Pith. sign in

REVIEW 2 major objections 3 minor 1 cited by

New Evidence for a Flux-independent Spectral Index of Sgr A* in the Near-infrared

T0 review · 2 major / 3 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Seven epochs of ground-based near-infrared imaging show Sgr A*’s spectral index stays constant at −0.50 over a factor of about 40 in brightness, ruling out a purely cutoff-driven synchrotron explanation of its variability.

desk verdict Careful, honest measurement of Sgr A*'s NIR color over the widest range yet; the constant-α conclusion holds for the bright subset and is conditional on assumed confusion colors at the faint end. read the letter →

arxiv 2411.11966 v1 pith:6FDCCB5N submitted 2024-11-18 astro-ph.HE astro-ph.GA

Hadrien Paugnat (1) , Tuan Do (1) , Abhimat K. Gautam (1) , Gregory D. Martinez (1) , Andrea M. Ghez (1) , Shoko Sakai (1) , Grant C. Weldon (1) , Matthew W. Hosek Jr. (1)
show 11 more authors
Zoë Haggard (1) Kelly Kosmo O'Neil (1 2) Eric E. Becklin (1) Gunther Witzel (3) Jessica R. Lu (4) Keith Matthews (5) ((1) UCLA (2) University of Nevada Reno (3) Max Planck Institute for Radio Astronomy (4) UC Berkeley (5) Caltech)
This is my paper · ORCID
classification astro-ph.HEastro-ph.GA
keywords spectralindexSagittariusA*near-infraredvariabilitymulti-outputGaussianprocesssynchrotroncutoffGalacticcentersupermassiveblackholeinfraredphotometry
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 asks whether the near-infrared color of Sagittarius A* changes as the black hole’s emission brightens and fades. Using seven epochs of ground-based adaptive-optics imaging in the $H$ and $K^\prime$ bands, the authors correct for overlapping starlight, interpolate the two light curves jointly with a multi-output Gaussian process, and fit an empirical model for the spectral index $\alpha$ (defined by $F_\nu\propto\nu^{\alpha}$). They find that $\alpha_{H-K^\prime}=-0.50\pm0.08_{\rm stat}\pm0.17_{\rm sys}$ fits the data from roughly 1 mJy to 40 mJy of dereddened 2 µm flux, with no evidence that the intrinsic spectral index depends on brightness. If correct, the mechanism driving Sgr A*’s infrared variability changes the overall level of emission but not the shape of the synchrotron spectrum, and the picture in which variability comes purely from shifts of the synchrotron cutoff frequency is ruled out.

What carries the argument

The $H-K^\prime$ spectral index $\alpha$ (with $F_\nu\propto\nu^{\alpha}$) is the central observable, since it tracks the slope of the synchrotron spectrum between 1.6 and 2.2 µm. Two tools carry the analysis: a multi-output Gaussian process (a joint interpolation of the two light curves that lets the correlation between bands be learned rather than fixed) and a four-parameter empirical model (slope $\xi$, intercept $\eta$, background magnitude, background spectral index) that converts an intrinsic spectral index law plus noise and background contamination into predicted measurements. The model’s slope $\xi$ discriminates among scenarios: $\xi=0$ is a constant spectral index, $\xi=1.23$ is the exponential cutoff model, and a bright red background encodes the two-state/quiescent-emission picture. The data prefer $\xi\approx 0$ and constrain the background by marginalizing over it in a Bayesian fit.

What would settle it

Measure the $H$-band magnitudes of S0-38 and S0-104 in epochs when they are not confused with Sgr A*, or with higher-resolution imaging, and recompute the confusion correction with the measured colors. If the true $(H-K^\prime)$ colors deviate substantially from the assumed $0.1\pm0.05$ and $0.0\pm0.1$, the corrected faint spectral indices would shift, and the constant-$\alpha$ conclusion either survives with revised colors or turns into a flux-dependent trend.

Watch

Extended reading notes

Core claim

The central claim is that Sgr A*-NIR has a flux-independent spectral index over the largest brightness range yet probed. After removing the flux of two stars confused with the black hole and interpolating between interleaved $H$ and $K^\prime$ frames, the best fit is $\alpha_{H-K^\prime}=-0.50\pm0.08_{\rm stat}\pm0.17_{\rm sys}$, constant from about 1 mJy to about 40 mJy of dereddened 2 µm flux (a factor of roughly 40 in brightness). The linear-slope parameter $\xi$ describing any magnitude dependence is consistent with zero, while the exponential cutoff model, defined by the predicted slope $\xi=1.23$, is excluded at about 5$\sigma$ with the bright subset and about 6$\sigma$ with the extended dataset. The same model places an upper limit of about 0.3 mJy (dereddened 2 µm flux, 95% confidence) on any red, quiescent emission component. The authors conclude that NIR variability is not caused by shifts of the synchrotron cutoff frequency alone and that the variable emission mechanism alters the normalization, not the shape, of the electron energy distribution that produces the NIR radiation.

Load-bearing premise

The load-bearing premise is that the two stars blended with Sgr A* (S0-38 and S0-104) have the assumed intrinsic $H-K^\prime$ colors used to derive their $H$-band magnitudes, since those colors set how much starlight is removed at faint fluxes and therefore determine whether the spectral index stays constant.

Editorial extensions

If this is right

  • If the constant spectral index holds, Sgr A*’s near-infrared flares are best explained by a variable number of electrons injected with the same power-law energy distribution ($p\approx 2$), rather than by a moving synchrotron cutoff.
  • The exponential cutoff model is excluded as the sole driver of NIR variability, so models must include another mechanism for the flux changes.
  • A red quiescent component, if it exists, contributes less than about 0.3 mJy of dereddened 2 µm flux, in tension with earlier two-state model predictions of roughly 0.7–1.1 mJy.
  • Earlier reports of dramatic reddening at faint fluxes are likely explained by background subtraction methods or unresolved stellar contamination rather than by the black hole’s intrinsic emission.
  • The bright-state spectral index of about $\alpha\approx-0.6$ extends to flux densities about 2.8 times brighter than previously measured, including the 2019 May 13 record flare.

Reading between the lines

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

  • A direct test would be to obtain unconfused $H$-band photometry of S0-38 and S0-104; if their true intrinsic colors are redder or bluer than assumed, the faint-end corrected spectral indices would shift systematically, potentially reviving a flux dependence.
  • If the constant index extends to even fainter fluxes with future larger telescopes, the two-state model would need to place its transition below about 0.3 mJy, making the quiescent component nearly invisible.
  • Measuring colors across more than two bands (for example $H$, $K^\prime$, and $M$) during the same flares would test whether the spectral energy distribution is a true power law or has curvature that two-band photometry cannot see.
  • The same joint-interpolation plus confusion-correction approach could be applied to other crowded-field variable sources, where apparent color–flux trends are often contaminated by unresolved stars.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 3 minor

Summary. The paper analyzes seven epochs (2005, 2019, and 2022) of Keck/NIRC2 imaging of Sgr A* in H and K′ bands, measuring the near-infrared spectral index α (Fν ∝ να) over the largest brightness range studied to date, roughly a factor of 40 in dereddened Ks flux. The analysis includes a star-planting-based correction for two stellar sources confused with Sgr A*, a Multi-Output Gaussian Process interpolation of the interleaved H and K′ lightcurves, and a flexible empirical model with free parameters for the slope ξ of α versus K′ magnitude, the intercept η, and the magnitude and spectral index of any background contribution. The authors find that the data prefer a constant spectral index α = −0.50 ± 0.08_stat ± 0.17_sys over roughly 1–40 mJy, disfavor the exponential-cutoff model of Witzel et al. (2018) at about 5–6σ, and place a 95% upper limit of about 0.3 mJy on redder quiescent emission.

Significance. If the result holds, it is a substantial observational constraint: it extends the flux range over which Sgr A*'s NIR spectral index is constant by roughly a factor of three compared with previous work, and it directly challenges the idea that NIR variability is driven primarily by shifts of the synchrotron cutoff frequency. The paper is unusually careful in several respects: the confusion correction is validated with star-planting simulations, the MOGP interpolation is tested with leave-one-out residuals (Appendix E.3), completeness limits are quantified, and the extinction systematic is separated from statistical error. The Bayesian model comparison is transparent, and the machine-readable table accompanying Figure 4 will be useful to the community. The main weakness identified in this report is a specific robustness gap concerning the assumed intrinsic colors of the confusing stars; the overall methodology is sound.

major comments (2)
  1. [3.2 / Table 5 / Eq. (2)] The faint-end constancy claim depends on the assumed intrinsic H−K′ colors of the two confusing stars, and this assumption is not stress-tested. S0-38 and S0-104 have no direct H-band measurement; their H magnitudes are set by (H−K′)_int = 0.1 ± 0.05 and 0.0 ± 0.1 (Table 5), and the star-planting correction then subtracts stellar contamination using these values. Equation (2) gives dα/d(H−K′) ≈ −3.5, so a 0.1–0.2 mag error in either star's assumed color, if it propagates into the recovered Sgr A* color, shifts α by roughly 0.35–0.7, several times the quoted statistical error and larger than the quoted 0.17 systematic. Table 4 shows confusion corrections Δα up to 1.88 precisely where the extended dataset adds information. The disappearance of the bluer-when-fainter trend in Figure 3 is therefore partly enforced by subtracting a stellar component whose H-band brightness is an assumption, and cannot by itself validate the correction. I ask the authors to add a robustness test that varies (H−K′)_int for S0-38 and S0-104 by plausible amounts, including a mismatched combination for the spectrally unknown S0-104, re-runs the star-planting/confusion-correction and the Section 5.2 inference, and reports how ξ and η move and whether the constant-α model remains preferred.
  2. [5.2 / Figure 7 / Table 7] The paper states that the extended dataset rules out the exponential cutoff model at about 6σ and gives a constant α over the full 1–40 mJy range, but the faint end contributes little independent statistical weight and the headline constancy is largely carried by the bright subset. From Figure 7, the ξ posteriors from the bright subset (−0.19+0.22/−0.19) and the extended dataset (0.21+0.17/−0.16) are both within about 1–1.3σ of zero, and Section 5.2 itself notes that most of the constraining power comes from the bright points. This is not a flaw in itself, but it means the claimed full-range constancy is a statement about the absence of a visible faint-end trend in noisier, heavily corrected data. The sensitivity test requested above should therefore be paired with a presentation of the faint-end subsample alone, for example the points with 16.5 < m_K′ < 17.2, showing the fits with ξ = 0 and ξ = 1.23, so that the reader can see how much of the conclusion depends on the faint points as opposed to extrapolation from the bright subset.
minor comments (3)
  1. [4.1.2 / Eq. (4)] The label 'exponential cutoff model' is defined by fixing ξ = 1.23 while leaving η free; the text should state explicitly that the full Witzel et al. (2018) model also predicts η through Eq. (4), so the 5–6σ exclusion applies to the predicted slope rather than to the complete two-parameter model.
  2. [5.1 / 5.2] The abstract quotes α = −0.50, while Sections 5.1 and 5.2 and Figure 8 report η = −0.49 ± 0.08; the text should state explicitly that −0.50 is the rounded central value of the extended-dataset posterior to avoid an apparent inconsistency.
  3. [Appendix G / Eq. (G25)] The derivation of the likelihood in Appendix G is compact because several variables (H_est, F_obs_H,SgrA, F_obs_H,bck) are defined in the flowchart of Figure 5; defining them again in the appendix would make the Jacobian computation in Eq. (G27) easier to verify.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the cutoff-slope test is a re-derived, falsifiable prediction and the faint-end color assumption is a stated systematic limitation, not a circular reduction.

full rationale

The central derivation is an empirical fit: ξ, η, m_bck and α_bck are free parameters (Section 4.2, Table 6), and the constant-slope conclusion is drawn from Bayesian evidence/AIC/BIC, not from renaming a fit as a prediction. The one imported quantity that could look load-bearing is the exponential-cutoff slope ξ = 1.23 from Witzel et al. (2018), which shares an author with this paper; however, Section 4.1.2 re-derives that value from wavelength ratios (Eq. 4), and the test uses new Keck data that reject it, so the citation is not being used as unverified support. The faint-end constant-α claim does depend on assumed intrinsic colors for the confusing stars S0-38 and S0-104 (Section 3.2, Table 5), and the paper explicitly flags this as an educated guess in Section 6.3; that is a genuine systematic-uncertainty limitation that could bias the extended dataset, but it is not circular because the colors are set from stellar classification and literature, not from the target spectral index. The bright subset (m_K' < 16.5) independently rules out the cutoff model at about 5σ, so the principal rejection does not rely on the faint-end correction. Overall, no step in the claimed derivation reduces by construction to its own input.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The central claim is an observational fit with four fitted model parameters (ξ, η, m_bck, α_bck), several calibration inputs from the literature (S0-2 extinction, zero-points, confusing-star colors), and a set of modeling assumptions (linear α-magnitude relation, additive background, MOGP kernel choice). No invented physical entities are introduced. The free parameters are honestly labeled as fits; the main deliverable is the fitted slope ξ consistent with zero, not a derived prediction.

free parameters (6)
  • ξ (slope of α vs. magnitude) = −0.19 ± 0.22 (bright); 0.21 ± 0.17 (extended)
    Slope in Eq. 3; fitted, and the paper's central result is that it is consistent with 0.
  • η (spectral index at m0 = 15.8) = −0.50 ± 0.08 stat, ±0.17 sys
    Reported constant spectral index; the Gaussian prior N(−0.5, 1) is weak relative to the posterior width of 0.08.
  • m_bck^K' (background magnitude in K') = ≥ 20.3 (95%, stellar, extended); ≥ 18.3 (95%, red, extended)
    Equivalent magnitude of the unresolved background or quiescent component; constrained only to lower limits.
  • α_bck (background spectral index) = Unconstrained; degenerate with η when α_bck ≈ η
    Posterior shows a degeneracy reported in Figure 8; relevant to the quiescent-emission upper limit.
  • m0_K' (reference magnitude) = 15.8
    Chosen near the sample's weighted mean magnitude to ease interpretation of η; a coordinate choice that does not change the fit.
  • Noise law C_band, β_band per night = e.g., log10 C_K' = −1.56 ± 0.09, β_K' = 0.50 ± 0.03 (Table 3)
    Per-night power-law photometric uncertainty fits (Eq. 1); used for error bars, ancillary to the central claim.
assumptions (6)
  • domain assumption Additive background plus uncorrelated Gaussian white noise per band (Eq. 7)
    The empirical model adds background flux linearly and treats noise as uncorrelated Gaussians; the two-state interpretation is acknowledged in Section 4.2 to be approximate since flaring and quiescent processes may not be additive.
  • domain assumption Intrinsic spectral index is a linear function of magnitude (Eq. 3)
    The parameterization α_s = −0.4 ξ (m − 15.8) + η cannot capture a nonlinear flux dependence except through the background term; this shapes the definition of 'flux independence' being tested.
  • domain assumption Color excess derived from S0-2, E(H−K') = 2.07 ± 0.05, applies to Sgr A*
    Extinction correction assumes S0-2 is a B0-2V star of known intrinsic color and that differential extinction between S0-2 and Sgr A* is negligible; a wrong extinction shifts all α values by the 0.17 systematic (Section 3.1, Appendix C).
  • domain assumption Assumed intrinsic colors of confusing stars: S0-38 (H−K')_int = 0.1 ± 0.05; S0-104 (H−K')_int = 0.0 ± 0.1
    H-band magnitudes of the stars confused with Sgr A* are derived, not measured (Section 3.2, Table 5); biases here propagate into the confusion correction at faint fluxes.
  • domain assumption MOGP kernel: RBF + exponential + constant + white noise, with 0 ≤ l_exp ≤ 10 min
    Interpolation fidelity depends on the kernel capturing the true cross-band correlated variability; supported by leave-one-out tests (Appendix E.3).
  • domain assumption Cutoff-shift model of Witzel et al. (2018) implies ξ = 1.23
    The tested prediction is imported from a prior paper with author overlap; the present paper fixes the slope to 1.23 and leaves the intercept η free (Section 4.1.2).

how reviews work

0 comments
Cite this review

Pith. "Pith review of New Evidence for a Flux-independent Spectral Index of Sgr A* in the Near-infrared." pith.science (2026). https://pith.science/paper/6FDCCB5N

@misc{pith2026241111966,
  author       = {Pith},
  title        = {Pith review of: New Evidence for a Flux-independent Spectral Index of Sgr A* in the Near-infrared},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6FDCCB5N}},
  note         = {Machine review of arXiv:2411.11966}
}
abstract

In this work, we measure the spectral index of Sagittarius A* (Sgr A*) between the $H$ (1.6 $\mu$m) and $K^\prime$ (2.2 $\mu$m) broadband filters in the near-infrared (NIR), sampling over a factor $\sim 40$ in brightness, the largest range probed to date by a factor $\sim 3$. Sgr A*-NIR is highly variable, and studying the spectral index $\alpha$ (with $F_\nu \propto \nu^{\alpha}$) is essential to determine the underlying emission mechanism. For example, variations in $\alpha$ with flux may arise from shifts in the synchrotron cutoff frequency, changes in the distribution of electrons, or multiple concurrent emission mechanisms. We investigate potential variations of $\alpha_{H-K^\prime}$ with flux by analyzing 7 epochs (2005 to 2022) of Keck Observatory imaging observations from the Galactic Center Orbits Initiative (GCOI). We remove the flux contribution of known sources confused with Sgr A*-NIR, which can significantly impact color at faint flux levels. We interpolate between the interleaved $H$ and $K^\prime$ observations using Multi-Output Gaussian Processes. We introduce a flexible empirical model to quantify $\alpha$ variations and probe different scenarios. The observations are best fit by an $\alpha_{H-K^\prime} = - 0.50 \pm 0.08 _{\rm stat} \pm 0.17_{\rm sys}$ that is constant from $\sim 1$ mJy to $\sim 40$ mJy (dereddened 2 $\mu$m flux). We find no evidence for a flux-dependence of Sgr A*'s intrinsic spectral index. In particular, we rule out a model explaining NIR variability purely by shifts in the synchrotron cutoff frequency. We also constrain the presence of redder, quiescent emission from the black hole, concluding that the dereddened 2 $\mu$m flux contribution must be $\leq 0.3$ mJy at 95% confidence level.

Figures

Figures reproduced from arXiv: 2411.11966 by the authors.

Figure 1
Figure 1. Top row: example images in K′ (red) and H (blue) from 2022 May 21, zoomed in on a 1′′ × 1 ′′ box around Sgr A*, showcasing its large variations in brightness. The bright nearby stars S0-2 and S0-6 (mK′ ∼ 14, mH ∼ 16) are labeled for reference. Bottom panel: Sgr A*-NIR lightcurves in H and K′ -band for the same epoch, interpolated using the method described in section 3.3. The green arrows show which points in the li… view at source ↗
Figure 2
Figure 2. Comparison of the uncorrected (grey) and confusion corrected (red) lightcurves for 2022 Aug 19. The inset shows the posterior on the predicted position (relative to Sgr A*) of the star S0-38 confused with Sgr A*-NIR, inferred from orbital fits to long-term GC astrometric data (see Appendix D.1). that all epochs were going through the same analysis steps. In the absence of a confusing source, this amounts to checking… view at source ↗
Figure 3
Figure 3. ). In our dataset, the stars confused with Sgr A* are relatively faint (see [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (15 more)
Figure 4
Figure 4. Figure 4: Measurements of the H − K′ spectral index/color of Sgr A*-NIR plotted against the measured magnitude in K′ . The Strehl ratio cuts, confusion correction, and interpolation filters have been applied. The dashed black lines show the location of the magnitude cuts for the…
Figure 5
Figure 5. Figure 5: Flowchart representing the empirical model described in section 4, relating the measured spectral index (α est H−K′ ) to the measured magnitude in K′ band (mest K′ ). The free model parameters are highlighted in red. In green, we show the random variables that are samp…
Figure 6
Figure 6. Figure 6 [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]
Figure 7
Figure 7. Figure 7: Joint posterior distribution of ξ and η, inferred from the bright subset in black, from the extended dataset in blue (see [PITH_FULL_IMAGE:figures/full_fig_p018_7.png]
Figure 8
Figure 8. Figure 8: Joint posterior distribution of the constant spectral index η and the background parameters (mbck K′ , αbck), assuming a model with fixed slope ξ = 0, for the bright subset (in black) and the extended dataset (in blue). The magnitude of the background is completely unc…
Figure 9
Figure 9. Figure 9: Comparison between the actual H − K′ spectral index measurements from the full dataset (in black) and the predicted distribution of α est H−K′ at each measured magnitude in K′ (green). The contours are obtained similarly to [PITH_FULL_IMAGE:figures/full_fig_p020_9.png]
Figure 10
Figure 10. Figure 10: Comparison between the spectral index constraints obtained in this work, and values found in other photometric studies in the NIR. The confidence intervals were obtained by sampling model parameters from the posterior plotted in blue in [PITH_FULL_IMAGE:figures/full_…
Figure 11
Figure 11. Figure 11: Comparison between the spectral index constraints obtained in this work, and values found in spectroscopic studies in the NIR. Open (resp. filled) circles denote measurements with (resp. without) precursor/off-state substraction, a method that can bias spectral index …
Figure 12
Figure 12. Figure 12: Weighted standardized mean square error for the median estimator, as a function of Strehl ratio, for a reference star in each band. The red dashed lines indicate the chosen threshold values for the Strehl ratio cuts. α meas H−K′ = log10  λK′ λH −1 " 0.4E(H − K′ ) + …
Figure 13
Figure 13. Figure 13: Color excess Eˆ(H − K′ ) from equation (C4), obtained by setting α meas H−K′ = αint in equation (C3), as a function of the intrinsic spectral index αint of the source, for different extinction curves. We show three examples consistent with the values from Fritz et al.…
Figure 14
Figure 14. Figure 14: Example of synthetic frame construction in star-planting simulations. Here, the observed frame used as a reference is the first K′ frame of 2022 May 21. From left to right, the panels show 0.8 ′′ ×0.8 ′′ cutouts around Sgr A* of: (a) the simulated source representing …
Figure 15
Figure 15. Figure 15: Relation between the recovered and planted magnitude of Sgr A*-NIR obtained with star-planting simulations for three example frames in 2022 Aug 20: two in K′ -band (left panel), one in H-band (right panel). Sgr A* is confused with S0-38 for this epoch. The median and …
Figure 16
Figure 16. Figure 16: Same as the bottom panel of [PITH_FULL_IMAGE:figures/full_fig_p037_16.png]
Figure 17
Figure 17. Figure 17: Distribution of normalized leave-one-out errors for the MOGP interpolation (all epochs combined). F. ADDED UNCERTAINTIES FROM CONFUSION CORRECTION & INTERPOLATION In addition to the photometric uncertainties, the uncertainties on the spectral index measurements presen…
Figure 18
Figure 18. Figure 18: Comparison between the uncertainties added during the confusion correction step, and the uncertainties added during the interpolation step, as a function of K′ magnitude. Interpolation tends to adds more uncertainty for brighter points, but as Sgr A*-NIR gets fainter,…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Non-stop Variability of Sgr A* using JWST at 2.1 and 4.8 micron Wavelengths: Evidence for Distinct Populations of Faint and Bright Variable Emission

    astro-ph.GA 2025-01 conditional novelty 6.0 of 10

    JWST NIRCam infrared monitoring of Sgr A* finds fully continuous variability on seconds-to-year timescales, a faint/bright two-population flux distribution, and a 3-40 second lag of 4.8 micron behind 2.1 micron emission.

Reference graph

Works this paper leans on

97 extracted references · 18 canonical work pages · cited by 1 Pith paper

  1. [1]

    2023, ARA&A, 61, 329, doi: 10.1146/annurev-astro-052920-103508

    Aigrain, S., & Foreman-Mackey, D. 2023, ARA&A, 61, 329, doi: 10.1146/annurev-astro-052920-103508

    doi:10.1146/annurev-astro-052920-103508 2023

  2. [2]

    2020, MNRAS, 493, 1404, doi: 10.1093/mnras/staa318 Astropy Collaboration, Robitaille, T

    Anantua, R., Ressler, S., & Quataert, E. 2020, MNRAS, 493, 1404, doi: 10.1093/mnras/staa318 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f Astropy Collaboration, Pric...

    work page doi:10.1093/mnras/staa318 2020

  3. [3]

    K., Bautz, M

    Baganoff, F. K., Bautz, M. W., Brandt, W. N., et al. 2001, Nature, 413, 45, doi: 10.1038/35092510

    doi:10.1038/35092510 2001

  4. [4]

    K., Maeda, Y., Morris, M., et al

    Baganoff, F. K., Maeda, Y., Morris, M., et al. 2003, ApJ, 591, 891, doi: 10.1086/375145

    doi:10.1086/375145 2003

  5. [5]

    Balick, B., & Brown, R. L. 1974, ApJ, 194, 265, doi: 10.1086/153242

    doi:10.1086/153242 1974

  6. [6]

    2021, ApJ, 917, 8, doi: 10.3847/1538-4357/abf8ae

    Ball, D., ¨Ozel, F., Christian, P., Chan, C.-K., & Psaltis, D. 2021, ApJ, 917, 8, doi: 10.3847/1538-4357/abf8ae

    work page doi:10.3847/1538-4357/abf8ae 2021

  7. [7]

    C., Dexter, J., Asada, K., et al

    Bower, G. C., Dexter, J., Asada, K., et al. 2019, ApJL, 881, L2, doi: 10.3847/2041-8213/ab3397

    doi:10.3847/2041-8213/ab3397 2019

  8. [8]

    2022, ApJ, 931, 7, doi: 10.3847/1538-4357/ac6104

    Boyce, H., Haggard, D., Witzel, G., et al. 2022, ApJ, 931, 7, doi: 10.3847/1538-4357/ac6104

    doi:10.3847/1538-4357/ac6104 2022

Show all 97 references
  1. [9]

    2011, A&A, 532, A26, doi: 10.1051/0004-6361/201016134

    Bremer, M., Witzel, G., Eckart, A., et al. 2011, A&A, 532, A26, doi: 10.1051/0004-6361/201016134

    2011 doi

  2. [10]

    2021, The Journal of Open Source Software, 6, 3001, doi: 10.21105/joss.03001

    Buchner, J. 2021, The Journal of Open Source Software, 6, 3001, doi: 10.21105/joss.03001

    2021 doi

  3. [11]

    2018, MNRAS, 478, 5209, doi: 10.1093/mnras/sty1261

    Chael, A., Rowan, M., Narayan, R., Johnson, M., & Sironi, L. 2018, MNRAS, 478, 5209, doi: 10.1093/mnras/sty1261

    2018 doi

  4. [12]

    2021, MNRAS, 507, 5281, doi: 10.1093/mnras/stab2466

    Chatterjee, K., Markoff, S., Neilsen, J., et al. 2021, MNRAS, 507, 5281, doi: 10.1093/mnras/stab2466

    2021 doi

  5. [13]

    2019, ApJL, 882, L28, doi: 10.3847/2041-8213/ab3c68

    Chen, Z., Gallego-Cano, E., Do, T., et al. 2019, ApJL, 882, L28, doi: 10.3847/2041-8213/ab3c68

    2019 doi

  6. [14]

    Connors, R. M. T., Markoff, S., Nowak, M. A., et al. 2017, MNRAS, 466, 4121, doi: 10.1093/mnras/stw3150

    2017 doi

  7. [15]

    2011, MNRAS, 418, 1935, doi: 10.1111/j.1365-2966.2011.19612.x

    Contini, M. 2011, MNRAS, 418, 1935, doi: 10.1111/j.1365-2966.2011.19612.x

    2011

  8. [16]

    2018, A&A, 612, A34, doi: 10.1051/0004-6361/201732025

    Davelaar, J., Mo´ scibrodzka, M., Bronzwaer, T., & Falcke, H. 2018, A&A, 612, A34, doi: 10.1051/0004-6361/201732025

    2018 doi

  9. [17]

    2020, MNRAS, 497, 4999, doi: 10.1093/mnras/staa2288

    Dexter, J., Tchekhovskoy, A., Jim´ enez-Rosales, A., et al. 2020, MNRAS, 497, 4999, doi: 10.1093/mnras/staa2288

    2020 doi

  10. [18]

    2014, MNRAS, 441, 1005, doi: 10.1093/mnras/stu599

    Dibi, S., Markoff, S., Belmont, R., et al. 2014, MNRAS, 441, 1005, doi: 10.1093/mnras/stu599

    2014 doi

  11. [19]

    2000, A&AS, 147, 335, doi: 10.1051/aas:2000305

    Diolaiti, E., Bendinelli, O., Bonaccini, D., et al. 2000, A&AS, 147, 335, doi: 10.1051/aas:2000305

    2000 doi

  12. [20]

    M., Morris, M

    Do, T., Ghez, A. M., Morris, M. R., et al. 2009a, ApJ, 703, 1323, doi: 10.1088/0004-637X/703/2/1323 —. 2009b, ApJ, 691, 1021, doi: 10.1088/0004-637X/691/2/1021

    doi

  13. [21]

    R., Ghez, A

    Do, T., Lu, J. R., Ghez, A. M., et al. 2013, ApJ, 764, 154, doi: 10.1088/0004-637X/764/2/154

    2013 doi

  14. [22]

    2019a, Science, 365, 664, doi: 10.1126/science.aav8137

    Do, T., Hees, A., Ghez, A., et al. 2019a, Science, 365, 664, doi: 10.1126/science.aav8137

    doi

  15. [23]

    K., et al

    Do, T., Witzel, G., Gautam, A. K., et al. 2019b, ApJL, 882, L27, doi: 10.3847/2041-8213/ab38c3

    doi

  16. [24]

    2010, ApJ, 725, 450, doi: 10.1088/0004-637X/725/1/450

    Dodds-Eden, K., Sharma, P., Quataert, E., et al. 2010, ApJ, 725, 450, doi: 10.1088/0004-637X/725/1/450

    2010 doi

  17. [25]

    2009, ApJ, 698, 676, doi: 10.1088/0004-637X/698/1/676

    Dodds-Eden, K., Porquet, D., Trap, G., et al. 2009, ApJ, 698, 676, doi: 10.1088/0004-637X/698/1/676

    2009 doi

  18. [26]

    K., et al

    Dodds-Eden, K., Gillessen, S., Fritz, T. K., et al. 2011, The Astrophysical Journal, 728, 37, doi: 10.1088/0004-637X/728/1/37

    2011 doi

  19. [27]

    2001, ApJ, 558, 309, doi: 10.1086/322439

    Ribeiro, D. 2001, ApJ, 558, 309, doi: 10.1086/322439

    2001 doi

  20. [28]

    2006, A&A, 455, 1, doi: 10.1051/0004-6361:20064948

    Eckart, A., Sch¨ odel, R., Meyer, L., et al. 2006, A&A, 455, 1, doi: 10.1051/0004-6361:20064948

    2006 doi

  21. [29]

    K., Morris, M., et al

    Eckart, A., Baganoff, F. K., Morris, M., et al. 2004, A&A, 427, 1, doi: 10.1051/0004-6361:20040495

    2004 doi

  22. [30]

    K., Morris, M

    Eckart, A., Baganoff, F. K., Morris, M. R., et al. 2009, A&A, 500, 935, doi: 10.1051/0004-6361/200811354

    2009 doi

  23. [31]

    N., et al

    Eckart, A., Garc ´ ıa-Mar ´ ın, M., Vogel, S. N., et al. 2012, A&A, 537, A52, doi: 10.1051/0004-6361/201117779

    2012 doi

  24. [32]

    2018, arXiv e-prints, arXiv:1806.00284, doi: 10.48550/arXiv.1806.00284 41

    Eckart, A., Zajacek, M., Parsa, M., et al. 2018, arXiv e-prints, arXiv:1806.00284, doi: 10.48550/arXiv.1806.00284 41

    2018 doi arXiv

  25. [33]

    2005, ApJ, 628, 246, doi: 10.1086/430667 Event Horizon Telescope Collaboration, Akiyama, K.,

    Eisenhauer, F., Genzel, R., Alexander, T., et al. 2005, ApJ, 628, 246, doi: 10.1086/430667 Event Horizon Telescope Collaboration, Akiyama, K.,

    2005 doi

  26. [34]

    2022a, ApJL, 930, L16, doi: 10.3847/2041-8213/ac6672 —

    Alberdi, A., et al. 2022a, ApJL, 930, L16, doi: 10.3847/2041-8213/ac6672 —. 2022b, ApJL, 930, L12, doi: 10.3847/2041-8213/ac6674

    doi

  27. [35]

    2016, The Journal of Open Source Software, 1, 24, doi: 10.21105/joss.00024

    Foreman-Mackey, D. 2016, The Journal of Open Source Software, 1, 24, doi: 10.21105/joss.00024

    2016 doi

  28. [36]

    K., Gillessen, S., Dodds-Eden, K., et al

    Fritz, T. K., Gillessen, S., Dodds-Eden, K., et al. 2011, ApJ, 737, 73, doi: 10.1088/0004-637X/737/2/73

    2011 doi

  29. [37]

    K., Do, T., Ghez, A

    Gautam, A. K., Do, T., Ghez, A. M., et al. 2019, ApJ, 871, 103, doi: 10.3847/1538-4357/aaf103

    2019 doi

  30. [38]

    K., Do, T., Ghez, A

    Gautam, A. K., Do, T., Ghez, A. M., et al. 2024, ApJ, 964, 164, doi: 10.3847/1538-4357/ad26e6

    2024 doi

  31. [39]

    2010, Reviews of Modern Physics, 82, 3121, doi: 10.1103/RevModPhys.82.3121

    Genzel, R., Eisenhauer, F., & Gillessen, S. 2010, Reviews of Modern Physics, 82, 3121, doi: 10.1103/RevModPhys.82.3121

    2010 doi

  32. [40]

    2003, Nature, 425, 934, doi: 10.1038/nature02065

    Genzel, R., Sch¨ odel, R., Ott, T., et al. 2003, Nature, 425, 934, doi: 10.1038/nature02065

    2003 doi

  33. [41]

    M., Duchˆ ene, G., Matthews, K., et al

    Ghez, A. M., Duchˆ ene, G., Matthews, K., et al. 2003, ApJL, 586, L127, doi: 10.1086/374804

    2003 doi

  34. [42]

    M., Wright, S

    Ghez, A. M., Wright, S. A., Matthews, K., et al. 2004, ApJL, 601, L159, doi: 10.1086/382024

    2004 doi

  35. [43]

    M., Hornstein, S

    Ghez, A. M., Hornstein, S. D., Lu, J. R., et al. 2005, ApJ, 635, 1087, doi: 10.1086/497576

    2005 doi

  36. [44]

    M., Salim, S., Weinberg, N

    Ghez, A. M., Salim, S., Weinberg, N. N., et al. 2008, ApJ, 689, 1044, doi: 10.1086/592738

    2008 doi

  37. [45]

    2009a, ApJ, 692, 1075, doi: 10.1088/0004-637X/692/2/1075 —

    Gillessen, S., Eisenhauer, F., Trippe, S., et al. 2009a, ApJ, 692, 1075, doi: 10.1088/0004-637X/692/2/1075 —. 2009b, ApJ, 692, 1075, doi: 10.1088/0004-637X/692/2/1075

    doi

  38. [46]

    2006, ApJL, 640, L163, doi: 10.1086/503557

    Gillessen, S., Eisenhauer, F., Quataert, E., et al. 2006, ApJL, 640, L163, doi: 10.1086/503557

    2006 doi

  39. [47]

    1997, Geostatistics for Natural Resource

    Goovaerts, P. 1997, Geostatistics for Natural Resource

    1997

  40. [48]

    Evaluation, Vol. 42 GPy. since 2012, GPy: A Gaussian process framework in python, http://github.com/SheffieldML/GPy GRA VITY Collaboration, Abuter, R., Amorim, A., et al. 2019, A&A, 625, L10, doi: 10.1051/0004-6361/201935656 —. 2020, A&A, 638, A2, doi: 10.1051/0004-6361/202037...

    2012 doi

  41. [49]

    A., & Dexter, J

    Grigorian, A. A., & Dexter, J. 2024, MNRAS, 530, 1563, doi: 10.1093/mnras/stae934 Guti´ errez, E. M., Nemmen, R., & Cafardo, F. 2020, ApJL, 891, L36, doi: 10.3847/2041-8213/ab7998

    2024 doi

  42. [50]

    Ho, L. C. 2008, ARA&A, 46, 475, doi: 10.1146/annurev.astro.45.051806.110546

    2008 arXiv

  43. [51]

    D., Matthews, K., Ghez, A

    Hornstein, S. D., Matthews, K., Ghez, A. M., et al. 2007, ApJ, 667, 900, doi: 10.1086/520762

    2007 doi

  44. [52]

    R., Anderson, J., et al

    Hosek, Matthew W., J., Lu, J. R., Anderson, J., et al. 2019, ApJ, 870, 44, doi: 10.3847/1538-4357/aaef90 —. 2018, ApJ, 855, 13, doi: 10.3847/1538-4357/aaabbb

    2019 doi

  45. [53]

    E., Stenning, D

    Jones, D. E., Stenning, D. C., Ford, E. B., et al. 2022, The Annals of Applied Statistics, 16, 652 , doi: 10.1214/21-AOAS1471

    2022 doi

  46. [54]

    G., & Huijbregts, C

    Journel, A. G., & Huijbregts, C. J. 1976, Mining geostatistics, Academic Press

    1976

  47. [55]

    E., et al

    Krabbe, A., Iserlohe, C., Larkin, J. E., et al. 2006, ApJL, 642, L145, doi: 10.1086/504587

    2006 doi

  48. [56]

    2004, ApJL, 611, L101, doi: 10.1086/423985

    Liu, S., Petrosian, V., & Melia, F. 2004, ApJL, 611, L101, doi: 10.1086/423985

    2004 doi

  49. [57]

    R., Gautam, A

    Lu, J. R., Gautam, A. K., Chu, D., Terry, S. K., & Do, T. 2021, Keck-DataReductionPipelines/KAI: v1.0.0 Release of KAI, v1.0.0, Zenodo, doi: 10.5281/zenodo.6677744

    2021 doi

  50. [58]

    Macquart, J.-P., & Bower, G. C. 2006, ApJ, 641, 302, doi: 10.1086/500317

    2006 doi

  51. [59]

    Markoff, S., Falcke, H., Yuan, F., & Biermann, P. L. 2001, A&A, 379, L13, doi: 10.1051/0004-6361:20011346

    2001 doi

  52. [60]

    K., chen, s., et al

    Martinez, G., O’Neil, K. K., chen, s., et al. 2022, in American Astronomical Society Meeting Abstracts, Vol. 54, American Astronomical Society Meeting Abstracts, 302.20

    2022

  53. [61]

    C., Morris, M., Walter, F., & Baganoff, F

    Mauerhan, J. C., Morris, M., Walter, F., & Baganoff, F. K. 2005, ApJL, 623, L25, doi: 10.1086/429960

    2005 doi

  54. [62]

    2012, Science, 338, 84

    Meyer, L., Ghez, A., Sch¨ odel, R., et al. 2012, Science, 338, 84

    2012

  55. [63]

    2021, ApJL, 910, L1, doi: 10.3847/2041-8213/abeb70

    Murchikova, L. 2021, ApJL, 910, L1, doi: 10.3847/2041-8213/abeb70

    2021 doi

  56. [64]

    1995, Nature, 374, 623, doi: 10.1038/374623a0

    Narayan, R., Yi, I., & Mahadevan, R. 1995, Nature, 374, 623, doi: 10.1038/374623a0

    1995 doi

  57. [65]

    2008, ApJ, 680, 1174, doi: 10.1086/587791

    Nishiyama, S., Nagata, T., Tamura, M., et al. 2008, ApJ, 680, 1174, doi: 10.1086/587791

    2008 doi

  58. [66]

    2006, ApJ, 638, 839, doi: 10.1086/499038

    Nishiyama, S., Nagata, T., Kusakabe, N., et al. 2006, ApJ, 638, 839, doi: 10.1086/499038

    2006 doi

  59. [67]

    2019, A&A, 630, L3, doi: 10.1051/0004-6361/201936322

    Nogueras-Lara, F., Sch¨ odel, R., Najarro, F., et al. 2019, A&A, 630, L3, doi: 10.1051/0004-6361/201936322

    2019 doi

  60. [68]

    2020, A&A, 641, A141, doi: 10.1051/0004-6361/202038606

    Nogueras-Lara, F., Sch¨ odel, R., Neumayer, N., et al. 2020, A&A, 641, A141, doi: 10.1051/0004-6361/202038606

    2020 doi

  61. [69]

    T., Dong, H., et al

    Nogueras-Lara, F., Gallego-Calvente, A. T., Dong, H., et al. 2018, A&A, 610, A83, doi: 10.1051/0004-6361/201732002

    2018 doi

  62. [70]

    2006, ApJ, 643, 1011, doi: 10.1086/503273

    Paumard, T., Genzel, R., Martins, F., et al. 2006, ApJ, 643, 1011, doi: 10.1086/503273

    2006 doi

  63. [71]

    2017, MNRAS, 468, 2447, doi: 10.1093/mnras/stx596

    Ponti, G., George, E., Scaringi, S., et al. 2017, MNRAS, 468, 2447, doi: 10.1093/mnras/stx596

    2017 doi

  64. [72]

    2008, A&A, 488, 549–557, doi: 10.1051/0004-6361:200809986

    Porquet, D., Grosso, N., Predehl, P., et al. 2008, A&A, 488, 549–557, doi: 10.1051/0004-6361:200809986

    2008 doi

  65. [73]

    2021, Monthly Notices of the Royal Astronomical Society, 502, 2023 42

    Porth, O., Mizuno, Y., Younsi, Z., & Fromm, C. 2021, Monthly Notices of the Royal Astronomical Society, 502, 2023 42

    2021

  66. [74]

    2015, MNRAS, 452, 2269, doi: 10.1093/mnras/stv1428

    Roberts, S. 2015, MNRAS, 452, 2269, doi: 10.1093/mnras/stv1428

    2015 doi

  67. [75]

    E., & Williams, C

    Rasmussen, C. E., & Williams, C. K. I. 2005, Gaussian Processes for Machine Learning (The MIT Press), doi: 10.7551/mitpress/3206.001.0001

    2005 doi

  68. [76]

    M., White, C

    Ressler, S. M., White, C. J., & Quataert, E. 2023, MNRAS, 521, 4277, doi: 10.1093/mnras/stad837

    2023 doi

  69. [77]

    Ripperda, B., Bacchini, F., & Philippov, A. A. 2020, ApJ, 900, 100, doi: 10.3847/1538-4357/ababab

    2020 doi

  70. [78]

    R., Ghez, A., et al

    Sakai, S., Lu, J. R., Ghez, A., et al. 2019, ApJ, 873, 65, doi: 10.3847/1538-4357/ab0361 Sch¨ odel, R., Najarro, F., Muzic, K., & Eckart, A. 2010, A&A, 511, A18, doi: 10.1051/0004-6361/200913183 Sch¨ odel, R., Ott, T., Genzel, R., et al. 2003, ApJ, 596, 1015, doi: 10.1086/378122

    2019 doi

  71. [79]

    2017, A&A, 601, A80, doi: 10.1051/0004-6361/201628530

    Subroweit, M., Garc ´ ıa-Mar ´ ın, M., Eckart, A., et al. 2017, A&A, 601, A80, doi: 10.1051/0004-6361/201628530

    2017 doi

  72. [80]

    W., Seeger, M., & Jordan, M

    Teh, Y. W., Seeger, M., & Jordan, M. I. 2005, in International Workshop on Artificial Intelligence and

    2005

  73. [81]

    T., & Vacca, W

    Tokunaga, A. T., & Vacca, W. D. 2005a, PASP, 117, 421, doi: 10.1086/429382

    doi

  74. [82]

    T., & Vacca, W

    Tokunaga, A. T., & Vacca, W. D. 2005b, PASP, 117, 1459, doi: 10.1086/499029

    doi

  75. [83]

    2020, Applied Sciences, 10, doi: 10.3390/app10196765

    Torres-Valencia, C., Orozco, A., C´ ardenas-Pe˜ na, D., ´Alvarez Meza, A., & ´Alvarez, M. 2020, Applied Sciences, 10, doi: 10.3390/app10196765

    2020 doi

  76. [84]

    2011, A&A, 528, A140, doi: 10.1051/0004-6361/201015157

    Trap, G., Goldwurm, A., Dodds-Eden, K., et al. 2011, A&A, 528, A140, doi: 10.1051/0004-6361/201015157

    2011 doi

  77. [85]

    2007, Monthly Notices of the Royal Astronomical Society, 375, 764 van Dam, M

    Trippe, S., Paumard, T., Ott, T., et al. 2007, Monthly Notices of the Royal Astronomical Society, 375, 764 van Dam, M. A., Mignant, D. L., & Macintosh, B. A. 2004, Appl. Opt., 43, 5458, doi: 10.1364/AO.43.005458

    2007 doi

  78. [86]

    C., Do, T., Witzel, G., et al

    Weldon, G. C., Do, T., Witzel, G., et al. 2023, ApJL, 954, L33, doi: 10.3847/2041-8213/acf2f2

    2023 doi

  79. [87]

    2008, in Advances in Neural Information Processing Systems, ed

    Williams, C., Klanke, S., Vijayakumar, S., & Chai, K. 2008, in Advances in Neural Information Processing Systems, ed. D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou, Vol. 21 (Curran Associates, Inc.). https://proceedings.neurips.cc/paper files/paper/2008/ file/15d4e891d78497...

    2008

  80. [88]

    2014, in The Galactic Center: Feeding and Feedback in a Normal Galactic Nucleus, ed

    Witzel, G., Morris, M., Ghez, A., et al. 2014, in The Galactic Center: Feeding and Feedback in a Normal Galactic Nucleus, ed. L. O. Sjouwerman, C. C. Lang, & J. Ott, Vol. 303, 274–282, doi: 10.1017/S1743921314000738

    2014 doi

  81. [89]

    R., Ghez, A

    Witzel, G., Lu, J. R., Ghez, A. M., et al. 2016, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9909, Adaptive Optics Systems V, ed. E. Marchetti, L. M. Close, & J.-P. V´ eran, 99091O, doi: 10.1117/12.2233872

    2016 doi

  82. [90]

    2018, ApJ, 863, 15, doi: 10.3847/1538-4357/aace62

    Witzel, G., Martinez, G., Hora, J., et al. 2018, ApJ, 863, 15, doi: 10.3847/1538-4357/aace62

    2018 doi

  83. [91]

    P., et al

    Witzel, G., Martinez, G., Willner, S. P., et al. 2021, ApJ, 917, 73, doi: 10.3847/1538-4357/ac0891

    2021 doi

  84. [92]

    L., Le Mignant, D., Bouchez, A

    Wizinowich, P. L., Le Mignant, D., Bouchez, A. H., et al. 2006, PASP, 118, 297, doi: 10.1086/499290

    2006 doi

  85. [93]

    R., Ghez, A

    Yelda, S., Lu, J. R., Ghez, A. M., et al. 2010, ApJ, 725, 331, doi: 10.1088/0004-637X/725/1/331

    2010 doi

  86. [94]

    2014, ARA&A, 52, 529, doi: 10.1146/annurev-astro-082812-141003

    Yuan, F., & Narayan, R. 2014, ARA&A, 52, 529, doi: 10.1146/annurev-astro-082812-141003

    2014 doi

  87. [95]

    2003, ApJ, 598, 301, doi: 10.1086/378716

    Yuan, F., Quataert, E., & Narayan, R. 2003, ApJ, 598, 301, doi: 10.1086/378716

    2003 doi

  88. [96]

    2004, ApJ, 606, 894, doi: 10.1086/383117

    Yuan, F., Quataert, E., & Narayan, R. 2004, ApJ, 606, 894, doi: 10.1086/383117

    2004 doi

  89. [97]

    2012, AJ, 144, 1, doi: 10.1088/0004-6256/144/1/1 ´Alvarez, M

    Yusef-Zadeh, F., Wardle, M., Dodds-Eden, K., et al. 2012, AJ, 144, 1, doi: 10.1088/0004-6256/144/1/1 ´Alvarez, M. A., & Lawrence, N. D. 2011, The Journal of Machine Learning Research, 12, 1459 ´Alvarez, M. A., Rosasco, L., & Lawrence, N. D. 2012, Foundations and Trends in Mach...

    2012 doi

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.