in planetary mass ratios.

desk verdict Four new planets are real and the completed 112-planet catalog is valuable, but the sub-Saturn desert 'confirmation' rests on raw counts and should be softened or efficiency-corrected. read the letter →

arxiv 2504.20155 v1 pith:SYR6M5DJ submitted 2025-04-28 astro-ph.EP astro-ph.GAastro-ph.SR

Yuqian Gui , Weicheng Zang , Ruocheng Zhai , Yoon-Hyun Ryu , Andrzej Udalski , Hongjing Yang , Cheongho Han , Shude Mao
show 34 more authors
Michael D. Albrow Sun-Ju Chung Andrew Gould Kyu-Ha Hwang Youn Kil Jung In-Gu Shin Yossi Shvartzvald Jennifer C. Yee Sang-Mok Cha Dong-Jin Kim Hyoun-Woo Kim Seung-Lee Kim Chung-Uk Lee Dong-Joo Lee Yongseok Lee Byeong-Gon Park Richard W. Pogge Przemek Mróz Micha{l} K. Szymański Jan Skowron Rados{l}aw Poleski Igor Soszyński Pawe{l} Pietrukowicz Szymon Koz{l}owski Krzysztof Ulaczyk Krzysztof A. Rybicki Patryk Iwanek Marcin Wrona Mariusz Gromadzki Hanyue Wang Jiyuan Zhang Renkun Kuang Qiyue Qian Wei Zhu
This is my paper · ORCID
classification astro-ph.EPastro-ph.GAastro-ph.SR
keywords gravitationalmicrolensingexoplanetdetectionKMTNetAnomalyFinderplanetarymassratiosub-SaturndesertGalacticbulgelight-curvemodeling
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 completes the final piece of a systematic census of planets found by gravitational microlensing in the first four years of the KMTNet survey: the low-cadence 'subprime' fields of 2017. It reports four new unambiguous planets—KMT-2017-BLG-0849, KMT-2017-BLG-1057, OGLE-2017-BLG-0364, and KMT-2017-BLG-2331—with planet-host mass ratios from $1.0\times10^{-4}$ to $1.3\times10^{-3}$, plus one candidate, KMT-2017-BLG-0958, whose light curve cannot be conclusively separated from a binary-source interpretation. Once merged with previously reported planets, the 2016-2019 AnomalyFinder sample contains 112 unambiguous planets, about three times the number of microlensing planets known before KMTNet, and nearly half of them come from the low-cadence subprime fields. The paper's key statistical claim is that this complete sample confirms the 'sub-Saturn desert,' a deficit of planets with mass ratios $\log q$ between $-3.6$ and $-3.0$, previously seen in 2018-2019 and now supported by the complete 2016-2017 seasons.

What carries the argument

The machine that carries the argument is the AnomalyFinder pipeline, applied uniformly to every KMTNet event: each light curve is fitted with a point-source point-lens (PSPL) model, the residuals are scanned for anomalies, and the anomalous events are re-fitted with 2L1S binary-lens models, in which the planet-host mass ratio $q$ and projected separation $s$ are the parameters of interest. Completeness comes from the pipeline, not from any single event: the same search and the same model-selection rules have now been applied to all four seasons and to both prime and subprime fields. Within individual events, the load-bearing analysis tools are the 'hotter' Markov-chain Monte Carlo search that maps out competing caustic-crossing topologies, the 1L2S (binary-source) check that separates genuine planets from two-star sources, and a deliberately permissive $\Delta\chi^2 > 20$ rule for discarding alternative models, with all surviving models reported so that readers can apply their own threshold.

What would settle it

Re-run the complete 2017 subprime search with the same data but with two independent reviewers classifying all 3,315 candidates, and also inject synthetic planet signals into the light curves to measure what fraction are recovered. If the final planet count or the number of planets with $\log q$ between $-3.6$ and $-3.0$ changes by more than the reported uncertainties, the claimed completeness and the confirmed desert would be contradicted.

Watch

Extended reading notes

Core claim

The central claim, stated the way the authors would state it, is that the first four years of KMTNet data now hold a complete, homogeneously searched planetary sample, and that its mass-ratio distribution is a fair census of the planets the survey could detect. After the 2017 subprime analysis reported here, the combined 2016-2019 AnomalyFinder sample contains 112 unambiguous planets, with a seasonal distribution $(23,30,35,24)$ consistent with Poisson fluctuations; of these, 37 were first found by the systematic search and 75 were previously known from by-eye searches and then recovered. The paper finds that the 2017 subprime sample alone contains 15 unambiguous planets, of which 5 have $\log q < -3.0$. The mass-ratio distribution of the full sample shows a plateau, or desert, between $\log q = -3.6$ and $-3.0$, and the paper argues this now confirms the desert reported from the 2018 and 2019 seasons, making it a genuine feature of planets around M and K dwarfs rather than an artifact of incomplete searching. The new planets add mass ratios $(1.0,1.2,4.6,13)\times10^{-4}$, corresponding to median planetary masses of $6.4, 24, 76$, and $171\,M_\oplus$ around $0.2-0.6\,M_\odot$ hosts.

Load-bearing premise

The load-bearing premise is that the 2016-2017 data really are complete: every planet-like anomaly among the 3,315 candidate signals was caught by one person's manual review, and the deliberately loose rule for discarding alternative explanations did not let any impostor into the final 112-planet list.

Editorial extensions

If this is right

  • The 112-planet complete sample provides a homogeneous statistical basis for planet demographics at separations beyond the snow line, the regime where microlensing is uniquely sensitive.
  • The confirmed sub-Saturn desert at $\log q \in [-3.6,-3.0]$ becomes a feature that any formation model—most directly the core-accretion runaway-growth scenario—must reproduce for low-mass host stars.
  • Because 37 of the 112 planets were found only by the systematic search, future microlensing planet samples that rely on by-eye detection will be incomplete at low mass ratios, motivating automated searches in ongoing and planned surveys.
  • The 2016-2019 KMTNet sample reaches mass ratios an order of magnitude smaller than pre-KMTNet samples and increases the number of $\log q < -4$ planets five-fold, sharpening measurements of the low-ratio slope of the mass-ratio function.

Reading between the lines

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

  • If the desert is a real feature of the planet population, it should reappear in the 2020-2022 KMTNet seasons and in future bulge surveys; a filled-in desert there would instead point to a time-varying selection effect.
  • The paper's permissive $\Delta\chi^2 > 20$ threshold means borderline events (for example the candidate KMT-2017-BLG-0958 and the 'Wide B'/'Wide C' models of OGLE-2017-BLG-0364) sit close to the sample boundary, so a uniform re-analysis across all seasons with a single stricter threshold would test how much the desert's edges move.
  • The desert spans roughly the planet-mass range of the 'planet gap' or 'Neptune desert' seen in radial-velocity and transit demographics around sun-like stars; if the two deficits share a formation origin, microlensing offers the only current way to test that origin at wide separations and around M-dwarf hosts.
  • The unresolved 1L2S degeneracy in the candidate event hints that small numbers of 'possible' planets may exist in all seasons, so the true planet count could be slightly higher than 112.
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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 completes the KMTNet AnomalyFinder planetary sample for the 2017 subprime fields by analyzing four unambiguous planets (KMT-2017-BLG-0849, KMT-2017-BLG-1057, OGLE-2017-BLG-0364, KMT-2017-BLG-2331) and one candidate (KMT-2017-BLG-0958). The light-curve analysis follows the series standard: 2L1S grid searches with VBBinaryLensing, checks of close/wide topologies, 1L2S alternatives, and higher-order effects, followed by Bayesian estimates of host and planet masses. The authors combine these with previously published and forthcoming events to form a sample of 112 unambiguous planets from 2016–2019, which they state nearly triples the microlensing planetary sample, and they claim that the \"sub-Saturn desert\" (log q = [-3.6, -3.0]) found in the 2018–2019 KMTNet samples is confirmed by the 2016–2017 samples.

Significance. If the completeness claims hold, the 112-planet AnomalyFinder sample is the first large homogeneous microlensing sample spanning four seasons, and it will be a valuable resource for mass-ratio function studies. The individual event analyses are careful, including explicit treatment of the Cannae/von Schlieffen degeneracy for KMT-2017-BLG-0849 and the close/wide degeneracy for OGLE-2017-BLG-0364, with the full Δχ² values reported so readers can apply alternative thresholds. The Bayesian physical-parameter estimates use external Galactic-model priors and lens-flux upper limits, which is appropriate. The primary weakness is that the paper's headline claim—that the sub-Saturn desert is \"confirmed\" by the 2016–2017 data—is based on raw counts rather than the efficiency-corrected analysis used to establish the desert in 2018–2019.

major comments (3)
  1. [Abstract and Section 5, Figure 15] The claim that the sub-Saturn desert is \"confirmed\" by the 2016–2017 KMTNet samples is not supported by the analysis as presented. The 2018–2019 desert was established only after correcting for KMTNet/AnomalyFinder detection efficiency (Zang et al. 2025), whereas Section 5 for 2016–2017 uses only raw counts in Figure 15 and explicitly states that the authors \"do not attempt to study the mass-ratio function here.\" A raw deficit in log q = [-3.6, -3.0] could be produced by selection effects, including the manual triage of 3315 AnomalyFinder candidates by a single operator and the different sensitivities of by-eye versus AnomalyFinder discovery. Comparing this raw distribution with the heterogeneous pre-KMTNet sample is not a controlled test. Please rephrase the abstract and Section 5 to say that the 2016–2017 data are consistent with the desert, or provide an efficiency-corrected analysis of the 2016–2017 fields.
  2. [Section 3.1 and Table 8] The paper adopts a degeneracy criterion (exclude models with Δχ² > 20) that is looser than the series standard (Δχ² > 10), but Table 8 uses a different rule, listing only models with Δχ² < 10 relative to the best fit. For OGLE-2017-BLG-0364, the Close Inner model is disfavored by only Δχ² = 18.9 and is retained as a viable solution in the light-curve and Bayesian analyses (Tables 4 and 7), yet Table 8 reports only log q = -3.341. If Close Inner is correct, log q ≈ -2.855, a materially different planet that lies just outside the desert interval. The sample definition and the \"unambiguous\" classification need a single, clearly stated threshold, and the table should either list the degenerate solutions or explain why they are omitted from the final sample.
  3. [Section 1 and Section 5] The completeness of the 2016–2017 sample—and hence the 112-planet \"complete sample\" claim and the raw mass-ratio distribution in Figure 15—rests on a manual step: \"the operator (W. Zang) identified 133 anomalous events\" from 3315 AnomalyFinder candidates. This operator-dependent triage is not validated, for example by an independent search or a reproducibility measure, and it is load-bearing for the completeness claim. Please quantify the reproducibility of this step or state clearly as a limitation that subtle anomalies may have been missed, which would affect the raw mass-ratio distribution and the desert interpretation.
minor comments (5)
  1. [Section 5 and Table 8 note] The typo \"AnomlyFinder\" appears multiple times (Section 5, Table 8 note, Figure 15 labels) and should be corrected to \"AnomalyFinder.\"
  2. [Section 1] \"From the 2017 KNTNet subprime data\" should be \"2017 KMTNet subprime data.\"
  3. [Section 3.4] \"A PLPS fit by excluding the anomaly\" should read \"A PSPL fit.\"
  4. [Section 3.2] The sentence \"We expect that ρ and q are the lower limits because the caustic crossing time is ≤ 2tEρ for a larger source\" is unclear; consider rephrasing to state that the heuristic estimates are lower limits because finite-source effects can be larger than inferred from the caustic-crossing duration.
  5. [Section 5] The sentence listing the seasonal distribution (23, 30, 35, 24) states it is consistent with Poisson variations; adding a brief significance estimate (e.g., the standard deviation of a Poisson mean of 28 is about 5.3) would make this quantitative.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the new light-curve analyses, sample completion, and desert confirmation rest on independent data, with only minor self-citations that are not load-bearing.

full rationale

The paper's central deliverables are the light-curve models and physical parameters of five 2017 events, the completion of the 2016-2019 KMTNet AnomalyFinder planetary sample, and an assessment of the mass-ratio distribution. All of these are derived from new fits to photometric data using standard microlensing formalism (VBBinaryLensing, MCMC, CMD-based source characterization), not from the claims they are said to support. The 'sub-Saturn desert' confirmation compares the 2016 and 2017 seasons with the 2018 and 2019 seasons in which the desert was first reported in Zang et al. (2025); those are independent data sets, so the confirmation is not circular. The paper does cite several prior AnomalyFinder papers by the same collaboration, but the load-bearing steps do not reduce to those citations: the exclusion of KMT-2017-BLG-1145 is justified by the algorithm limitation studied in Kuang et al. (2022), and the planetary-event definition follows Zang et al. (2025). These are self-citations, but they are stated criteria rather than assumed conclusions, and the new seasonal data provide independent content. The main caveat is scientific rather than circular: the 2016/2017 'confirmation' uses raw cumulative counts without the detection-efficiency correction applied to the 2018/2019 analysis, so the desert claim is weaker than the abstract implies; however, this is an evidentiary strength issue, not a circular reduction.

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

The central claims rest on the fitted mass ratios of the new planets, the Galactic model priors used to convert those ratios to physical parameters, and the assumption that the 2016-2017 sample is complete without an additional detection-efficiency correction. No new physical entities are introduced.

free parameters (5)
  • log q of KMT-2017-BLG-0849 (Wide A) = -3.996 ± 0.061
    Fitted from the light curve; enters the sub-Saturn desert distribution and the sample.
  • log q of KMT-2017-BLG-1057 = -3.937 ± 0.091
    Fitted from the light curve; same role.
  • log q of OGLE-2017-BLG-0364 (Wide A) = -3.334 ± 0.079
    Best-fit value; degenerate models span -3.286 to -2.855, so this parameter carries model uncertainty relevant to the desert edge.
  • log q of KMT-2017-BLG-2331 = -2.893 ± 0.044
    Fitted from the light curve.
  • Degeneracy threshold Δχ2 > 20 = 20
    Hand-set criterion to exclude degenerate models, looser than the series standard of Δχ2 < 10 for degeneracy; affects which planets enter the sample.
assumptions (5)
  • domain assumption Galactic model priors for lens mass, distance, and velocity (Section 4.1, following Yang et al. 2021).
    The Bayesian estimates of host mass, planet mass, and distance depend on this model; it assumes the planetary occurrence rate is independent of host-star properties.
  • domain assumption Photometric error bars are recalibrated so each data set has χ2/dof = 1 (Section 2, Yee et al. 2012 method).
    All Δχ2 comparisons and confidence intervals assume the error bars are correct to a common normalization.
  • standard math The 2L1S and 1L2S magnification models computed with VBBinaryLensing are accurate (Section 3.1).
    The fitted parameters and model comparisons assume the lensing magnification calculations are correct.
  • domain assumption The source angular radius θ* is derived from HST CMD calibration and the Adams et al. (2018) color-surface brightness relations (Section 4.1).
    θ* propagates into θE, μrel, and the Bayesian mass estimates.
  • domain assumption The sub-Saturn desert comparison treats the raw 2016-2017 mass-ratio distribution as comparable to the efficiency-corrected 2018-2019 distribution (Section 5).
    The confirmation claim does not apply a detection-efficiency correction to the new sample, implicitly assuming the raw plateau is not a selection artifact.

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Pith. "Pith review of Systematic KMTNet Planetary Anomaly Search. XII. Complete Sample of 2017 Subprime Field Planets." pith.science (2026). https://pith.science/paper/SYR6M5DJ

@misc{pith2026250420155,
  author       = {Pith},
  title        = {Pith review of: Systematic KMTNet Planetary Anomaly Search. XII. Complete Sample of 2017 Subprime Field Planets},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SYR6M5DJ}},
  note         = {Machine review of arXiv:2504.20155}
}
abstract

We report the analysis of four unambiguous planets and one possible planet from the subprime fields ($\Gamma \leq 1~{\rm hr}^{-1}$) of the 2017 Korea Microlensing Telescope Network (KMTNet) microlensing survey, to complete the KMTNet AnomalyFinder planetary sample for the 2017 subprime fields. They are KMT-2017-BLG-0849, KMT-2017-BLG-1057, OGLE-2017-BLG-0364, and KMT-2017-BLG-2331 (unambiguous), as well as KMT-2017-BLG-0958 (possible). For the four unambiguous planets, the mean planet-host mass ratios, $q$, are $(1.0, 1.2, 4.6, 13) \times 10^{-4}$, the median planetary masses are $(6.4, 24, 76, 171)~M_{\oplus}$ and the median host masses are $(0.19, 0.57, 0.49, 0.40)~M_{\odot}$ from a Bayesian analysis. We have completed the AnomalyFinder planetary sample from the first 4-year KMTNet data (2016--2019), with 112 unambiguous planets in total, which nearly tripled the microlensing planetary sample. The ``sub-Saturn desert'' ($\log q = \left[-3.6, -3.0\right]$) found in the 2018 and 2019 KMTNet samples is confirmed by the 2016 and 2017 KMTNet samples.

Figures

Figures reproduced from arXiv: 2504.20155 by the authors.

Figure 1
Figure 1. displays the light curve of KMT-2017-BLG-0849. The light curve exhibits a bump-type anomaly, which could, in principle, be caused by a 2L1S or a 1L2S model. We first consider the 2L1S modeling. By excluding the data over the anomaly, a PSPL fit yields (t0, u0, tE) = (7971.2, 1.10, 24.9). From [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Scatter plot of “hotter” MCMC of ∆ξ vs. log q of KMT￾2017-BLG-0849, where ∆ξ is the offset between the center of the caustic and the intersection of the source trajectory and the planet￾host axis. We find ∆χ 2 barriers of ∼ 20 between the “Wide A” and “Wide B” models, ∼ 70 between the “Wide A” and “Wide C” models, ∆χ 2 ∼ 90 between the “Wide B” and “Wide D” models. Color coding is (purple, red, yellow, green, cyan, … view at source ↗
Figure 4
Figure 4. A close-up of the anomaly with the 2L1S close and 1L2S models of KMT-2017-BLG-0849. Symbols are similar to those in [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figures from the paper (10 more)
Figure 3
Figure 3. Figure 3: Caustic crossing geometries of KMT-2017-BLG-0849 models. In each panel, the red lines represent the caustics, the solid black line represents the source trajectory, the radius of the green dot represents the source radius, and the line with an arrow indicates the direc…
Figure 6
Figure 6. Figure 6: Geometries of KMT-2017-BLG-1057 and KMT-2017- BLG-2331 models. Symbols are similar to those in [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 8
Figure 8. Figure 8: Scatter plot of “hotter” MCMC of ∆ξ vs. log q for the wide models of OGLE-2017-BLG-0364. The distribution is derived by multiplying the photometric error bars by a factor of √ 3 and then multiplying the resulting χ 2 by 3.0 for the plot. We find three local minima and …
Figure 7
Figure 7. Figure 7: The observed data and the 2L1S and 1L2S models of OGLE-2017-BLG-0364. A grid search identifies three local minima whose ∆χ 2 < 100 than other local minima, including two close models (i.e., “Close Inner” and “Close Outer”) and one wide model. A “hotter” MCMC analysis, …
Figure 9
Figure 9. Figure 9: Caustic crossing geometries of OGLE-2017-BLG-0364. model, and [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]
Figure 10
Figure 10. Figure 10: Observed data and the 2L1S model for KMT-2017- BLG-2331. tral caustic (e.g., Udalski et al. 2005; Dong et al. 2009). The grid search finds only one local minimum whose ∆χ 2 < 100 than other local minima and further investigation including “hotter” MCMC does not locate…
Figure 12
Figure 12. Figure 12: Caustic geometries of the candidate planetary event, KMT-2017-BLG-0958. color–magnitude diagram (CMD, Yoo et al. 2004), which is constructed from the ambient stars around the event. From the CMD, we estimate the centroid of the red-giant clump as (V − I, I) cl, for wh…
Figure 13
Figure 13. Figure 13: Color-magnitude diagram for the four unambiguous planetary events. The CMDs of KMT-2017-BLG-0849, KMT-2017-BLG￾1057, and KMT-2017-BLG-2331 are constructed using the KMTC field stars, and the CMD of OGLE-2017-BLG-0364 is built using the OGLE-III star catalog (Szymanski…
Figure 14
Figure 14. Figure 14: Bayesian posterior distributions of the mass of the host star, Mhost, the planetary mass, Mplanet, the lens distance, DL, the projected planet-host separation, r⊥, and the lens-source relative proper motion in the heliocentric frame, µhel,rel. In each panel, the solid…
Figure 15
Figure 15. Figure 15: Left: Histogram distributions of log q for the 2016–2019 KMTNet AnomalyFinder-discovery (magenta) and AnomlyFinder-recovery (blue) planets, and the planets detected before KMTNet’s regular survey (black). Right: Cumulative distributions of log q for the pre-KMTNet pla…

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.