Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

RTFAST-Spectra: Emulation of X-ray reverberation mapping for active galactic nuclei

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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

Pith's one-line read A neural network emulates the RTDIST X-ray model to ~1% accuracy at 200x speed

desk verdict Useful first emulator for RTDIST with genuinely honest caveats, but the 'drop-in replacement' and 'O(1%) precision' claims are stronger than the tests support; worth sending to a serious referee after revision. read the letter →

arxiv 2412.10131 v2 pith:W7QXFXXT submitted 2024-12-13 astro-ph.HE astro-ph.IM

Benjamin Ricketts , Daniela Huppenkothen , Matteo Lucchini , Adam Ingram , Guglielmo Mastroserio , Matthew Ho , Benjamin Wandelt This is my paper · ORCID
classification astro-ph.HEastro-ph.IM
keywords X-rayreverberationmappingneuralnetworkemulatoractivegalacticnucleiBayesianinferenceprincipalcomponentanalysisRTDISTblackholespin
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 targets a bottleneck: Bayesian fitting of the self-consistent X-ray reverberation model RTDIST takes weeks to months because each spectrum costs about half a second, so full posterior exploration is impractical. The authors build RTFAST-Spectra, a neural-network emulator that replaces RTDIST's spectral calculation for active galactic nuclei, compressing the 2017-bin output to 200 PCA components and training on 11 million RTDIST spectra. They report ~1% spectral precision across the 17-parameter space and a conservative 200x per-call speedup (up to ~8000x when batched), which turns month-long MCMC fits into hours. The paper also documents where the emulator is not safe: outside the six physical constraints it produces nonsense, and a soft-excess bias in a simulated fit pushed spin and density away from their true values.

What carries the argument

Principal Component Analysis reduces the 2017 energy bins of each log-scaled, standardized spectrum to 200 principal components that preserve correlations between continuum and atomic lines; a 12-layer, 256-node feed-forward network with GELU activations learns the mapping from the 17 RTDIST parameters to these components, trained with a variance-weighted mean-squared-error loss and averaged over a 7-network ensemble. The six physical constraints of Section 3.1.2 shrink the training space to about 1% of the raw parameter volume, which is what makes the accuracy achievable.

What would settle it

Fit a simulated XMM-Newton observation generated from RTDIST with parameters at the edge of the Section 3.1.2 box (coronal height 100 Rg, spin 0.998, electron density $10^{19}$.5 $cm^{-3}$) using RTFAST and the authors' MCMC settings; if the true spin and density fall outside the 3-$\sigma$ posterior, as they do in the paper's central Ark-564-like fit, then the O(1%) spectral precision does not translate into unbiased parameter inference in the regime the emulator was designed for.

Watch

Extended reading notes

Core claim

The central claim is that RTFAST-Spectra is a drop-in replacement for the spectral part of RTDIST for AGN: for parameters inside the constrained training box, it reproduces RTDIST's 0.1-20 keV spectra with fractional errors mostly within the 3% systematic calibration limit of XMM-Newton, with a typical O(1%) error and no systematic bias after averaging seven networks. The speed gain is O($10^{2}$) per sequential call and O($10^{4}$) for vectorised batches, so the authors demonstrate an MCMC posterior recovery on simulated XMM-Newton data that would have taken a month and instead runs in hours. They also show that the emulator captures the expected parameter behaviour (steepening continuum with photon index, the relativistically smeared Fe K complex) and that it fails catastrophically outside the training box, which is why users must enforce the Section 3.1.2 constraints.

Load-bearing premise

The central premise is that the six physical constraints in Section 3.1.2 define a box large enough to contain every active galactic nucleus of interest, and that every user will stay inside that box; if a real source falls outside it—say a retrograde spin or a softer-than-allowed soft excess—RTFAST will silently return nonsense while looking like a valid spectrum.

Editorial extensions

If this is right

  • A full MCMC fit to one AGN spectrum drops from weeks to hours, because RTFAST evaluates a spectrum in ~1.6 ms versus ~0.32 s for RTDIST, and batched evaluations are ~8000x faster.
  • Bayesian users can now map degeneracies such as the distance-mass-ionisation correlation and the height-spin banana with full posteriors rather than point estimates.
  • The differentiable emulator makes gradient-based samplers (HMC) and simulation-based inference practical for RTDIST, not just random-walk MCMC.
  • Every RTFAST fit must stay inside the Section 3.1.2 constraints; outside them the emulator silently returns unphysical spectra, so results outside the box should always be rechecked with RTDIST.
  • The paper recommends validating any RTFAST posterior by re-evaluating draws with the original RTDIST, a hybrid approach that keeps most of the speed gain while catching emulator bias.

Reading between the lines

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

  • Beyond the paper: if the bias is concentrated in the soft excess as the Ark 564 fit suggests, restricting RTFAST fits to 1-10 keV should remove most of the spin and density bias; this is testable with the authors' own simulated data.
  • Beyond the paper: the PCA-plus-ensemble recipe is modular, so emulating the reflection spectrum alone or the relativistic illumination profile would cut error by roughly an order of magnitude and make the emulator's accuracy comparable to XRISM resolution.
  • Beyond the paper: the six-constraint box acts as a hard prior; a survey of AGN with retrograde spins, extreme densities, or unusual fluxes would show how often real targets fall outside the box, and retraining with relaxed bounds is the straightforward remedy.
  • Beyond the paper: the differentiable emulator enables gradient-based inference and simulation-based calibration, which could make hierarchical multi-source fits for the Hubble constant computationally feasible, a goal the authors cite as motivation.
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

4 major / 5 minor

Summary. The manuscript presents RTFAST-Spectra, a neural-network emulator for the spectral component of the RTDIST X-ray reverberation model. The authors train an ensemble of seven feed-forward networks on 1.1e7 RTDIST spectra with 17 free parameters, using PCA to reduce the output to 200 components. They report an O(10^2) speed-up, claim O(1%) precision, and demonstrate MCMC posterior recovery on a simulated XMM-Newton observation. The paper also discusses parameter-space constraints, data generation, preprocessing, network architecture, and robustness tests.

Significance. If the claimed precision were established, this would be a valuable tool: it would make full Bayesian posterior inference for a state-of-the-art relativistic reflection model practical for the first time, with applications to AGN spin and distance measurements. The authors provide a large public training set, open-source code, and a reproducible benchmark pipeline. The ensemble methodology and physical constraints are sensible. However, the current evidence does not fully support the central 'drop-in replacement with O(1%) precision' claim: the residual metric is not in physical flux units, only 71% of bins meet the looser 3% criterion, and the single recovery test shows >3-sigma biases in spin and density. The paper's own Section 5.1 advises checking against RTDIST, which qualifies the drop-in promise. These issues are addressable with additional validation and a revised framing.

major comments (4)
  1. [Section 3.1.4, Eq. (1)] The residual metric chi_i is defined in the standardized log-flux space after steps (ii)-(iii) of Section 3.1.3, so a value of 0.01 does not correspond to a 1% error in physical flux. Throughout the paper (e.g., Figs. 7, 8, 10, 11 and the Abstract), these residuals are quoted as 'percentage' errors and compared to the 3% XMM-Newton effective-area systematic. This conflates two different quantities. The paper should report residuals in physical flux units (after inverting the log and standardization) for the headline precision claim, and state the actual physical-flux percentile ranges.
  2. [Section 4.3 and Fig. 10] The paper states that over 71% of individual energy bins satisfy the 3% criterion and that worst-case residuals extend to 15%, yet the Abstract claims O(1%) precision over all 17 free parameters. A 71% pass rate at 3% is not a 1% precision claim, and the 15% worst case is an order of magnitude above the stated precision. The authors should either revise the central claim to match the actual distribution or provide a stronger justification, e.g., showing that the high-residual bins are masked by typical backgrounds or detector responses in the intended use cases.
  3. [Section 4.4, Table 3 and Fig. 12] In the simulated recovery test, the true spin a=0.9 is recovered as 0.64+0.05-0.06 and the true logNe=17 as 16.41+0.08-0.07, placing the truth outside the 3-sigma posterior intervals. The authors argue this is an upper limit because the chosen parameters are in the worst 5% of emulator performance, but no systematic mapping between spectral residuals and posterior bias is provided. This single example does not establish that the bias is confined to a small, identifiable region of parameter space, and it directly undermines the claim that RTFAST is a drop-in replacement for Bayesian inference. A systematic injection-recovery study across the training box is needed, together with a diagnostic for detecting when the emulator bias is likely to dominate.
  4. [Section 5.1] The recommendation that users 'should always check the fitted model and plot posterior draws from the original RTDIST' is in tension with the Abstract's claim that RTFAST is a 'drop in replacement' for RTDIST. If final verification with the original model is required, the emulator is better described as an accelerator for exploratory fitting and chain initialization, not a drop-in replacement. The authors should clarify the intended workflow and adjust the central claim accordingly.
minor comments (5)
  1. [Figure 5 and Section 3.2.1] The text says the best network has 8 fully connected layers with 256 nodes, while the figure caption states 12 hidden layers, an input layer of 20 nodes, and an output layer of 40 nodes; the text also says the input has 17 parameters and the output 200 PCA components. Please reconcile these descriptions.
  2. [Section 3.2.1] There is a typo: 'Rectified error Linear Unit' should be 'Rectified Linear Unit'.
  3. [Section 5.1] The phrase 'full full Bayesian' contains a duplicate 'full'; please remove it.
  4. [Figure 10 caption] The caption reads 'Black indicates an error above%.' The threshold number is missing; please insert the numerical value.
  5. [Table 2] The prior for distance is labeled 'logU(3.5, 500)' and then described with units '105 kpc', which is confusing; please state the unit explicitly in the prior column or in a footnote.

Circularity Check

0 steps flagged · score 0.0 of 10

RTFAST is a surrogate fit benchmarked against its own target model; the recovery test is a closure test, not a prediction, so no fitted input is disguised as a prediction.

full rationale

The paper's derivation chain is: use RTDIST to generate 1.1e7 spectra under physically constrained priors; apply log-scaling, standardization, and PCA to 200 components; train a feed-forward neural network ensemble; compare the reconstructed output against RTDIST on a held-out 1000-spectrum test set using Eq. 1; and run an MCMC closure fit to a simulated XMM-Newton observation. Every validation step compares RTFAST output directly to RTDIST output for parameter sets not used in training. This is exactly the correct external benchmark for a surrogate model: the surrogate is supposed to reproduce the target code, and held-out test residuals measure generalization, not circularity. The Sec. 4.4 'Recovering parameters' test is a closure test: the simulated data are generated from RTDIST, and the aim is to see whether RTFAST recovers the known inputs, not to claim a new physical prediction. The paper explicitly reports that the truth falls outside the 3-sigma posterior bounds for spin and logNe in this particular case, which is evidence of imperfection rather than of circularity. The load-bearing claims (O(1%) precision, 200x speedup) are measured against RTDIST and wall-clock time, not against any prior model parameters. No self-citation is load-bearing for the core emulation claim; in particular, the paper does not invoke a uniqueness theorem to forbid alternatives. The constrained parameter box of Section 3.1.2 is a modeling choice, not a result derived from RTFAST output. Therefore no step reduces by construction to its own inputs, and the appropriate circularity score is 0.

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

The emulator is a fit to RTDIST, not a physical derivation, so the ledger records the modeling choices and domain assumptions that determine what the emulator can represent. No new physical entities are introduced. The main entries are the PCA truncation, the flux floor, the loss weighting, ensemble size, and the physical parameter box.

free parameters (5)
  • PCA component count K = 200
    Selected by iterative reconstruction-error test with 0.1% threshold; caps spectral fidelity of the emulator.
  • Flux floor = 1e-11 photons/cm2/s/keV
    Pre-processing clamps all model fluxes below this to 1e-11; defines the low-flux regime and affects residual statistics.
  • Loss weighting constant = 1
    Added in Eq. 4 so low-variance PCA components are not ignored; chosen by hand, affects training balance.
  • Ensemble size = 7
    Averaged 7 neural networks; chosen due to diminishing returns with compute; improves bias and variance.
  • NN hyperparameters (architecture, learning rate, batch size) = 8 or 12 hidden layers, 256 nodes, GELU; LR 1e-4; batch 1024
    Selected by grid sweeps; the paper's text and Fig. 5 disagree on layer count, so the exact architecture is ambiguous.
assumptions (5)
  • domain assumption RTDIST output is the ground truth for the spectra being emulated
    The entire training, validation, and test data are produced by RTDIST (Section 3.1); any inaccuracies in RTDIST propagate to RTFAST.
  • domain assumption The six physical restrictions in Section 3.1.2 define the only parameter region relevant for AGN fitting
    Training space is reduced to about 1% of the nominal range; outside it RTFAST is not valid (Section 4.5).
  • domain assumption Prograde black hole spin only
    Constraint (vi) assumes retrograde spin is unobserved and excludes it from training.
  • ad hoc to paper 200 PCA components plus 0.1% reconstruction threshold preserves all scientifically relevant spectral features
    Chosen by their own threshold, not derived; crucial because PCA errors propagate into the emulator.
  • standard math Universal approximation theorem and standard PCA decomposition
    Used to justify ANN emulation and dimension reduction in Section 3.

how reviews work

0 comments
Cite this review

Pith. "Pith review of RTFAST-Spectra: Emulation of X-ray reverberation mapping for active galactic nuclei." pith.science (2026). https://pith.science/paper/W7QXFXXT

@misc{pith2026241210131,
  author       = {Pith},
  title        = {Pith review of: RTFAST-Spectra: Emulation of X-ray reverberation mapping for active galactic nuclei},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/W7QXFXXT}},
  note         = {Machine review of arXiv:2412.10131}
}
abstract

Bayesian analysis has begun to be more widely adopted in X-ray spectroscopy, but it has largely been constrained to relatively simple physical models due to limitations in X-ray modelling software and computation time. As a result, Bayesian analysis of numerical models with high physics complexity have remained out of reach. This is a challenge, for example when modelling the X-ray emission of accreting black hole X-ray binaries, where the slow model computations severely limit explorations of parameter space and may bias the inference of astrophysical parameters. Here, we present RTFAST-Spectra: a neural network emulator that acts as a drop in replacement for the spectral portion of the black hole X-ray reverberation model RTDIST. This is the first emulator for the reltrans model suite and the first emulator for a state-of-the-art x-ray reflection model incorporating relativistic effects with 17 physically meaningful model parameters. We use Principal Component Analysis to create a light-weight neural network that is able to preserve correlations between complex atomic lines and simple continuum, enabling consistent modelling of key parameters of scientific interest. We achieve a $\mathcal{O}(10^2)$ times speed up over the original model in the most conservative conditions with $\mathcal{O}(1\%)$ precision over all 17 free parameters in the original numerical model, taking full posterior fits from months to hours. We employ Markov Chain Monte Carlo sampling to show how we can better explore the posteriors of model parameters in simulated data and discuss the complexities in interpreting the model when fitting real data.

Figures

Figures reproduced from arXiv: 2412.10131 by the authors.

Figure 1
Figure 1. Corner plot of 4 of the training set parameters and their distributions. Possible training data to be generated is constrained by the physical constraints outlined in section 3.1.2. The full distributions can be found in Fig. A1. and 73km/s/Mpc at their most extreme (Freedman 2021). We allow for a wider range of possible inferred values to allow for flexibility in inference of the implied ionization of the disk in t… view at source ↗
Figure 2
Figure 2. A heat-map of the percentage residual difference between the PCA reconstruction and RTDIST sorted by the model parameter spin (a). Yellow to orange indicates 1-10% error, green indicates 0.1-1% error and blue indicates 0.01% error and below. Black indicates an error above 10%. White indicates that both the RTFAST and RTDIST output are below the detectable limit outlined in section 3.1.3. also approaching storage lim… view at source ↗
Figure 3
Figure 3. The first 5 PCA components plotted as a function of energy. Each component is in arbitrary units and is scaled appropriately for their actual contribution in the final spectrum. 0 50 100 150 200 Number of PCA components 10−6 10−5 10−4 10−3 10−2 10−1 Total unexplained variance [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Cumulative summing of the explained variance by PCA component subtracted from 1. We report how we reached this approach and our best performing strategy in the following section. 3.2.1 Architecture The neural network in this paper is built using the Pytorch package (Pa…
Figure 5
Figure 5. Figure 5: Network architecture of RTFAST. RTFAST is composed of 12 hidden layers, each with 256 nodes, an input layer of 20 nodes and an output layer of 40 nodes. Between each layer, with the exception of the output layer, is a GELU activation function. 0 500 1000 1500 Training …
Figure 7
Figure 7. Figure 7: Comparison of residual probability density functions on the test set between using a single neural network and the ensemble. The dashed red vertical lines indicate 1% error and the dashed blue vertical line indicated 0% error. The ensemble centers the emulator outputs …
Figure 8
Figure 8. Figure 8: A direct comparison of RTFAST and RTDIST. The top panel shows the photon flux spectrum from RTFAST and RTDIST as a function of energy, with both lines on top of one another. The middle panel shows the same in keV2 /cm/s/keV units. The inset panel shows a zoom-in of the…
Figure 9
Figure 9. Figure 9: Model spectra generated by RTFAST are plotted on top of one another, coloured by increasing brightness with increasing Γ with all other parameters fixed to that of the parameters in [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]
Figure 10
Figure 10. Figure 10: A heat-map of the fractional residual difference between RTFAST and RTDIST (see eq. 1). Yellow to orange indicates 1-10% error, green indicates 0.01-3% error and below. Black indicates an error above %. White indicates that the model output is below the detectable lim…
Figure 11
Figure 11. Figure 11: Distribution of the percentage residual difference between RTFAST and RTDIST as a function of energy. The shade of region indicates the 25 − 75%, 5 − 95%, and 1 − 99% region in increasing lightness respectively. h = 4.81e + 00+6.02e−01 −4.77e−01 0.5 0.6 0.7 0.8 a a = …
Figure 13
Figure 13. Figure 13 [PITH_FULL_IMAGE:figures/full_fig_p012_13.png]
Figure 12
Figure 12. Figure 12: Posteriors of fitting for a more realistic scenario where 2 sigma contours are plotted. The plot limits indicate the 3 sigma boundary. We show 5 of the 8 parameters varied. The full posteriors can be found in appendix B. The blue lines show the true parameters used to…
Figure 14
Figure 14. Figure 14: The two dimensional log-likelihoods of fitting the spectrum, vary￾ing mass and spin, plotted in contours through parameter space. Brighter contours indicate higher likelihood. The red lines indicate the true parame￾ters and what should be the peak of likelihood is rep…
Figure 15
Figure 15. Figure 15: The log likelihood as a function of spin when all other parameters are kept fixed. The true spin is plotted as a vertical dashed line at 0.9 as the most likely outcome. This implies a bias in the emulator such that it produces a spectrum that would be identified at lo…

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. Emulation of non-linear 1D spectral models: relativistic X-ray reflection

    astro-ph.IM 2026-07 accept novelty 6.0 of 10

    A modular operator-learning emulator (RTFAST2) reproduces the relativistically convolved reflection spectrum of reltrans to O(0.1)% precision with 4–10× speed-up and unbiased posterior recovery on simulated spectra.

Reference graph

Works this paper leans on

80 extracted references · 66 canonical work pages · cited by 1 Pith paper

  1. [1]

    Alsing J., et al., 2020, The Astrophysical Journal Supplement Series, 249, 5

    work page 2020

  2. [2]

    101, 1996, George H

    Arnaud K., 1996, in Astronomical Data Analysis Software and Systems V, ASP Conference Series, Vol. 101, 1996, George H. Jacoby and Jeannette Barnes, eds., p. 17.. p. 17

    work page 1996

  3. [3]

    R., Patil R

    Baker M. R., Patil R. B., 1998, Reliable Computing, 4, 235

    work page 1998

  4. [4]

    S., O’hagan A., 2009, Technometrics, 51, 425

    Bastos L. S., O’hagan A., 2009, Technometrics, 51, 425

    work page 2009

  5. [5]

    Bellman R., 1966, science, 153, 34

    work page 1966

  6. [6]

    K., Peiris H

    Bird S., Rogers K. K., Peiris H. V., Verde L., Font-Ribera A., Pontzen A., 2019, Journal of Cosmology and Astroparticle Physics, 2019, 050

    work page 2019

  7. [7]

    232, Aug

    Blandford R., K \"o nigl A., 1979, Astrophysical Journal, Part 1, vol. 232, Aug. 15, 1979, p. 34-48., 232, 34

    work page 1979

  8. [8]

    Buchner J., 2023, Statistic Surveys, 17, 169

    work page 2023

Show all 80 references
  1. [9]

    Buchner J., et al., 2014, Astronomy & Astrophysics, 564, A125

    2014

  2. [10]

    G., Dow E., Wang Q., 2014, SIAM Journal on Scientific Computing, 36, A1500

    Constantine P. G., Dow E., Wang Q., 2014, SIAM Journal on Scientific Computing, 36, A1500

    2014

  3. [11]

    Cranmer K., Brehmer J., Louppe G., 2020, Proceedings of the National Academy of Sciences, 117, 30055

    2020

  4. [12]

    T., Bardeen J

    Cunningham C. T., Bardeen J. M., 1973, Astrophysical Journal, Vol. 183, pp. 237-264 (1973), 183, 237

    1973

  5. [13]

    W., Falanga M., Fukumura K., Reynolds C

    Dauser T., Garcia J., Wilms J., B \"o ck M., Brenneman L. W., Falanga M., Fukumura K., Reynolds C. S., 2013, Monthly Notices of the Royal Astronomical Society, 430, 1694

    2013

  6. [14]

    Dauser T., Garc \' a J., Wilms J., 2016, Astronomische Nachrichten, 337, 362

    2016

  7. [15]

    D., Vaughan S., Lefkir M., Wynn G., 2024, Monthly Notices of the Royal Astronomical Society, 529, 1752

    Dias S. D., Vaughan S., Lefkir M., Wynn G., 2024, Monthly Notices of the Royal Astronomical Society, 529, 1752

    2024

  8. [16]

    A., Steiner J

    Dong Y., Garc \' a J. A., Steiner J. F., Gou L., 2020, Monthly Notices of the Royal Astronomical Society, 493, 4409

    2020

  9. [17]

    Dov c iak M., Karas V., Yaqoob T., 2004, @doi [ ] 10.1086/421115 , https://ui.adsabs.harvard.edu/abs/2004ApJS..153..205D 153, 205

    2004 doi

  10. [18]

    A., Miller J

    Draghis P. A., Miller J. M., Costantini E., Gallo L. C., Reynolds M., Tomsick J. A., Zoghbi A., 2024, @doi [ ] 10.3847/1538-4357/ad43ea , https://ui.adsabs.harvard.edu/abs/2024ApJ...969...40D 969, 40

    2024 doi

  11. [19]

    J., Roweth D., 1987, @doi [Physics Letters B] https://doi.org/10.1016/0370-2693(87)91197-X , 195, 216

    Duane S., Kennedy A., Pendleton B. J., Roweth D., 1987, @doi [Physics Letters B] https://doi.org/10.1016/0370-2693(87)91197-X , 195, 216

    1987 doi

  12. [20]

    Ducoffe M., Precioso F., 2015, arXiv preprint arXiv:1511.06412

    2015 arXiv

  13. [21]

    Fabian A., 1989, Monthly Notices of the Royal Astronomical Society, 238, 41P

    1989

  14. [22]

    C., Rees M

    Fabian A. C., Rees M. J., Stella L., White N. E., 1989, @doi [ ] 10.1093/mnras/238.3.729 , http://adsabs.harvard.edu/abs/1989MNRAS.238..729F 238, 729

    1989 doi

  15. [23]

    W., Lang D., Goodman J., 2013, Publications of the Astronomical Society of the Pacific, 125, 306

    Foreman-Mackey D., Hogg D. W., Lang D., Goodman J., 2013, Publications of the Astronomical Society of the Pacific, 125, 306

    2013

  16. [24]

    L., 2021, The Astrophysical Journal, 919, 16

    Freedman W. L., 2021, The Astrophysical Journal, 919, 16

    2021

  17. [25]

    pp 76--87

    Freeman P., Doe S., Siemiginowska A., 2001, in Astronomical Data Analysis. pp 76--87

    2001

  18. [26]

    A., Hu M., Malik A

    Ganaie M. A., Hu M., Malik A. K., Tanveer M., Suganthan P. N., 2022, Engineering Applications of Artificial Intelligence, 115, 105151

    2022

  19. [27]

    Garc \' a J., Dauser T., Reynolds C., Kallman T., McClintock J., Wilms J., Eikmann W., 2013, The Astrophysical Journal, 768, 146

    2013

  20. [28]

    A., et al., 2018, The Astrophysical Journal, 864, 25

    Garc \' a J. A., et al., 2018, The Astrophysical Journal, 864, 25

    2018

  21. [29]

    C., Arzoumanian Z., Okajima T., 2012, in Space Telescopes and Instrumentation 2012: Ultraviolet to Gamma Ray

    Gendreau K. C., Arzoumanian Z., Okajima T., 2012, in Space Telescopes and Instrumentation 2012: Ultraviolet to Gamma Ray. pp 322--329

    2012

  22. [30]

    Girolami M., Calderhead B., 2011, Journal of the Royal Statistical Society Series B: Statistical Methodology, 73, 123

    2011

  23. [31]

    XMM-SOCCAL-TN-0018, Calibration status document, ESA-ESAC, Villafranca del …

    Guainazzi M., Kirsch M., Haberl F., de Juan Ovelar M., 2013, Technical report, XMM-Newton Calibration Technical Note. XMM-SOCCAL-TN-0018, Calibration status document, ESA-ESAC, Villafranca del …

    2013

  24. [32]

    J., Lawrence E., Wagner C., 2009, The Astrophysical Journal, 705, 156

    Heitmann K., Higdon D., White M., Habib S., Williams B. J., Lawrence E., Wagner C., 2009, The Astrophysical Journal, 705, 156

    2009

  25. [33]

    Heitmann K., Lawrence E., Kwan J., Habib S., Higdon D., 2013, The Astrophysical Journal, 780, 111

    2013

  26. [34]

    Hendrycks D., Gimpel K., 2016, arXiv preprint arXiv:1606.08415

    2016 arXiv

  27. [35]

    D., et al., 2022, The Planetary Science Journal, 3, 91

    Himes M. D., et al., 2022, The Planetary Science Journal, 3, 91

    2022

  28. [36]

    A., 2019, Monthly Notices of the Royal Astronomical Society, 488, 324

    Ingram A., Mastroserio G., Dauser T., Hovenkamp P., van der Klis M., Garc \' a J. A., 2019, Monthly Notices of the Royal Astronomical Society, 488, 324

    2019

  29. [37]

    Ingram A., et al., 2022, Monthly Notices of the Royal Astronomical Society, 509, 619

    2022

  30. [38]

    Jansen F., et al., 2001, Astronomy & Astrophysics, 365, L1

    2001

  31. [39]

    Jiang J., et al., 2019, Monthly Notices of the Royal Astronomical Society, 489, 3436

    2019

  32. [40]

    S., Barret D., Peille P., Willingale R., Dauser T., Wilms J., Guainazzi M., Miller J

    Kammoun E. S., Barret D., Peille P., Willingale R., Dauser T., Wilms J., Guainazzi M., Miller J. M., 2022, @doi [ ] 10.1051/0004-6361/202243606 , https://ui.adsabs.harvard.edu/abs/2022A&A...664A..29K 664, A29

    2022 doi

  33. [41]

    Kara E., Fabian A., Cackett E., Uttley P., Wilkins D., Zoghbi A., 2013, Monthly Notices of the Royal Astronomical Society, 434, 1129

    2013

  34. [42]

    Kara E., Alston W., Fabian A., Cackett E., Uttley P., Reynolds C., Zoghbi A., 2016, Monthly Notices of the Royal Astronomical Society, 462, 511

    2016

  35. [43]

    Kaushal N., Villaescusa-Navarro F., Giusarma E., Li Y., Hawry C., Reyes M., 2022, The Astrophysical Journal, 930, 115

    2022

  36. [44]

    E., Vogl C., Buchner J., Contardo G., Williamson M., Van der Smagt P., 2021, The Astrophysical Journal Letters, 910, L23

    Kerzendorf W. E., Vogl C., Buchner J., Contardo G., Williamson M., Van der Smagt P., 2021, The Astrophysical Journal Letters, 910, L23

    2021

  37. [45]

    P., 2014, arXiv preprint arXiv:1412.6980

    Kingma D. P., 2014, arXiv preprint arXiv:1412.6980

    2014 arXiv

  38. [46]

    Kotov O., Churazov E., Gilfanov M., 2001, Monthly Notices of the Royal Astronomical Society, 327, 799

    2001

  39. [47]

    Kwan J., Heitmann K., Habib S., Padmanabhan N., Lawrence E., Finkel H., Frontiere N., Pope A., 2015, The Astrophysical Journal, 810, 35

    2015

  40. [48]

    Lakshminarayanan B., Pritzel A., Blundell C., 2017, Advances in neural information processing systems, 30

    2017

  41. [49]

    Levinson A., Blandford R., 1996, The Astrophysical Journal, 456, L29

    1996

  42. [50]

    Lewin C., et al., 2022, The Astrophysical Journal, 939, 109

    2022

  43. [51]

    Loshchilov I., 2017, arXiv preprint arXiv:1711.05101

    2017 arXiv

  44. [52]

    Loshchilov I., Hutter F., 2016, arXiv preprint arXiv:1608.03983

    2016 arXiv

  45. [53]

    Ludlam R., et al., 2020, The Astrophysical Journal, 895, 45

    2020

  46. [54]

    Masterson M., et al., 2022, The Astrophysical Journal, 934, 35

    2022

  47. [55]

    Matzeu G., et al., 2022, Monthly Notices of the Royal Astronomical Society, 515, 6172

    2022

  48. [56]

    E., et al., 2011, Classical and Quantum Gravity, 28, 114009

    McClintock J. E., et al., 2011, Classical and Quantum Gravity, 28, 114009

    2011

  49. [57]

    D., Beckman R

    McKay M. D., Beckman R. J., Conover W. J., 2000, Technometrics, 42, 55

    2000

  50. [58]

    M., Sousa J

    Mendes-Moreira J., Soares C., Jorge A. M., Sousa J. F. D., 2012, Acm computing surveys (csur), 45, 1

    2012

  51. [59]

    Nathan E., et al., 2024, Monthly Notices of the Royal Astronomical Society, 533, 2441

    2024

  52. [60]

    S., Georganopoulos M., Guiriec S., Meyer E

    Nemmen R. S., Georganopoulos M., Guiriec S., Meyer E. T., Gehrels N., Sambruna R. M., 2012, Science, 338, 1445

    2012

  53. [61]

    \"O zel F., Freire P., 2016, Annual Review of Astronomy and Astrophysics, 54, 401

    2016

  54. [62]

    Pastore A., Carnini M., 2021, Journal of Physics G: Nuclear and Particle Physics, 48, 084001

    2021

  55. [63]

    Paszke A., et al., 2019, Curran Associates, Inc, pp 8024--8035

    2019

  56. [64]

    A., McClintock J

    Remillard R. A., McClintock J. E., 2006, Annu. Rev. Astron. Astrophys., 44, 49

    2006

  57. [65]

    P., Verrelst J., G \'o mez-Dans J., Mu \ n oz-Mar \' J., Moreno J., Camps-Valls G., 2015, Remote Sensing, 7, 9347

    Rivera J. P., Verrelst J., G \'o mez-Dans J., Mu \ n oz-Mar \' J., Moreno J., Camps-Valls G., 2015, Remote Sensing, 7, 9347

    2015

  58. [66]

    E., Boettcher M., Markoff S., Tavecchio F., 2017, Space Science Reviews, 207, 5

    Romero G. E., Boettcher M., Markoff S., Tavecchio F., 2017, Space Science Reviews, 207, 5

    2017

  59. [67]

    Ross R., Fabian A., 2005, Monthly Notices of the Royal Astronomical Society, 358, 211

    2005

  60. [68]

    I., Sunyaev R

    Shakura N. I., Sunyaev R. A., 1973, Astronomy and Astrophysics, Vol. 24, p. 337-355, 24, 337

    1973

  61. [69]

    Sobolewska M., Papadakis I., 2009, Monthly Notices of the Royal Astronomical Society, 399, 1597

    2009

  62. [70]

    P., 2022, Monthly Notices of the Royal Astronomical Society, 511, 1771

    Spurio Mancini A., Piras D., Alsing J., Joachimi B., Hobson M. P., 2022, Monthly Notices of the Royal Astronomical Society, 511, 1771

    2022

  63. [71]

    Sunyaev R., Truemper J., 1979, Nature, 279, 506

    1979

  64. [72]

    pp 1139--1147

    Sutskever I., Martens J., Dahl G., Hinton G., 2013, in International conference on machine learning. pp 1139--1147

    2013

  65. [73]

    S., Price R

    Thorne K. S., Price R. H., 1975, Astrophysical Journal, vol. 195, Feb. 1, 1975, pt. 2, p. L101-L105., 195, L101

    1975

  66. [74]

    Uttley P., Casella P., 2014, Space Science Reviews, 183, 453

    2014

  67. [75]

    P., Sabater N., Mu \ n oz-Mar \' J., Camps-Valls G., Moreno J., 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 4918

    Vicent J., Verrelst J., Rivera-Caicedo J. P., Sabater N., Mu \ n oz-Mar \' J., Camps-Valls G., Moreno J., 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 4918

    2018

  68. [76]

    Virtanen P., et al., 2020, @doi [Nature Methods] 10.1038/s41592-019-0686-2 , https://rdcu.be/b08Wh 17, 261

    2020 doi

  69. [77]

    C., et al., 2016, Results in Physics, 6, 1179

    Weisskopf M. C., et al., 2016, Results in Physics, 6, 1179

    2016

  70. [78]

    Wilms J., Allen A., McCray R., 2000, The Astrophysical Journal, 542, 914

    2000

  71. [79]

    Xu Y., et al., 2022, Monthly Notices of the Royal Astronomical Society, 513, 1910

    2022

  72. [80]

    A., Johnson W

    Zdziarski A. A., Johnson W. N., Magdziarz P., 1996, Monthly Notices of the Royal Astronomical Society, 283, 193

    1996

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.