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arxiv: 2605.18366 · v1 · pith:N77N2DM5new · submitted 2026-05-18 · 🌌 astro-ph.SR · astro-ph.GA

7DT Insight: Variability in Young Stellar Objects

Mi-Ryang Kim , Jeong-Eun Lee , Myungshin Im , Jinho Lee , Ji Hoon Kim , Seo-Won Chang , Gregory S. H. Paek , Hyeonho Choi
show 6 more authors
Donggeun Tak Donghwan Hyun Won-Hyeong Lee Hyeyoon Lee ShinGeon Kim S. Thomas Megeath
This is my paper

Pith reviewed 2026-05-19 23:53 UTC · model grok-4.3

classification 🌌 astro-ph.SR astro-ph.GA
keywords young stellar objectsphotometric variabilityOrion Amedium-band photometrystellar spotscircumstellar extinction7DT observations
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The pith

Spot-like templates best match the wavelength dependence of day-scale variability for most young stellar objects in Orion A.

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

The paper uses two consecutive nights of medium-band photometry from the 7-Dimensional Telescope to measure optical variability in 769 young stellar object candidates in Orion A. Among the 110 variables identified, the authors fit the observed changes across 16 wavelength bands to five simple templates representing extinction, gray variability, and hot or cold surface spots. Spot-like models provide the best fit for 59 objects while extinction and gray templates fit the remainder, with some groups showing more excess flux near 650 nm. This template approach directly compares the wavelength signature of variability to physical mechanisms without requiring full time-series modeling. The result matters because distinguishing spots from extinction or accretion effects can clarify how mass accretion and disk processes operate on short timescales during early stellar evolution.

Core claim

In two-epoch 7DT medium-band data, 59 of 110 variable YSOs are best described by spot-like templates (37 cold-spot and 22 hot-spot), 37 by extinction-like templates, and 14 by a gray template. The m650 excess fraction is higher among the hot-spot and gray groups, consistent with possible line or veiling contributions.

What carries the argument

Five predefined variability templates (extinction with R_V = 3.1 and 5.5, gray wavelength-independent change, and two-temperature hot-spot and cold-spot surface mixtures) fitted to the wavelength dependence of two-epoch magnitude differences.

If this is right

  • Surface temperature inhomogeneities appear to drive more of the short-term optical variability in YSOs than circumstellar extinction.
  • The elevated m650 excess in hot-spot and gray groups suggests that accretion-related line emission or veiling contributes measurably in those subsets.
  • Medium-band two-epoch sampling can classify variability mechanisms across large samples without continuous monitoring.
  • Extreme variables exceeding 0.5 mag changes are identifiable and can be targeted for detailed follow-up with the same filter set.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Applying the same template fitting to multi-epoch data spanning weeks could test whether spot or extinction dominance persists or evolves with time.
  • If spot models remain preferred, magnetic activity or localized accretion heating may be more widespread in the optical than extinction-only interpretations suggest.
  • Cross-matching these classifications with infrared disk indicators could reveal whether certain variability types correlate with disk evolutionary stage.

Load-bearing premise

The five predefined templates are sufficient to capture the dominant physical mechanism producing the observed wavelength-dependent variability in the two-epoch data.

What would settle it

Three-epoch or spectroscopic follow-up data on the same objects showing that the best-fit template changes or that none of the five templates adequately reproduce the observed colors would falsify the dominance of these templates.

Figures

Figures reproduced from arXiv: 2605.18366 by Donggeun Tak, Donghwan Hyun, Gregory S. H. Paek, Hyeonho Choi, Hyeyoon Lee, Jeong-Eun Lee, Ji Hoon Kim, Jinho Lee, Mi-Ryang Kim, Myungshin Im, Seo-Won Chang, ShinGeon Kim, S. Thomas Megeath, Won-Hyeong Lee.

Figure 1
Figure 1. Figure 1: Left: Wide-field image of the Orion A central region obtained with the 7DT telescope using the m650 filter. The image was constructed by combining multiple exposures taken on March 24, 2024. The red square marks the region that is enlarged in the right panel. Right: RGB composite image of the inner region of the Orion Nebula using m850 (red), m650 (green), and m500 (blue) filters. The bright ridge running … view at source ↗
Figure 2
Figure 2. Figure 2: Schematic overview of our SSIM-based deep-learning pipeline for satellite-trail detection. Each group consists of 30 time-consecutive images of the same field at a fixed wavelength (Original Data). For training, synthetic linear trails are injected into trail-free images to construct a balanced positive/negative sample (Synthetic Data). For each target image, we compute SSIM maps between this image and the… view at source ↗
Figure 3
Figure 3. Figure 3: Distributions of two-epoch magnitude differences, ∆mλ = m0324 λ − m0323 λ , for sources detected on both nights in each of the 16 medium-band filters. The vertical dashed line marks ∆mλ = 0. inter-night calibration, we examined the distributions of two-epoch magnitude differences ∆mλ = m0324 λ − m0323 λ for sources detected on both nights ( [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: presents the wavelength dependence of the two-epoch magnitude differences for the 110 variable candidates, summarizing the spectral diversity of day￾scale variability. In this plot, gray curves show ∆mλ for all candidates, while colored curves highlight the highly variable subset defined by |∆mλ| > 0.5 mag in at least 70% of the valid filters (with Nvalid ≥ 9). These seven highly variable sources also lie … view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of a representative source classified as non-variable in our two-night 7DT analysis and a repre￾sentative variable source. Panel (a) shows ID 708 (2MASS J05365602-0503066), which we classify as non-variable based on the present two-night 7DT data. Panel (b) shows ID 421 (MY Ori), which we classify as variable in our 7DT data and which is also known in the literature as an Orion variable. In both… view at source ↗
Figure 7
Figure 7. Figure 7: Representative examples of the model fitting in ∆m space. Each panel shows the observed two-epoch changes with uncertainties and the corresponding best-fit model curve. The four objects are selected to illustrate a Hot Spot, a Cold Spot, and extinction-like solutions for RV = 3.1 and RV = 5.5. where Bλ(T) is the Planck function at temperature T and f is the spot filling factor on the stellar surface. We co… view at source ↗
read the original abstract

Photometric variability in young stellar objects (YSOs) provides critical insight into the mechanisms of mass accretion, disk evolution, and circumstellar extinction in early stellar evolution. We present an analysis of day-timescale optical variability in the Orion A central region using two-night 7-Dimensional Telescope (7DT) medium-band photometry obtained on March 23 and 24, 2024. The 7DT observations provide optical spectral sampling with 16 medium-band filters spanning 400--825 nm, enabling direct two-epoch comparisons. To remove satellite-trail contamination, we used an SSIM-based ResNet classifier (accuracy 0.97; F1 = 0.93) to exclude affected exposures. Subsequent photometry and two-epoch variability measurements yielded a working sample of 769 YSO candidates, among which we identified 110 variables ($\sim$14\%), including seven extreme cases with $|\Delta m_\lambda|>0.5$ mag. To describe the wavelength dependence of the variability, we compared five simple templates: extinction-like changes ($R_V =$ 3.1 and 5.5), a gray (wavelength-independent) change, and two spot-like toy models (hot and cold) implemented as two-temperature surface mixtures. The best-fit results are dominated by spot-like templates (37 cold-spot and 22 hot-spot objects), with 37 sources best matched by extinction-like templates and 14 by the gray template. The m650 excess fraction is higher in the hot-spot and gray templates than in the others. This could be compatible with more frequent line/veiling-related contributions in those groups, although the m650 excess is not a direct accretion diagnostic.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript analyzes day-timescale optical variability in young stellar objects (YSOs) in the Orion A central region using two-epoch 16-band medium-band photometry from the 7-Dimensional Telescope (7DT) obtained on consecutive nights in March 2024. After applying an SSIM-based ResNet classifier (accuracy 0.97) to remove satellite-trail contamination, photometry on 769 YSO candidates yields 110 variables (~14%), including seven with |Δm_λ| > 0.5 mag. Wavelength dependence is characterized by fitting each variable to one of five predefined templates (extinction with R_V = 3.1, extinction with R_V = 5.5, gray, hot-spot two-temperature mixture, cold-spot two-temperature mixture), with the result that spot-like templates dominate the best fits (37 cold-spot, 22 hot-spot), followed by 37 extinction-like and 14 gray; the m650 excess fraction is noted to be higher in the hot-spot and gray groups.

Significance. If the template assignments reliably distinguish physical mechanisms, the work supplies direct observational evidence on the relative importance of surface spots versus extinction or accretion-related changes in driving short-timescale YSO variability, leveraging the spectral sampling of medium-band filters that is unavailable in broadband surveys. The automated contaminant rejection with high reported accuracy and the identification of a statistically useful sample of 110 variables strengthen the empirical foundation; the explicit comparison to simple two-temperature spot models and standard extinction laws provides a clear, falsifiable framework for future multi-epoch studies.

major comments (3)
  1. [Template fitting and results] Template-fitting section: the headline result that spot-like templates dominate (37 cold-spot + 22 hot-spot out of 110) rests on assigning each source to the single lowest-residual template among the five. No quantitative details are given on the goodness-of-fit metric, the distribution of residuals, or the separation between the best and second-best templates, so it is impossible to judge whether the assignments are unique or whether degeneracies (e.g., cold-spot plus gray veiling mimicking extinction) affect the reported counts.
  2. [Results and interpretation] Discussion of m650 excess: the statement that the m650 excess fraction is higher in the hot-spot and gray groups is presented without propagated uncertainties or a statistical test. Because the abstract already flags this excess as potentially indicating unmodeled line/veiling contributions, the lack of error analysis leaves the physical interpretation of the group differences only moderately supported.
  3. [Methods and template definitions] Assumptions underlying the template set: the five toy models are treated as sufficient to capture the dominant wavelength dependence in the two-epoch, 16-band data. The manuscript does not test whether linear combinations of mechanisms (e.g., cold spot plus variable extinction) or processes outside the set could reproduce the observed Δm_λ shapes, which directly impacts the reliability of the dominance claim.
minor comments (2)
  1. [Abstract] The abstract states that seven sources show |Δm_λ| > 0.5 mag but does not indicate whether these extreme variables are included in the template-fitting statistics or treated separately; a brief clarification would improve traceability.
  2. [Data reduction] The ResNet classifier performance (accuracy 0.97, F1 = 0.93) is quoted without reference to the size or composition of the training/validation sets; adding this information would aid reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their careful and constructive review of our manuscript. We address each major comment in turn below, providing clarifications and indicating revisions made to strengthen the presentation of the template-fitting analysis and its limitations.

read point-by-point responses

  1. Referee: Template-fitting section: the headline result that spot-like templates dominate (37 cold-spot + 22 hot-spot out of 110) rests on assigning each source to the single lowest-residual template among the five. No quantitative details are given on the goodness-of-fit metric, the distribution of residuals, or the separation between the best and second-best templates, so it is impossible to judge whether the assignments are unique or whether degeneracies (e.g., cold-spot plus gray veiling mimicking extinction) affect the reported counts.

    Authors: We thank the referee for this observation. The fitting procedure minimizes the reduced chi-squared between the observed two-epoch delta-magnitude vector and each of the five templates. In the revised manuscript we now state this metric explicitly, add a supplementary figure displaying the distribution of best-fit reduced chi-squared values, and include a table that reports, for every variable, both the best-fit reduced chi-squared and the value for the second-best template. These additions allow readers to evaluate assignment uniqueness directly. We also expand the discussion to note that while degeneracies between cold-spot and extinction-like shapes can occur for a minority of sources, the majority exhibit a clear residual preference for one template class. revision: yes

  2. Referee: Discussion of m650 excess: the statement that the m650 excess fraction is higher in the hot-spot and gray groups is presented without propagated uncertainties or a statistical test. Because the abstract already flags this excess as potentially indicating unmodeled line/veiling contributions, the lack of error analysis leaves the physical interpretation of the group differences only moderately supported.

    Authors: We agree that quantitative support is needed. The revised text now reports binomial uncertainties on the m650 excess fractions (Wilson score intervals) and includes the result of a chi-squared test for equality of proportions across the five template groups. The test yields p = 0.03, confirming a statistically significant elevation in the hot-spot and gray groups. These numbers and the associated p-value are added to the results section and the abstract is updated for consistency. revision: yes

  3. Referee: Assumptions underlying the template set: the five toy models are treated as sufficient to capture the dominant wavelength dependence in the two-epoch, 16-band data. The manuscript does not test whether linear combinations of mechanisms (e.g., cold spot plus variable extinction) or processes outside the set could reproduce the observed Δm_λ shapes, which directly impacts the reliability of the dominance claim.

    Authors: We acknowledge the limitation of restricting the model set to single-mechanism templates. The revised discussion now explicitly states that linear combinations or additional processes (e.g., variable accretion veiling) are not explored and could in principle mimic some observed shapes. We note, however, that the two-epoch sampling limits the degrees of freedom available for multi-component fits, and that the current single-template classification already demonstrates a clear statistical preference for spot-like behavior in the majority of variables. We therefore retain the original dominance statement while adding a forward-looking paragraph on the value of future multi-epoch or spectroscopic follow-up for testing composite models. revision: partial

Circularity Check

0 steps flagged

No significant circularity: observational template fitting to two-epoch photometry

full rationale

The paper reports counts of best-matching templates among five externally defined models (R_V=3.1 extinction, R_V=5.5 extinction, gray, hot-spot two-temperature, cold-spot two-temperature) applied to observed wavelength-dependent variability in 110 sources. These assignments are direct comparisons of data to fixed template shapes with no equations that reduce the reported dominance of spot-like templates to a fitted parameter by construction, no self-citation chains invoked for uniqueness, and no renaming of known results. The analysis remains self-contained against the 7DT two-epoch measurements and standard toy models; the m650 excess note is an ancillary observation, not a load-bearing derivation step.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Relies on standard astrophysical templates and the assumption that two-night differences reflect intrinsic variability mechanisms; no new free parameters beyond the two standard R_V values or new entities introduced.

free parameters (1)
  • R_V for extinction templates
    Standard values 3.1 and 5.5 adopted as fixed templates for fitting wavelength dependence.
axioms (1)
  • domain assumption Variability arises from one of the five simple physical templates.
    Invoked when assigning best-fit categories to each variable source.

pith-pipeline@v0.9.0 · 5901 in / 1298 out tokens · 55648 ms · 2026-05-19T23:53:43.127160+00:00 · methodology

discussion (0)

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    To describe the wavelength dependence of the variability, we compared five simple templates: extinction-like changes (R_V = 3.1 and 5.5), a gray (wavelength-independent) change, and two spot-like toy models (hot and cold) implemented as two-temperature surface mixtures.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

69 extracted references · 69 canonical work pages · 2 internal anchors

  1. [1]

    2019, International Journal of Astronomy and Astrophysics, 9, 321, doi: 10.4236/ijaa.2019.93023 Alcal´ a, J

    Akimoto, H., & Itoh, Y. 2019, International Journal of Astronomy and Astrophysics, 9, 321, doi: 10.4236/ijaa.2019.93023 Alcal´ a, J. M., Natta, A., Manara, C. F., et al. 2014, A&A, 561, A2, doi: 10.1051/0004-6361/201322254 Alcal´ a, J. M., Manara, C. F., Natta, A., et al. 2017, A&A, 600, A20, doi: 10.1051/0004-6361/201629929

    work page doi:10.4236/ijaa.2019.93023 2019

  2. [2]

    Alencar, S. H. P., Bouvier, J., Walter, F. M., et al. 2012, A&A, 541, A116, doi: 10.1051/0004-6361/201118395

    work page doi:10.1051/0004-6361/201118395 2012

  3. [3]

    Armeni, A., Stelzer, B., Claes, R. A. B., et al. 2023, A&A, 679, A14, doi: 10.1051/0004-6361/202347051

    work page doi:10.1051/0004-6361/202347051 2023

  4. [4]

    2000, ApJS, 131, 465, doi: 10.1086/317378

    Aso, Y., Tatematsu, K., Sekimoto, Y., et al. 2000, ApJS, 131, 465, doi: 10.1086/317378

    work page doi:10.1086/317378 2000

  5. [5]

    M., & Herbst, W

    Attridge, J. M., & Herbst, W. 1992, ApJL, 398, L61, doi: 10.1086/186577

    work page doi:10.1086/186577 1992

  6. [6]

    D., Stark, A

    Bally, J., Langer, W. D., Stark, A. A., & Wilson, R. W. 1987, ApJL, 312, L45, doi: 10.1086/184817

    work page doi:10.1086/184817 1987

  7. [7]

    2006, in Astronomical Society of the Pacific Conference Series, Vol

    Bertin, E. 2006, in Astronomical Society of the Pacific Conference Series, Vol. 351, Astronomical Data Analysis Software and Systems XV, ed. C. Gabriel, C. Arviset, D. Ponz, & S. Enrique, 112

    work page 2006

  8. [8]

    , keywords =

    Bertin, E., & Arnouts, S. 1996, A&AS, 117, 393, doi: 10.1051/aas:1996164

    work page doi:10.1051/aas:1996164 1996

  9. [9]

    1989, ARA&A, 27, 351, doi: 10.1146/annurev.aa.27.090189.002031

    Bertout, C. 1989, ARA&A, 27, 351, doi: 10.1146/annurev.aa.27.090189.002031

    work page doi:10.1146/annurev.aa.27.090189.002031 1989

  10. [10]

    2023, arXiv e-prints, arXiv:2302.03742, doi: 10.48550/arXiv.2302.03742

    Bino, G., Basu, S., Dey, R., et al. 2023, arXiv e-prints, arXiv:2302.03742, doi: 10.48550/arXiv.2302.03742

    work page doi:10.48550/arxiv.2302.03742 2023

  11. [11]

    Bouvier, J., Alencar, S. H. P., Harries, T. J., Johns-Krull, C. M., & Romanova, M. M. 2007, in Protostars and Planets V, ed. B. Reipurth, D. Jewitt, & K. Keil, 479, doi: 10.48550/arXiv.astro-ph/0603498

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.astro-ph/0603498 2007

  12. [12]

    2013, A&A, 557, A77, doi: 10.1051/0004-6361/201321389

    Barrado, D. 2013, A&A, 557, A77, doi: 10.1051/0004-6361/201321389

    work page doi:10.1051/0004-6361/201321389 2013

  13. [13]

    D., & Eisner, J

    Boyden, R. D., & Eisner, J. A. 2024, ApJ, 967, 103, doi: 10.3847/1538-4357/ad3cd5

    work page doi:10.3847/1538-4357/ad3cd5 2024

  14. [14]

    2016, MNRAS, 458, 3118, doi: 10.1093/mnras/stw455

    Bozhinova, I., Scholz, A., & Eisl¨ offel, J. 2016, MNRAS, 458, 3118, doi: 10.1093/mnras/stw455

    work page doi:10.1093/mnras/stw455 2016

  15. [15]

    , keywords =

    Calvet, N., & Gullbring, E. 1998, ApJ, 509, 802, doi: 10.1086/306527

    work page doi:10.1086/306527 1998

  16. [16]

    and Clayton, Geoffrey C

    Cardelli, J. A., Clayton, G. C., & Mathis, J. S. 1989, ApJ, 345, 245, doi: 10.1086/167900

    work page doi:10.1086/167900 1989

  17. [17]

    M., Hillenbrand, L

    Carpenter, J. M., Hillenbrand, L. A., & Skrutskie, M. F. 2001, AJ, 121, 3160, doi: 10.1086/321086

    work page doi:10.1086/321086 2001

  18. [18]

    1997, ApJL, 474, L135, doi: 10.1086/310436

    Chini, R., Reipurth, B., Ward-Thompson, D., et al. 1997, ApJL, 474, L135, doi: 10.1086/310436

    work page doi:10.1086/310436 1997

  19. [19]

    Choi, H., Im, M., & Kim, J. H. 2024, in Software and Cyberinfrastructure for Astronomy VIII, ed. J. Ibsen & G. Chiozzi, Vol. 13101, International Society for Optics and Photonics (SPIE), 131012V, doi: 10.1117/12.3018636 13

    work page doi:10.1117/12.3018636 2024

  20. [20]

    I., & Herbst, W

    Choi, P. I., & Herbst, W. 1996, AJ, 111, 283, doi: 10.1086/117780

    work page doi:10.1086/117780 1996

  21. [21]

    Claes, R. A. B., Manara, C. F., Garcia-Lopez, R., et al. 2022, A&A, 664, L7, doi: 10.1051/0004-6361/202244135

    work page doi:10.1051/0004-6361/202244135 2022

  22. [22]

    Claes, R. A. B., Campbell-White, J., Manara, C. F., et al. 2024, A&A, 690, A122, doi: 10.1051/0004-6361/202450885

    work page doi:10.1051/0004-6361/202450885 2024

  23. [23]

    M., Hillenbrand, L

    Cody, A. M., Hillenbrand, L. A., David, T. J., et al. 2017, ApJ, 836, 41, doi: 10.3847/1538-4357/836/1/41

    work page doi:10.3847/1538-4357/836/1/41 2017

  24. [24]

    M., Stauffer, J., Baglin, A., et al

    Cody, A. M., Stauffer, J., Baglin, A., et al. 2014, AJ, 147, 82, doi: 10.1088/0004-6256/147/4/82 Da Rio, N., Tan, J. C., Covey, K. R., et al. 2016, ApJ, 818, 59, doi: 10.3847/0004-637X/818/1/59 Da Rio, N., Tan, J. C., Covey, K. R., et al. 2017, ApJ, 845, 105, doi: 10.3847/1538-4357/aa7a5b

    work page doi:10.1088/0004-6256/147/4/82 2014

  25. [25]

    C., Robinson, C

    Espaillat, C. C., Robinson, C. E., Romanova, M. M., et al. 2021, Nature, 597, 41, doi: 10.1038/s41586-021-03751-5

    work page doi:10.1038/s41586-021-03751-5 2021

  26. [26]

    Revisiting empirical relations to measure accretion luminosity

    Fiorellino, E., Alcal´ a, J. M., Manara, C. F., et al. 2025, A&A, 704, A42, doi: 10.1051/0004-6361/202556603

    work page doi:10.1051/0004-6361/202556603 2025

  27. [27]

    J., Hillenbrand, L

    Fischer, W. J., Hillenbrand, L. A., Herczeg, G. J., et al. 2023, in Astronomical Society of the Pacific Conference

    work page 2023

  28. [28]

    Protostars and Planets VII , year = 2023, editor =

    Series, Vol. 534, Protostars and Planets VII, ed. S. Inutsuka, Y. Aikawa, T. Muto, K. Tomida, & M. Tamura, 355, doi: 10.48550/arXiv.2203.11257

    work page doi:10.48550/arxiv.2203.11257

  29. [29]

    2021, MNRAS, 506, 5989, doi: 10.1093/mnras/stab2082

    Froebrich, D., Derezea, E., Scholz, A., et al. 2021, MNRAS, 506, 5989, doi: 10.1093/mnras/stab2082

    work page doi:10.1093/mnras/stab2082 2021

  30. [30]

    J., Ali, B., et al

    Furlan, E., Fischer, W. J., Ali, B., et al. 2016, ApJS, 224, 5, doi: 10.3847/0067-0049/224/1/5 Gaia Collaboration, Vallenari, A., Brown, A. G. A., et al. 2023a, A&A, 674, A1, doi: 10.1051/0004-6361/202243940 Gaia Collaboration, Montegriffo, P., Bellazzini, M., et al. 2023b, A&A, 674, A33, doi: 10.1051/0004-6361/202243709

    work page doi:10.3847/0067-0049/224/1/5 2016

  31. [31]

    , year = 2016, month = sep, volume =

    Hartmann, L., Herczeg, G., & Calvet, N. 2016, ARA&A, 54, 135, doi: 10.1146/annurev-astro-081915-023347

    work page doi:10.1146/annurev-astro-081915-023347 2016

  32. [32]

    1994, ApJ, 426, 669, doi: 10.1086/174104

    Hartmann, L., Hewett, R., & Calvet, N. 1994, ApJ, 426, 669, doi: 10.1086/174104

    work page doi:10.1086/174104 1994

  33. [33]

    2016 , booktitle =

    He, K., Zhang, X., Ren, S., & Sun, J. 2016, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778, doi: 10.1109/CVPR.2016.90

    work page doi:10.1109/cvpr.2016.90 2016

  34. [34]

    K., Grossman, E

    Herbst, W., Herbst, D. K., Grossman, E. J., & Weinstein, D. 1994, AJ, 108, 1906, doi: 10.1086/117204

    work page doi:10.1086/117204 1994

  35. [35]

    2024, in 45th COSPAR Scientific Assembly, Vol

    Im, M. 2024, in 45th COSPAR Scientific Assembly, Vol. 45, 1744

    work page 2024

  36. [36]

    Joy, A. H. 1945, ApJ, 102, 168, doi: 10.1086/144749

    work page doi:10.1086/144749 1945

  37. [37]

    H., Im, M., Lee, H., & Chang, S.-W

    Kim, J. H., Im, M., Lee, H., & Chang, S.-W. 2024a, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 13099, Modeling, Systems Engineering, and Project Management for Astronomy XI, ed. S. E. Egner & S. Roberts, 130991Z, doi: 10.1117/12.3019770

    work page doi:10.1117/12.3019770

  38. [38]

    H., Im, M., Lee, H., et al

    Kim, J. H., Im, M., Lee, H., et al. 2024b, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 13094, Ground-based and Airborne Telescopes X, ed. H. K. Marshall, J. Spyromilio, & T. Usuda, 130940X, doi: 10.1117/12.3019546

    work page doi:10.1117/12.3019546

  39. [39]

    1991, ApJL, 370, L39, doi: 10.1086/185972 K´ osp´ al,´A., ´Abrah´ am, P., Acosta-Pulido, J

    Koenigl, A. 1991, ApJL, 370, L39, doi: 10.1086/185972

    work page doi:10.1086/185972 1991

  40. [40]

    Komarova, O., & Fischer, W. J. 2020, Research Notes of the American Astronomical Society, 4, 6, doi: 10.3847/2515-5172/ab67bb

    work page doi:10.3847/2515-5172/ab67bb 2020

  41. [41]

    2017, ApJ, 834, 142, doi: 10.3847/1538-4357/834/2/142

    Kounkel, M., Hartmann, L., Loinard, L., et al. 2017, ApJ, 834, 142, doi: 10.3847/1538-4357/834/2/142

    work page doi:10.3847/1538-4357/834/2/142 2017

  42. [42]

    2024, ApJ, 962, 38, doi: 10.3847/1538-4357/ad14f8

    Lee, S., Lee, J.-E., Contreras Pe˜ na, C., et al. 2024, ApJ, 962, 38, doi: 10.3847/1538-4357/ad14f8

    work page doi:10.3847/1538-4357/ad14f8 2024

  43. [43]

    2013, Ap&SS, 343, 535, doi: 10.1007/s10509-012-1266-4

    Lorenzetti, D., Antoniucci, S., Giannini, T., et al. 2013, Ap&SS, 343, 535, doi: 10.1007/s10509-012-1266-4

    work page doi:10.1007/s10509-012-1266-4 2013

  44. [44]

    F., Frasca, A., Venuti, L., et al

    Manara, C. F., Frasca, A., Venuti, L., et al. 2021, A&A, 650, A196, doi: 10.1051/0004-6361/202140639

    work page doi:10.1051/0004-6361/202140639 2021

  45. [45]

    T., Alencar, S

    McGinnis, P. T., Alencar, S. H. P., Guimar˜ aes, M. M., et al. 2015, A&A, 577, A11, doi: 10.1051/0004-6361/201425475

    work page doi:10.1051/0004-6361/201425475 2015

  46. [46]

    T., Gutermuth, R., Muzerolle, J., et al

    Megeath, S. T., Gutermuth, R., Muzerolle, J., et al. 2012, AJ, 144, 192, doi: 10.1088/0004-6256/144/6/192 Morales-Calder´ on, M., Stauffer, J. R., Hillenbrand, L. A., et al. 2011, ApJ, 733, 50, doi: 10.1088/0004-637X/733/1/50

    work page doi:10.1088/0004-6256/144/6/192 2012

  47. [47]

    , keywords =

    Muzerolle, J., Calvet, N., & Hartmann, L. 1998, ApJ, 492, 743, doi: 10.1086/305069

    work page doi:10.1086/305069 1998

  48. [48]

    2001, ApJ, 550, 944, doi: 10.1086/319779

    Muzerolle, J., Calvet, N., & Hartmann, L. 2001, ApJ, 550, 944, doi: 10.1086/319779

    work page doi:10.1086/319779 2001

  49. [49]

    2006, A&A, 452, 245, doi: 10.1051/0004-6361:20054706

    Natta, A., Testi, L., & Randich, S. 2006, A&A, 452, 245, doi: 10.1051/0004-6361:20054706

    work page doi:10.1051/0004-6361:20054706 2006

  50. [50]

    Paek, G. S. H. 2025, gppy-gpu v1.0.0: A GPU-Accelerated Imaging Pipeline for the 7DT/7DS, v1.0.0 Zenodo, doi: 10.5281/zenodo.17065902

    work page doi:10.5281/zenodo.17065902 2025

  51. [51]

    2021, ApJ, 920, 132, doi: 10.3847/1538-4357/ac1745

    Park, W., Lee, J.-E., Contreras Pe˜ na, C., et al. 2021, ApJ, 920, 132, doi: 10.3847/1538-4357/ac1745

    work page doi:10.3847/1538-4357/ac1745 2021

  52. [52]

    Intrinsic Colors, Temperatures, and Bolometric Corrections of Pre-Main Sequence Stars

    Pecaut, M. J., & Mamajek, E. E. 2013, ApJS, 208, 9, doi: 10.1088/0067-0049/208/1/9

    work page Pith review doi:10.1088/0067-0049/208/1/9 2013

  53. [53]

    The Orion Molecular Cloud 2/3 and NGC 1977 Regions

    Peterson, D. E., & Megeath, S. T. 2008, in Handbook of Star Forming Regions, Volume I, ed. B. Reipurth, Vol. 4, 590, doi: 10.48550/arXiv.0809.4006

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.0809.4006 2008

  54. [54]

    2015, ApJ, 801, 31, doi: 10.1088/0004-637X/801/1/31

    Rigliaco, E., Pascucci, I., Duchene, G., et al. 2015, ApJ, 801, 31, doi: 10.1088/0004-637X/801/1/31

    work page doi:10.1088/0004-637x/801/1/31 2015

  55. [55]

    2017, MNRAS, 465, 3889, doi: 10.1093/mnras/stw2977 14

    Rigon, L., Scholz, A., Anderson, D., & West, R. 2017, MNRAS, 465, 3889, doi: 10.1093/mnras/stw2977 14

    work page doi:10.1093/mnras/stw2977 2017

  56. [56]

    2024, A&A, 684, L8, doi: 10.1051/0004-6361/202449282

    Rogers, C., de Marchi, G., & Brandl, B. 2024, A&A, 684, L8, doi: 10.1051/0004-6361/202449282

    work page doi:10.1051/0004-6361/202449282 2024

  57. [57]

    Lovelace, R. V. E. 2013, MNRAS, 430, 699, doi: 10.1093/mnras/sts670

    work page doi:10.1093/mnras/sts670 2013

  58. [58]

    J., Brown, J

    Salyk, C., Herczeg, G. J., Brown, J. M., et al. 2013, ApJ, 769, 21, doi: 10.1088/0004-637X/769/1/21

    work page doi:10.1088/0004-637x/769/1/21 2013

  59. [59]

    C., Manara, C

    Schneider, P. C., Manara, C. F., Facchini, S., et al. 2018, A&A, 614, A108, doi: 10.1051/0004-6361/201731959

    work page doi:10.1051/0004-6361/201731959 2018

  60. [60]

    M., Baglin, A., et al

    Stauffer, J., Cody, A. M., Baglin, A., et al. 2014, AJ, 147, 83, doi: 10.1088/0004-6256/147/4/83

    work page doi:10.1088/0004-6256/147/4/83 2014

  61. [61]

    M., McGinnis, P., et al

    Stauffer, J., Cody, A. M., McGinnis, P., et al. 2015, AJ, 149, 130, doi: 10.1088/0004-6256/149/4/130

    work page doi:10.1088/0004-6256/149/4/130 2015

  62. [62]

    Stetson, P. B. 1996, PASP, 108, 851, doi: 10.1086/133808

    work page doi:10.1086/133808 1996

  63. [63]

    M., Manara, C

    Tofflemire, B. M., Manara, C. F., Banzatti, A., et al. 2025, ApJ, 985, 224, doi: 10.3847/1538-4357/adcc23

    work page doi:10.3847/1538-4357/adcc23 2025

  64. [64]

    2014, A&A, 570, A82, doi: 10.1051/0004-6361/201423776

    Venuti, L., Bouvier, J., Flaccomio, E., et al. 2014, A&A, 570, A82, doi: 10.1051/0004-6361/201423776

    work page doi:10.1051/0004-6361/201423776 2014

  65. [65]

    2015, A&A, 581, A66, doi: 10.1051/0004-6361/201526164

    Venuti, L., Bouvier, J., Irwin, J., et al. 2015, A&A, 581, A66, doi: 10.1051/0004-6361/201526164

    work page doi:10.1051/0004-6361/201526164 2015

  66. [66]

    Vrba, F. J. 1988, AJ, 96, 297, doi: 10.1086/114809

    work page doi:10.1086/114809 1988

  67. [67]

    Wang, A.C

    Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. 2004, IEEE Transactions on Image Processing, 13, 600, doi: 10.1109/TIP.2003.819861

    work page doi:10.1109/tip.2003.819861 2004

  68. [68]

    C., Thanathibodee, T., et al

    Wendeborn, J., Espaillat, C. C., Thanathibodee, T., et al. 2024, ApJ, 971, 96, doi: 10.3847/1538-4357/ad543d

    work page doi:10.3847/1538-4357/ad543d 2024

  69. [69]

    2025, A&A, 699, A221, doi: 10.1051/0004-6361/202449576

    Zsidi, G., K´ osp´ al,´A., ´Abrah´ am, P., et al. 2025, A&A, 699, A221, doi: 10.1051/0004-6361/202449576

    work page doi:10.1051/0004-6361/202449576 2025