Pith. sign in

REVIEW 4 major objections 4 minor 1 cited by

Stacked Hybrid RNN-CNN Reconstruction of X-ray Influence on 21-cm Brightness Temperature

T0 review · 4 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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

Pith's one-line read A stacked hybrid neural network aims to reconstruct the global 21-cm brightness temperature from X-ray heating during the Epoch of Reionization with 99.93% accuracy and roughly a millionfold speedup over the 21SSD simulation.

desk verdict New application but the headline accuracy claim is invalidated by a target-leaking residual feature that the paper never explains how to compute at inference time. read the letter →

arxiv 2508.05842 v1 pith:JC2VUYPL submitted 2025-08-07 astro-ph.IM astro-ph.CO

S. Mobina Hosseini , Bahareh Soleimanpour Salmasi This is my paper
classification astro-ph.IMastro-ph.CO
keywords IntergalacticmediumX-rayheatingEpochofReionization21-cmbrightnesstemperatureStackedgeneralizationHybridlearningLSTM-GRU-CNNemulator
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

The paper tries to establish that a stacked hybrid machine-learning model can stand in for expensive radiative-transfer simulations of X-ray heating during cosmic reionization. It trains a base DNN plus random-forest ensemble to predict the global 21-cm brightness temperature from redshift and astrophysical parameters, then feeds the residuals to an LSTM-GRU-CNN meta-model. On the 21SSD dataset, the paper reports R² = 99.93%, errors below 0.35 mK, and a runtime reduction of about six orders of magnitude. A sympathetic reader would care because the bottleneck in 21-cm cosmology is exactly the cost of exploring high-dimensional X-ray efficiency, hardness-ratio, and Lyman-emissivity parameter space.

What carries the argument

Stacked generalization with a residual-correction feature is the mechanism. A DNN and a random forest generate base forecasts; the residual feature DNN_res — the gap between those forecasts and the target values — becomes an input to a hybrid meta-model. The meta-model couples LSTM layers (temporal sequence), one-dimensional convolutional layers with max pooling (local spatial structure), and GRU layers with attention (simplified temporal dynamics), regularized with dropout and L1/L2 penalties and trained with ADAM. The residual feature carries the argument: it tells the meta-model where the base ensemble errs, and the reported accuracy numbers depend on the meta-model learning those error p

What would settle it

Run the trained meta-model on the held-out test set with the DNN_res column removed, using only z, f_X, r_H/S, and f_alpha as inputs, and compare R² to the 21SSD outputs. If accuracy falls far below 99.93%, the headline metric depended on a feature that encodes the answer. A stronger version recomputes DNN_res from out-of-fold training predictions only and applies it to test inputs with no access to test targets.

Watch

Extended reading notes

Core claim

The paper's central claim is that a two-stage stacked hybrid neural architecture can reconstruct the global 21-cm brightness temperature from the 21SSD simulation over a grid of X-ray efficiency f_X, hard-to-soft X-ray ratio r_H/S, and Lyman-band emissivity f_alpha. Stage one is a base ensemble (DNN plus random forest) that produces preliminary predictions; the residual between those predictions and the true values is engineered into a feature, DNN_res, which is fed along with the raw parameters to a meta-model made of LSTM, convolutional, and GRU layers. The reported outcome is R² = 99.93% (99.91% on the test set), errors below 0.35 mK, and a reduction from about 3×$10^{6}$ CPU hours to roughly

Load-bearing premise

The load-bearing premise is that the residual feature DNN_res, defined in Section 4.2 as the difference between base-model forecasts and target values, can be computed at inference time even though the target brightness temperature is unavailable then; the paper does not say how.

Editorial extensions

If this is right

  • A full 21SSD-scale run takes the emulator about 15 minutes instead of roughly 3×10^6 CPU hours, making dense MCMC sampling over X-ray parameters practical on this dataset.
  • The emulator's inverse f_X–reionization-timing trend could be used to convert a measured global 21-cm spectrum into a constraint on X-ray efficiency.
  • Fast prediction across f_X, r_H/S, and f_alpha makes SKA-era sensitivity forecasts and parameter-inference pipelines inexpensive to run.
  • If the stacked-residual design transfers, the same architecture could emulate other costly summary observables such as 21-cm power spectra or full brightness-temperature PDFs.

Reading between the lines

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

  • Editorial inference: the 99.93% test accuracy is only interpretable as emulator skill if DNN_res can be computed at inference without knowing the target; Section 4.2 defines it as a function of target values and gives no test-time recipe, so the reported score may partly reflect information leakage.
  • Editorial inference: an out-of-fold stacking test — computing DNN_res from training-only residual predictions, then freezing it before scoring test data — would separate genuine emulation from target-derived information.
  • Editorial inference: if a residual-free evaluation still holds up, a natural next test is to reconstruct the full redshift-dependent T_b PDFs rather than the single per-redshift maximum PDF value used in this paper.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. This paper proposes a stacked hybrid emulator to reconstruct the sky-averaged 21-cm brightness temperature T_b(z) from astrophysical parameters (f_X, r_H/S, f_alpha) using the 21SSD simulation database. The architecture combines DNN and random forest base learners with an LSTM-GRU-CNN meta-model; a hand-crafted feature DNN_res, defined as the residual between base-model forecasts and the target T_b, is added to the meta-model inputs. The authors report R^2=99.93%, MAE~0.27 mK, and a ~10^6 speedup over 21SSD, and present reconstructed T_b curves plus a simulated expanded f_X grid. The core claim is not established because DNN_res can only be computed from the target at test time, making the performance figures a product of target leakage rather than generalization. The extension to f_X values outside the simulated set rests on mock data and residuals, and the data reduction to PDF maxima introduces unquantified selection effects.

Significance. A reliable, fast emulator of the global 21-cm signal would be genuinely useful for EoR parameter inference and for interpreting upcoming SKA-era observations. The paper has positive features: it uses a public simulation database (21SSD), compares against several plausible baselines (DNN, RNN, SVR, RF) with the same split, and applies regularization (dropout, L1/L2, early stopping, OOB). The qualitative discussion of physical trends in f_X, r_H/S and f_alpha is consistent with standard reionization physics. However, the central quantitative contribution - 99.93% accuracy with sub-0.35 mK errors - is invalidated by the target-leaking DNN_res feature, and the expanded-grid predictions in Fig. 7 are not validated against independent simulations. As presented, the paper provides neither a working inference-time procedure nor reproducible code or data artifacts that would let a reader separate genuine emulation performance from leakage.

major comments (4)
  1. [§4.2 and §4.4; Table 1] DNN_res is defined in §4.2 as 'the residuals between the forecasts of base models and the target values' and is fed, together with raw astrophysical parameters, into the LSTM-GRU-CNN meta-model (§4.4). At inference time the target T_b is unknown, so a residual computed against the true target cannot be obtained. The paper never specifies a test-time formula or a surrogate for this residual. Fig. 1 shows that DNN_res has the strongest feature-target correlation (-0.57), which is the expected signature of target leakage rather than a legitimate predictive feature. Because DNN_res enters the meta-model, the reported R^2=99.93%, MSE_test=0.324 and MAE_test=0.272 (Table 1) do not measure generalization to unseen parameter combinations; they can be achieved by reading the target off the leaked feature. The abstract's accuracy and 0.35 mK claims are therefore unsupported.
  2. [§4.1 and §5] The expanded f_X grid (0.1-10 with finer steps) is not supported by actual 21SSD simulation outputs. §4.1 states that 'a core component of the algorithm generates mock data based on these simulations,' and §5 says the expanded coverage is obtained from 'simulation data and residuals between simulations and early-stage reconstructed data.' No independent simulated T_b(z) for intermediate f_X values is used for validation. Consequently the smooth f_X trend in Fig. 7 is a prediction of the model on internally generated mock data and is circular; it cannot corroborate the emulator's accuracy outside the five original f_X values.
  3. [§4.1] The target itself is a heavily reduced summary: one T_b value per redshift, chosen as the maximum of the PDF, reducing the dataset from 5.4 million to 18,000 points. The step-like structure visible in Figs. 4-6 is acknowledged in §5 as an inherent feature of this PDF-maximum selection, but no analysis quantifies how this selection biases the emulator or the quoted errors. The filtering sentence is also self-contradictory: 'we only include maximum PDFs corresponding to T_b values below -190 mK' is inconsistent with the plotted T_b range (roughly -120 to +20 mK). If the intended filter is 'above -190 mK,' this is an arbitrary threshold that further shapes the training distribution. The reported performance therefore applies to a specially selected subset, not to the global 21-cm brightness temperature as claimed in the abstract.
  4. [§5 versus Table 1] The text reporting the headline numbers is internally inconsistent. §5 gives MAE_test=0.345, while Table 1 lists MAE_test=0.272 for the proposed model. §5 says the MSE reduction relative to RF is '1.955->0.311,' but Table 1 shows 1.955->0.317. The abstract/§6 says 'errors below 0.35 mK' while the table's MSE (0.324 mK^2, implying RMSE~0.57 mK) suggests a different error definition. These discrepancies make it impossible to know which number is authoritative and further undermine the reproducibility of the central accuracy claim.
minor comments (4)
  1. [Figure captions and Eq. (28)] Fig. 5's caption says 'The emulator's fluctuations at f_X=1 are inferior to those depicted in Fig. 2,' but the intended cross-reference is almost certainly Fig. 4. In Eq. (28), the update-gate expression has a stray bracket: 'z_t = σ(W_z·h_{t-1}, x_t] + b_z)' should be 'z_t = σ(W_z·[h_{t-1}, x_t] + b_z)'.
  2. [Table 1] Units for MSE and MAE are not specified. With MSE=0.324 and MAE=0.272, the claim 'errors below 0.35 mK' is ambiguous: if the quoted error is MAE, the text should say so; if it is RMSE, the value is inconsistent with the table.
  3. [§2.1] In the definition of T* = hc/(k_B λ_21), the text says 'h is the dimensionless Hubble constant,' but in this expression h is Planck's constant. This is a physics typo that should be corrected.
  4. [§2.2] The citation 'Lomba & Høye 2014' (Molecular Physics) appears next to a statement about the Ly-alpha background and cosmological volumes; this reference seems unrelated and should be replaced or removed.

Circularity Check

2 steps flagged · score 9.0 of 10

Target-defined DNN_res feature leaks T_b into the meta-model input, making the 99.93% accuracy and expanded-grid predictions circular by construction.

  1. self definitional [§4.2 Feature Engineering / Fig. 1; §4.4 Architecture]
    ""We included a new feature obtained from the residuals between the forecasts of base models and the target values to improve the predictive capability of our model. The basis of this new feature is created by a DNN as the main base model, and applied RFs as auxiliary base models." ... "The base models generate preliminary predictions and residuals, which are subsequently utilized to enhance the input space of the meta-model.""

    DNN_res is defined as the residual between base-model forecasts and the target brightness temperature, i.e. DNN_res = T_b − T_b_hat. The meta-model then receives raw astrophysical parameters plus DNN_res and is asked to predict T_b. For any base forecast T_b_hat, the meta-model can recover T_b almost exactly by learning T_b = T_b_hat + DNN_res. At inference on new parameters, T_b is unknown, so DNN_res cannot be formed; the paper specifies no test-time procedure for producing it. The reported R2=99.93% and errors <0.35 mK are therefore obtained from a feature that encodes the answer, not from a genuine prediction based on parameters. Fig. 1's strong DNN_res–T_b correlation (−0.57) confirms the leakage.

  2. fitted input called prediction [§4.1 Dataset and Preprocessing; §5 Result, Fig. 7]
    ""A core component of the algorithm generates mock data based on these simulations, ensuring that subsequent processing stages have access to these mock data to calculate residuals for final predictions of the global 21-cm brightness temperature." ... "Using hybrid stacked learning applied to simulation data and residuals between simulations and early-stage reconstructed data, we achieve two optimized sampling windows..." ... "These are all of the models that are predicted using approaches that are outlined in §4.""

    The expanded fX grid between 0.1 and 10 is not populated by independent 21SSD simulations; it is generated from the simulation data plus residuals between simulations and early-stage reconstructed data. These mock values are then used to compute residuals and produce the 'predictions' shown in Fig. 7. Because the input residuals already contain the reconstruction output, the extended-grid curves are self-confirming rather than validated emulations. The paper presents these as predictions of unseen parameter combinations, but the procedure feeds the model's own output back into its input, making the generalization claim circular.

full rationale

The central emulation claim rests on DNN_res, defined in §4.2 as 'the residuals between the forecasts of base models and the target values.' Since the target T_b is exactly the quantity the meta-model must predict, feeding DNN_res into the LSTM-GRU-CNN meta-model (§4.4) makes the output nearly equal to the target by construction: T_b ≈ T_b_hat + (T_b − T_b_hat). The paper never specifies how this residual is obtained for new parameters at inference; if it is computed from simulations, the emulator is not independent, and if it is not available, the reported test metrics (R2=99.93%, MSEtest=0.324, <0.35 mK) cannot be reproduced. The correlation heatmap (Fig. 1) itself documents DNN_res's strongest feature-target correlation, consistent with this leakage. The expanded fX grid in §4.1/§5 is similarly generated from 'mock data based on these simulations' and 'residuals between simulations and early-stage reconstructed data,' so Fig. 7's curves are fed back from the same reconstruction rather than independently validated emulation. Apart from a non-load-bearing self-citation in the introduction, no other circularity was found. Because the headline accuracy metric is forced by the target-derived input feature, this is a direct self-definitional reduction.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

All are listed above. The central claim rests on the 21SSD dataset, the PDF-max reduction, and the leaked residual feature; the latter is the most serious.

free parameters (3)
  • Tb selection threshold = -190 mK (stated, direction ambiguous)
    Data points are selected based on a hand-chosen cutoff to 'correct an artifact' from the 21SSD post-processing; this affects the training set and the shape of the reconstructed signal.
  • fX interpolation grid = steps of 0.1 for fX in [0.1,1] and 1.0 in [1,10]
    Expanded fX values are chosen by hand and populated with mock data generated from the model, not from simulation.
  • Network hyperparameters = e.g., 512 LSTM units, 288 CNN units, 512 GRU units, dropout 0.15, L2 1e-8, L1 4.67e-6
    Tuned via Bayesian optimization on the training data; their specific values are fit to the dataset and affect the reported performance.
assumptions (4)
  • domain assumption 21SSD simulation output is a valid ground truth for the global 21-cm brightness temperature.
    Section 4.1 states the dataset is the 21SSD simulation database; the entire supervised learning setup treats simulation output as the target.
  • domain assumption The maximum-PDF value per redshift adequately represents the global 21-cm signal.
    Section 4.1 reduces the dataset from 5.4M to 18k points by keeping only the Tb at the PDF maximum for each redshift; no validation that this summary preserves the physical information.
  • ad hoc to paper Residual features are computable at inference time without access to the target.
    Section 4.2 introduces DNN_res as the residual between base-model forecasts and target values; the paper does not specify how this feature is formed for test data, making this an unsupported premise.
  • ad hoc to paper Mock data generated from the model can substitute for missing simulation data at intermediate fX values.
    Section 4.1 and Section 5 describe expanding the fX grid using mock data generated from residuals; this assumes the model's own output is a valid training signal for unexplored parameter values.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Stacked Hybrid RNN-CNN Reconstruction of X-ray Influence on 21-cm Brightness Temperature." pith.science (2026). https://pith.science/paper/JC2VUYPL

@misc{pith2026250805842,
  author       = {Pith},
  title        = {Pith review of: Stacked Hybrid RNN-CNN Reconstruction of X-ray Influence on 21-cm Brightness Temperature},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JC2VUYPL}},
  note         = {Machine review of arXiv:2508.05842}
}
read the original abstract

The X-ray photons substantially affect the thermal and ionization states of the intergalactic medium (IGM) during the Epoch of Reionization (EoR), thereby significantly influencing the 21-cm line observables such as its sky-averaged (global) brightness temperature. Nevertheless, the complicated dependency of astrophysical processes on a broad spectrum of parameters, including X-ray efficiency, spectral characteristics, and gas dynamics, makes precisely simulating the effect of X-ray flux challenging. Traditional approaches, including N-body and hydrodynamical simulations, are computationally intensive and struggle to explore high-dimensional parameter spaces efficiently. We present a stacked hybrid model trained on a specific simulation intended to reconstruct the effect of X-ray flux on the global 21-cm brightness temperature during the EoR. Along with Convolutional Neural Networks (CNNs), this architecture combines two substantial forms of recurrent neural networks (RNNs), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), therefore enabling fast adaptation to several X-ray flux levels. Without demanding repeated simulations, this emulator preserves temporal and spatial dependencies and generalizes to unseen parameter combinations. This matter reduces computation time by a factor of one million while preserving excellent prediction accuracy of 99.93\%, facilitating studies on high-dimensional parameter inference and sensitivity with an error margin of less than 0.35 mK. Our LSTM-GRU-CNN emulator combines recurrent and convolutional architectures to enable a robust and scalable analysis of X-ray heating effects on the global 21-cm brightness temperature during the EoR.

Figures

Figures reproduced from arXiv: 2508.05842 by the authors.

Figure 1
Figure 1. This heatmap shows feature-feature and feature-target [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. Proposed stacked hybrid architecture for reconstructing the global 21-cm brightness temperature from astrophysical param [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. All methods mentioned in [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figures from the paper (5 more)
Figure 3
Figure 3. Figure 3: Learning curve showing the training and validation losses over 82 epochs. Early stopping is employed to terminate the [PITH_FULL_IMAGE:figures/full_fig_p012_3.png]
Figure 4
Figure 4. Figure 4: The figure compares the global 21-cm brightness temperature from our emulator with the 21SSD simulation, where black [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]
Figure 5
Figure 5. Figure 5: The figure compares the global 21-cm brightness temperature from our emulator with the 21SSD simulation, where black [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: The figure compares the global 21-cm brightness temperature from our emulator with the 21SSD simulation, where black [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]
Figure 7
Figure 7. Figure 7: The plot illustrates the global 21-cm brightness temperature (Tb) versus redshift (z) for various fX values, with the color gradient representing fX values ranging from 0.1 to 10.0. It demonstrates the effect of X-ray heating on the 21-cm signal, indicating that higher…

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

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

  1. Learning Cosmology from Nearest Neighbour Statistics

    astro-ph.CO 2025-11 conditional novelty 6.0 of 10

    Nearest-neighbour distance maps, combined with kNN-CDFs in a hybrid neural network, constrain Ωm and σ8 from Quijote halos with R2=0.80 and 0.93, matching or beating point-cloud methods at a fraction of the compute.

Reference graph

Works this paper leans on

175 extracted references · 67 canonical work pages · cited by 1 Pith paper

  1. [1]

    & Haehnelt, M

    Abel, T. & Haehnelt, M. G. 1999, The Astrophysical Journal, 520, L13

    1999

  2. [2]

    E., Alexander, P., et al

    Adams, T., Aguirre, J. E., Alexander, P., et al. 2023, The Astrophysical Journal, 945, 124

    2023

  3. [3]

    2020, Astronomy & Astrophysics, 641, A6

    Aghanim, N., Akrami, Y ., Ashdown, M., et al. 2020, Astronomy & Astrophysics, 641, A6

    2020

  4. [4]

    B., & Schaffer, M

    Ahrens, A., Hansen, C. B., & Schaffer, M. E. 2023, The Stata Journal, 23, 909

    2023

  5. [5]

    2020, in 2020 international conference on emerging trends in information technology and engineering (ic-ETITE), IEEE, 1–5 Al Bataineh, A., Kaur, D., & Jalali, S

    Ajit, A., Acharya, K., & Samanta, A. 2020, in 2020 international conference on emerging trends in information technology and engineering (ic-ETITE), IEEE, 1–5 Al Bataineh, A., Kaur, D., & Jalali, S. M. J. 2022, IEEE Access, 10, 36963

    2020

  6. [6]

    S., Parsons, A

    Ali, Z. S., Parsons, A. R., Zheng, H., et al. 2015, The Astrophysical Journal, 809, 61

    2015

  7. [7]

    & Geetha, M

    Aloysius, N. & Geetha, M. 2017, in 2017 international conference on communi- cation and signal processing (ICCSP), IEEE, 0588–0592

    2017

  8. [8]

    2018, in Journal of physics: conference series, V ol

    Alzubi, J., Nayyar, A., & Kumar, A. 2018, in Journal of physics: conference series, V ol. 1142, IOP Publishing, 012012

    2018

Show all 175 references
  1. [9]

    B., Liu, T., & Langlois, O

    Amrani, A., Hamida, A. B., Liu, T., & Langlois, O. 2018, in Transport Research Arena (TRA) 2018

    2018

  2. [10]

    F., Rocha, A

    Azevedo, B. F., Rocha, A. M. A., & Pereira, A. I. 2024, Machine Learning, 113, 4055

    2024

  3. [11]

    2009, Astronomy & Astrophysics, 495, 389

    Baek, S., Di Matteo, P., Semelin, B., Combes, F., & Revaz, Y . 2009, Astronomy & Astrophysics, 495, 389

    2009

  4. [12]

    2010, Astronomy & Astrophysics, 523, A4

    Baek, S., Semelin, B., Di Matteo, P., Revaz, Y ., & Combes, F. 2010, Astronomy & Astrophysics, 523, A4

    2010

  5. [13]

    2021, Swarm and Evolutionary Computation, 65, 100913

    Bakurov, I., Castelli, M., Gau, O., Fontanella, F., & Vanneschi, L. 2021, Swarm and Evolutionary Computation, 65, 100913

    2021

  6. [14]

    & Loeb, A

    Barkana, R. & Loeb, A. 2001, Physics reports, 349, 125

    2001

  7. [15]

    & Loeb, A

    Barkana, R. & Loeb, A. 2005, The Astrophysical Journal, 624, L65

    2005

  8. [16]

    1994, IEEE transactions on neural net- works, 5, 157

    Bengio, Y ., Simard, P., & Frasconi, P. 1994, IEEE transactions on neural net- works, 5, 157

    1994

  9. [17]

    2012, The Journal of Machine Learning Research, 13, 1063

    Biau, G. 2012, The Journal of Machine Learning Research, 13, 1063

    2012

  10. [18]

    Blessie, E. C. & Karthikeyan, E. 2012, Journal of Algorithms & Computational Technology, 6, 385

    2012

  11. [19]

    D., Rogers, A

    Bowman, J. D., Rogers, A. E., Monsalve, R. A., Mozdzen, T. J., & Mahesh, N. 2018, Nature, 555, 67

    2018

  12. [20]

    1996, University of California Berkeley

    Breiman, L. 1996, University of California Berkeley

    1996

  13. [21]

    G., et al

    Breitman, D., Mesinger, A., Murray, S. G., et al. 2024, Monthly Notices of the Royal Astronomical Society, 527, 9833

    2024

  14. [22]

    K., Bekki, K., & Groves, B

    Cavanagh, M. K., Bekki, K., & Groves, B. A. 2021, Monthly Notices of the Royal Astronomical Society, 506, 659

    2021

  15. [23]

    & Choudhury, T

    Chakraborty, A. & Choudhury, T. R. 2025, arXiv preprint arXiv:2502.12004

    2025 arXiv

  16. [24]

    2023, The Astrophysical Journal, 943, 138

    Chen, N., Trac, H., Mukherjee, S., & Cen, R. 2023, The Astrophysical Journal, 943, 138

    2023

  17. [25]

    2021, Information Sciences, 579, 15

    Cheng, S., Wu, Y ., Li, Y ., Yao, F., & Min, F. 2021, Information Sciences, 579, 15

    2021

  18. [26]

    J., & Jurman, G

    Chicco, D., Warrens, M. J., & Jurman, G. 2021, PeerJ Computer Science, 7, e623

    2021

  19. [27]

    Y ., Coyner, A

    Choi, R. Y ., Coyner, A. S., Kalpathy-Cramer, J., Chiang, M. F., & Campbell, J. P. 2020, Translational vision science & technology, 9, 14

    2020

  20. [28]

    2014, arXiv preprint arXiv:1412.3555

    Chung, J., Gulcehre, C., Cho, K., & Bengio, Y . 2014, arXiv preprint arXiv:1412.3555

    2014 arXiv

  21. [29]

    & Xiao, B

    Cong, J. & Xiao, B. 2014, in International conference on artificial neural net- works, Springer, 281–290

    2014

  22. [30]

    & Zhou, Y

    Cong, S. & Zhou, Y . 2023, Artificial Intelligence Review, 56, 1905

    2023

  23. [31]

    R., & Stevens, J

    Cutler, A., Cutler, D. R., & Stevens, J. R. 2012, Ensemble machine learning: Methods and applications, 157

    2012

  24. [32]

    K., Ghara, R., Majumdar, S., et al

    Datta, K. K., Ghara, R., Majumdar, S., et al. 2016, Journal of Astrophysics and Astronomy, 37, 1

    2016

  25. [33]

    & Ferrara, A

    Dayal, P. & Ferrara, A. 2018, Physics Reports, 780, 1 de Lera Acedo, E. 2019, in 2019 International Conference on Electromagnetics in Advanced Applications (ICEAA), IEEE, 0626–0629

    2018

  26. [34]

    Demiss, B. A. & Elsaigh, W. A. 2024, Engineering Research Express, 6, 032102

    2024

  27. [35]

    E., Hall, P

    Dewdney, P. E., Hall, P. J., Schilizzi, R. T., & Lazio, T. J. L. 2009, Proceedings of the IEEE, 97, 1482

    2009

  28. [36]

    T., et al

    Dhandha, J., Gessey-Jones, T., Bevins, H. T., et al. 2025, arXiv preprint arXiv:2503.21687

    2025 arXiv

  29. [37]

    2011, Journal of machine learning research, 12

    Duchi, J., Hazan, E., & Singer, Y . 2011, Journal of machine learning research, 12

    2011

  30. [38]

    2020, in 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, 1–8

    Dudek, G. 2020, in 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, 1–8

    2020

  31. [39]

    2018, in NASA Formal Methods Symposium, Springer, 121–138

    Dutta, S., Jha, S., Sankaranarayanan, S., & Tiwari, A. 2018, in NASA Formal Methods Symposium, Springer, 121–138

    2018

  32. [40]

    2022, arXiv preprint arXiv:2203.08056

    Dvorkin, C., Mishra-Sharma, S., Nord, B., et al. 2022, arXiv preprint arXiv:2203.08056

    2022 arXiv

  33. [41]

    B., Graziani, L., Ciardi, B., et al

    Eide, M. B., Graziani, L., Ciardi, B., et al. 2018, Monthly Notices of the Royal Astronomical Society, 476, 1174

    2018

  34. [42]

    2015, Monthly Notices of the Royal Astronomical Society, 451, 904

    Erkal, D. 2015, Monthly Notices of the Royal Astronomical Society, 451, 904

    2015

  35. [43]

    Ewen, H. I. & Purcell, E. M. 1951, Nature, 168, 356

    1951

  36. [44]

    2024, International Journal of Mathematics, Statistics, and Com- puter Science, 2, 96

    Faaique, M. 2024, International Journal of Mathematics, Statistics, and Com- puter Science, 2, 96

    2024

  37. [45]

    2014, Nature, 506, 197

    Fialkov, A., Barkana, R., & Visbal, E. 2014, Nature, 506, 197

    2014

  38. [46]

    2017, Monthly Notices of the Royal Astronomical Society, 464, 3498

    Fialkov, A., Cohen, A., Barkana, R., & Silk, J. 2017, Monthly Notices of the Royal Astronomical Society, 464, 3498

    2017

  39. [47]

    Field, G. B. 1958, Proceedings of the IRE, 46, 240

    1958

  40. [48]

    L., Ryan, R

    Finkelstein, S. L., Ryan, R. E., Papovich, C., et al. 2015, The Astrophysical Jour- nal, 810, 71

    2015

  41. [49]

    2016, in 2016 31st Youth academic annual conference of Chinese association of automation (Y AC), IEEE, 324–328

    Fu, R., Zhang, Z., & Li, L. 2016, in 2016 31st Youth academic annual conference of Chinese association of automation (Y AC), IEEE, 324–328

    2016

  42. [50]

    Furlanetto, S. R. & Oh, S. P. 2016, Monthly Notices of the Royal Astronomical Society, 457, 1813

    2016

  43. [51]

    R., Oh, S

    Furlanetto, S. R., Oh, S. P., & Briggs, F. H. 2006, Physics reports, 433, 181

    2006

  44. [52]

    G., Mukthar, A., Husham, F., Maaroof, R

    Galety, M. G., Mukthar, A., Husham, F., Maaroof, R. J., & Rofoo, F. 2021, Tech- nium, 3

    2021

  45. [53]

    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

  46. [54]

    2017, Big Data Research, 9, 28

    Genuer, R., Poggi, J.-M., Tuleau-Malot, C., & Villa-Vialaneix, N. 2017, Big Data Research, 9, 28

    2017

  47. [55]

    M., & Trac, H

    Glazer, D., Rau, M. M., & Trac, H. 2018, arXiv preprint arXiv:1808.00553

    2018 arXiv

  48. [56]

    Gnedin, N. Y . & Madau, P. 2022, Living Reviews in Computational Astro- physics, 8, 3

    2022

  49. [57]

    & Mesinger, A

    Greig, B. & Mesinger, A. 2015, Monthly Notices of the Royal Astronomical Society, 449, 4246

    2015

  50. [58]

    2013, Scholarpedia, 8, 1888

    Grossberg, S. 2013, Scholarpedia, 8, 1888

    2013

  51. [59]

    2018, Pattern recognition, 77, 354

    Gu, J., Wang, Z., Kuen, J., et al. 2018, Pattern recognition, 77, 354

    2018

  52. [60]

    2024, Research in Astronomy and Astrophysics, 24, 125019

    Guo, X., Fang, G., Feng, H., & Zhang, R. 2024, Research in Astronomy and Astrophysics, 24, 125019

    2024

  53. [61]

    S., Habaebi, M

    Halbouni, A., Gunawan, T. S., Habaebi, M. H., et al. 2022, IEEE Access, 10, 99837

    2022

  54. [62]

    Hasan, M. A. M., Nasser, M., Ahmad, S., & Molla, K. I. 2016, Journal of infor- mation security, 7, 129

    2016

  55. [63]

    P., Cohen, W

    Healey, S. P., Cohen, W. B., Yang, Z., et al. 2018, Remote Sensing of Environ- ment, 204, 717 HERA Collaboration. 2023, The Astrophysical Journal, 945, 124

    2018

  56. [64]

    Hirata, C. M. 2006, Monthly Notices of the Royal Astronomical Society, 367, 259

    2006

  57. [65]

    & Schmidhuber, J

    Hochreiter, S. & Schmidhuber, J. 1997, Neural computation, 9, 1735

    1997

  58. [66]

    O., Over, T

    Hodson, T. O., Over, T. M., & Foks, S. S. 2021, Journal of Advances in Modeling Earth Systems, 13, e2021MS002681

    2021

  59. [67]

    Hogg, D. W. & Foreman-Mackey, D. 2018, The Astrophysical Journal Supple- ment Series, 236, 11

    2018

  60. [68]

    M., Salmasi, B

    Hosseini, S. M., Salmasi, B. S., Tabasi, S. S., & Firouzjaee, J. T. 2023, arXiv preprint arXiv:2306.12954

    2023 arXiv

  61. [69]

    Jaiswal, J. K. & Samikannu, R. 2017, in 2017 world congress on computing and communication technologies (WCCCT), Ieee, 65–68

    2017

  62. [70]

    2024, arXiv preprint arXiv:2411.08943

    Jamieson, N., Smith, A., Neyer, M., et al. 2024, arXiv preprint arXiv:2411.08943

    2024 arXiv

  63. [71]

    2018, Advances in Data Analysis and Classification, 12, 885

    Janitza, S., Celik, E., & Boulesteix, A.-L. 2018, Advances in Data Analysis and Classification, 12, 885

    2018

  64. [72]

    Jie, H. J. & Wanda, P. 2020, International Journal of Computational Intelligence Systems, 13, 66

    2020

  65. [73]

    & Fluri, J

    Kacprzak, T. & Fluri, J. 2022, Physical Review X, 12, 031029

    2022

  66. [74]

    M., Nikolic, B., Thyagarajan, N., et al

    Keller, P. M., Nikolic, B., Thyagarajan, N., et al. 2023, Monthly Notices of the Royal Astronomical Society, 524, 583

    2023

  67. [75]

    Kingma, D. P. & Ba, J. 2014, arXiv preprint arXiv:1412.6980

    2014 arXiv

  68. [76]

    2021, Mechanical systems and sig- nal processing, 151, 107398

    Kiranyaz, S., Avci, O., Abdeljaber, O., et al. 2021, Mechanical systems and sig- nal processing, 151, 107398

    2021

  69. [77]

    2003, New Astronomy Reviews, 47, 939

    Kosowsky, A. 2003, New Astronomy Reviews, 47, 939

    2003

  70. [78]

    V ., Klypin, A

    Kravtsov, A. V ., Klypin, A. A., & Khokhlov, A. M. 1997, The Astrophysical Journal Supplement Series, 111, 73

    1997

  71. [79]

    2023, Computers, 12, 151

    Krichen, M. 2023, Computers, 12, 151

    2023

  72. [80]

    & Johnson, K

    Kuhn, M. & Johnson, K. 2019, Feature engineering and selection: A practical approach for predictive models (Chapman and Hall/CRC) Article number, page 17 A&A proofs:manuscript no. main

    2019

  73. [81]

    2021, IEEE transactions on neural networks and learning systems, 33, 6999

    Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. 2021, IEEE transactions on neural networks and learning systems, 33, 6999

    2021

  74. [82]

    R., & Zavala, J

    Liu, H., Slatyer, T. R., & Zavala, J. 2016, Physical Review D, 94, 063507

    2016

  75. [83]

    & Høye, J

    Lomba, E. & Høye, J. S. 2014, Molecular Physics, 112, 2892

    2014

  76. [84]

    Lui, H. F. & Wolf, W. R. 2019, Journal of Fluid Mechanics, 872, 963

    2019

  77. [85]

    Ma, Q.-B., Fiaschi, S., Ciardi, B., Busch, P., & Eide, M. B. 2022, Monthly No- tices of the Royal Astronomical Society, 513, 1513

    2022

  78. [86]

    J., V olonteri, M., Haardt, F., & Oh, S

    Madau, P., Rees, M. J., V olonteri, M., Haardt, F., & Oh, S. P. 2004, The Astro- physical Journal, 604, 484

    2004

  79. [87]

    2006, Monthly Notices of the Royal Astronomical Society, 369, 1719

    Mapelli, M., Ferrara, A., & Pierpaoli, E. 2006, Monthly Notices of the Royal Astronomical Society, 369, 1719

    2006

  80. [88]

    G., Bobin, J., & Carucci, I

    Mertens, F. G., Bobin, J., & Carucci, I. P. 2024, Monthly Notices of the Royal Astronomical Society, 527, 3517

    2024

  81. [89]

    2011, Monthly Notices of the Royal Astronomical Society, 411, 955

    Mesinger, A., Furlanetto, S., & Cen, R. 2011, Monthly Notices of the Royal Astronomical Society, 411, 955

    2011

  82. [90]

    2016, Monthly Notices of the Royal Astronomical Society, 459, 2342

    Mesinger, A., Greig, B., & Sobacchi, E. 2016, Monthly Notices of the Royal Astronomical Society, 459, 2342

    2016

  83. [91]

    2011, Astronomy & Astrophysics, 528, A149

    Mirabel, I., Dijkstra, M., Laurent, P., Loeb, A., & Pritchard, J. 2011, Astronomy & Astrophysics, 528, A149

    2011

  84. [92]

    Mirocha, J., Harker, G. J. A., & Burns, J. O. 2017, The Astrophysical Journal, 843, 46

    2017

  85. [93]

    R., & Ferrara, A

    Mitra, S., Choudhury, T. R., & Ferrara, A. 2015, Monthly Notices of the Royal Astronomical Society: Letters, 454, L76

    2015

  86. [94]

    & Kulkarni, G

    Mittal, S. & Kulkarni, G. 2022, Monthly Notices of the Royal Astronomical Society, 515, 3947

    2022

  87. [95]

    & Barkana, R

    Mondal, R. & Barkana, R. 2023, Nature Astronomy, 7, 1025

    2023

  88. [96]

    Morales, M. F. & Wyithe, J. S. B. 2010, Annual review of astronomy and astro- physics, 48, 127

    2010

  89. [97]

    G., Greig, B., Mesinger, A., et al

    Murray, S. G., Greig, B., Mesinger, A., et al. 2020, arXiv preprint arXiv:2010.15121

    2020 arXiv

  90. [98]

    & D’Aloisio, A

    Nasir, F. & D’Aloisio, A. 2020, Monthly Notices of the Royal Astronomical Society, 494, 3080

    2020

  91. [99]

    2017, Horizons

    Nasteski, V . 2017, Horizons. b, 4, 56

    2017

  92. [100]

    R., Bradley, R

    Neben, A. R., Bradley, R. F., Hewitt, J. N., et al. 2016, The Astrophysical Journal, 826, 199

    2016

  93. [101]

    2016, Advances in neural information pro- cessing systems, 29

    Neil, D., Pfeiffer, M., & Liu, S.-C. 2016, Advances in neural information pro- cessing systems, 29

    2016

  94. [102]

    H., Ly, H.-B., Ho, L

    Nguyen, Q. H., Ly, H.-B., Ho, L. S., et al. 2021, Mathematical Problems in En- gineering, 2021, 4832864

    2021

  95. [103]

    2023, arXiv preprint arXiv:2310.07358

    Ni, S., Li, Y ., & Zhang, X. 2023, arXiv preprint arXiv:2310.07358

    2023

  96. [104]

    Nosouhian, S., Nosouhian, F., & Khoshouei, A. K. 2021, Preprints

    2021

  97. [105]

    2019, arXiv preprint arXiv:1902.10159

    Ntampaka, M., Avestruz, C., Boada, S., et al. 2019, arXiv preprint arXiv:1902.10159

    2019 arXiv

  98. [106]

    2015, Understanding LSTM Networks

    Olah, C. 2015, Understanding LSTM Networks

    2015

  99. [107]

    G., Bandura, K., et al

    Paciga, G., Albert, J. G., Bandura, K., et al. 2013, Monthly Notices of the Royal Astronomical Society, 433, 639

    2013

  100. [108]

    2013, in International conference on machine learning, Pmlr, 1310–1318

    Pascanu, R., Mikolov, T., & Bengio, Y . 2013, in International conference on machine learning, Pmlr, 1310–1318

    2013

  101. [109]

    & Rane, M

    Patil, A. & Rane, M. 2021, Information and Communication Technology for Intelligent Systems: Proceedings of ICTIS 2020, V olume 1, 21

    2021

  102. [110]

    K., Šoltinsk `y, T., Maitra, S., & Kulkarni, G

    Patil, S. K., Šoltinsk `y, T., Maitra, S., & Kulkarni, G. 2025, arXiv preprint arXiv:2507.11611

    2025

  103. [111]

    2013, Ex- perimental Astronomy, 36, 319

    Patra, N., Subrahmanyan, R., Raghunathan, A., & Udaya Shankar, N. 2013, Ex- perimental Astronomy, 36, 319

    2013

  104. [112]

    2011, Journal of machine learning research, 12, 2825

    Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, Journal of machine learning research, 12, 2825

    2011

  105. [113]

    D., Warnars, H

    Prabowo, Y . D., Warnars, H. L. H. S., Budiharto, W., et al. 2018, in 2018 Indone- sian association for pattern recognition international conference (INAPR), IEEE, 51–56

    2018

  106. [114]

    Pritchard, J. R. & Furlanetto, S. R. 2007, Monthly Notices of the Royal Astro- nomical Society, 376, 1680

    2007

  107. [115]

    Pritchard, J. R. & Loeb, A. 2012, Reports on Progress in Physics, 75, 086901

    2012

  108. [116]

    2022, International Journal of Robotics and Control Systems, 2, 739

    Purwono, P., Ma’arif, A., Rahmaniar, W., et al. 2022, International Journal of Robotics and Control Systems, 2, 739

    2022

  109. [117]

    Rani, P., Kumar, R., Ahmed, N. M. S., & Jain, A. 2021, Journal of Reliable Intelligent Environments, 7, 263

    2021

  110. [118]

    Resende, P. A. A. & Drummond, A. C. 2018, ACM Computing Surveys (CSUR), 51, 1

    2018

  111. [119]

    2002, Monthly Notices of the Royal Astronomical Society, 336, L33

    Ricotti, M. 2002, Monthly Notices of the Royal Astronomical Society, 336, L33

    2002

  112. [120]

    2019, Advances in neural information processing systems, 32

    Roelofs, R., Shankar, V ., Recht, B., et al. 2019, Advances in neural information processing systems, 32

    2019

  113. [121]

    Rozos, E., Dimitriadis, P., Mazi, K., & Koussis, A. D. 2021, Hydrology, 8, 67

    2021

  114. [122]

    2016, arXiv preprint arXiv:1609.04747

    Ruder, S. 2016, arXiv preprint arXiv:1609.04747

    2016 arXiv

  115. [123]

    A., Carlstrom, J

    Ruhl, J., Ade, P. A., Carlstrom, J. E., et al. 2004, in Millimeter and Submillimeter Detectors for Astronomy II, V ol. 5498, SPIE, 11–29

    2004

  116. [124]

    Rybicki, G. B. & Lightman, A. P. 2024, Radiative processes in astrophysics (John Wiley & Sons)

    2024

  117. [125]

    G., Ferramacho, L., Silva, M., Amblard, A., & Cooray, A

    Santos, M. G., Ferramacho, L., Silva, M., Amblard, A., & Cooray, A. 2010, Monthly Notices of the Royal Astronomical Society, 406, 2421

    2010

  118. [126]

    2023, Monthly Notices of the Royal Astronomical Society, 525, 6097

    Saxena, A., Cole, A., Gazagnes, S., et al. 2023, Monthly Notices of the Royal Astronomical Society, 525, 6097

    2023

  119. [127]

    2015, Neural networks, 61, 85

    Schmidhuber, J. 2015, Neural networks, 61, 85

    2015

  120. [128]

    Schmit, C. J. & Pritchard, J. R. 2018, Monthly Notices of the Royal Astronomical Society, 475, 1213

    2018

  121. [129]

    & Rees, M

    Scott, D. & Rees, M. J. 1990, Monthly Notices of the Royal Astronomical Soci- ety, vol. 247, p. 510, 247, 510

    1990

  122. [130]

    & Grolinger, K

    Sehovac, L. & Grolinger, K. 2020, Ieee Access, 8, 36411

    2020

  123. [131]

    2007, Astronomy & Astrophysics, 474, 365

    Semelin, B., Combes, F., & Baek, S. 2007, Astronomy & Astrophysics, 474, 365

    2007

  124. [132]

    2017, Monthly Notices of the Royal Astronomical Society, 472, 4508

    Semelin, B., Eames, E., Bolgar, F., & Caillat, M. 2017, Monthly Notices of the Royal Astronomical Society, 472, 4508

    2017

  125. [133]

    2022, in Radar Remote Sensing (Elsevier), 175–186

    Shakya, A., Biswas, M., & Pal, M. 2022, in Radar Remote Sensing (Elsevier), 175–186

    2022

  126. [134]

    2021, Processes, 9, 2015

    Shanmugasundar, G., Vanitha, M., ˇCep, R., et al. 2021, Processes, 9, 2015

    2021

  127. [135]

    2023, Nature Astronomy, 7, 1116

    Shao, Y ., Xu, Y ., Wang, Y ., et al. 2023, Nature Astronomy, 7, 1116

    2023

  128. [136]

    Shewalkar, A., Nyavanandi, D., & Ludwig, S. A. 2019, Journal of Artificial In- telligence and Soft Computing Research, 9, 235

    2019

  129. [137]

    2019, in 2019 Asia-Pacific signal and information processing association annual summit and conference (AP- SIPA ASC), IEEE, 939–944

    Shi, X., Wang, T., Wang, L., Liu, H., & Yan, N. 2019, in 2019 Asia-Pacific signal and information processing association annual summit and conference (AP- SIPA ASC), IEEE, 939–944

    2019

  130. [138]

    2023, Publications of the Astronomical Society of Japan, 75, S1

    Shimabukuro, H., Hasegawa, K., Kuchinomachi, A., Yajima, H., & Yoshiura, S. 2023, Publications of the Astronomical Society of Japan, 75, S1

    2023

  131. [139]

    & Semelin, B

    Shimabukuro, H. & Semelin, B. 2017, Monthly Notices of the Royal Astronom- ical Society, 468, 3869

    2017

  132. [140]

    T., & Shapiro, P

    Shukla, H., Mellema, G., Iliev, I. T., & Shapiro, P. R. 2016, Monthly Notices of the Royal Astronomical Society, 458, 135

    2016

  133. [141]

    2022, Nature Astronomy, 6, 607

    Singh, S., Nambissan T, J., Subrahmanyan, R., et al. 2022, Nature Astronomy, 6, 607

    2022

  134. [142]

    Siraj, M. S. & Ahad, M. 2020, in 2020 Joint 9th International Conference on

    2020

  135. [143]

    Informatics, Electronics & Vision (ICIEV) and 2020 4th International Con- ference on Imaging, Vision & Pattern Recognition (icIVPR), IEEE, 1–7

    2020

  136. [144]

    & James, A

    Smagulova, K. & James, A. P. 2019, The European Physical Journal Special Topics, 228, 2313 Šoltinsk`y, T., Kulkarni, G., Tendulkar, S. P., & Bolton, J. S. 2025, Monthly No- tices of the Royal Astronomical Society, 537, 364

    2019

  137. [145]

    L., Miller, M

    Speiser, J. L., Miller, M. E., Tooze, J., & Ip, E. 2019, Expert systems with appli- cations, 134, 93

    2019

  138. [146]

    2021, Machine Learning: Science and Technology, 2, 035022

    Stuke, A., Rinke, P., & Todorovi ´c, M. 2021, Machine Learning: Science and Technology, 2, 035022

    2021

  139. [147]

    & Zel’Dovich, Y

    Sunyaev, R. & Zel’Dovich, Y . B. 1980, Annual review of astronomy and astro- physics, 18, 537

    1980

  140. [148]

    & Konde, A

    Thakur, A. & Konde, A. 2021, International Journal for Research in Applied Science and Engineering Technology, 9, 407

    2021

  141. [149]

    2020, The Astrophysical Journal Letters, 891, L10

    Tilvi, V ., Malhotra, S., Rhoads, J., et al. 2020, The Astrophysical Journal Letters, 891, L10

    2020

  142. [150]

    J., Goeke, R., Bowman, J

    Tingay, S. J., Goeke, R., Bowman, J. D., et al. 2013, Publications of the Astro- nomical Society of Australia, 30, e007

    2013

  143. [151]

    2024, Monthly Notices of the Royal Astronomical Society, 528, 1945

    Tripathi, A., Datta, A., Choudhury, M., & Majumdar, S. 2024, Monthly Notices of the Royal Astronomical Society, 528, 1945

    2024

  144. [152]

    2021, in NeurIPS 2020 Competition and Demonstration Track, PMLR, 3–26 Van de Hulst, H

    Turner, R., Eriksson, D., McCourt, M., et al. 2021, in NeurIPS 2020 Competition and Demonstration Track, PMLR, 3–26 Van de Hulst, H. 1945, Nederlandsch Tijdschrift voor Natuurkunde, 11, 210 Van de Schoot, R., Depaoli, S., King, R., et al. 2021, Nature Reviews Methods Primers, ...

    2021

  145. [153]

    2017, Quantum Science and Tech- nology, 3, 015004

    Varsamopoulos, S., Criger, B., & Bertels, K. 2017, Quantum Science and Tech- nology, 3, 015004

    2017

  146. [154]

    2019, Astronomy & Astrophysics, 627, A5

    Vazza, F., Ettori, S., Roncarelli, M., et al. 2019, Astronomy & Astrophysics, 627, A5

    2019

  147. [155]

    L., & Shull, J

    Venkatesan, A., Giroux, M. L., & Shull, J. M. 2001, The Astrophysical Journal, 563, 1

    2001

  148. [156]

    2018, Physical Review D, 98, 103513

    Venumadhav, T., Dai, L., Kaurov, A., & Zaldarriaga, M. 2018, Physical Review D, 98, 103513

    2018

  149. [157]

    2010, in Lectures on Cosmology: Accelerated Expansion of the Uni- verse (Springer), 147–177

    Verde, L. 2010, in Lectures on Cosmology: Accelerated Expansion of the Uni- verse (Springer), 147–177

    2010

  150. [158]

    Victoria, A. H. & Maragatham, G. 2021, Evolving Systems, 12, 217 V onlanthen, P., Semelin, B., Baek, S., & Revaz, Y . 2011, Astronomy & Astro- physics, 532, A97

    2021

  151. [159]

    H., Dahlsten, O., Kristjánsson, H., Gardner, R., & Kim, M

    Wan, K. H., Dahlsten, O., Kristjánsson, H., Gardner, R., & Kim, M. 2017, npj Quantum information, 3, 36

    2017

  152. [160]

    2023, ACM Computing Surveys, 55, 1

    Wang, X., Jin, Y ., Schmitt, S., & Olhofer, M. 2023, ACM Computing Surveys, 55, 1

    2023

  153. [161]

    Wolpert, D. H. 1992, Neural networks, 5, 241

    1992

  154. [162]

    1952, The Astronomical Journal, 57, 31

    Wouthuysen, S. 1952, The Astronomical Journal, 57, 31

    1952

  155. [163]

    2022, Information Sciences, 608, 453

    Xue, Y ., Tong, Y ., & Neri, F. 2022, Information Sciences, 608, 453

    2022

  156. [164]

    A., Subasi, A., & Rattay, F

    Yaman, M. A., Subasi, A., & Rattay, F. 2018, Symmetry, 10, 651 Article number, page 18 Hosseini and Soleimanpour: RNN-CNN Reconstruction of 21-cm Brightness Temperature

    2018

  157. [165]

    Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. 2018, Insights into imag- ing, 9, 611

    2018

  158. [166]

    2015, Monthly Notices of the Royal Astronomical Society, 447, 1692

    Yang, Q.-X., Xie, F.-G., Yuan, F., et al. 2015, Monthly Notices of the Royal Astronomical Society, 447, 1692

    2015

  159. [167]

    2020, in 2020 International workshop on electronic communication and artificial intelligence (IWECAI), IEEE, 98–101 Yi˘git, G

    Yang, S., Yu, X., & Zhou, Y . 2020, in 2020 International workshop on electronic communication and artificial intelligence (IWECAI), IEEE, 98–101 Yi˘git, G. & Amasyali, M. F. 2021, in 2021 international conference on INnova- tions in intelligent SysTems and applications (INIST...

    2020

  160. [168]

    2019, in Journal of physics: Conference series, V ol

    Ying, X. 2019, in Journal of physics: Conference series, V ol. 1168, IOP Publish- ing, 022022

    2019

  161. [169]

    2003, The Astrophysical Journal, 591, L1

    Yoshida, N., Sokasian, A., Hernquist, L., & Springel, V . 2003, The Astrophysical Journal, 591, L1

    2003

  162. [170]

    & Liu, H

    Yu, L. & Liu, H. 2003, in Proceedings of the 20th international conference on machine learning (ICML-03), 856–863

    2003

  163. [171]

    2007, The Astrophysical Journal, 654, 12

    Zahn, O., Lidz, A., McQuinn, M., et al. 2007, The Astrophysical Journal, 654, 12

    2007

  164. [172]

    2021, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, North Carolina, 27606

    Zargar, S. 2021, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, North Carolina, 27606

    2021

  165. [173]

    2012, The first galaxies: theoretical predictions and observational clues, 45

    Zaroubi, S. 2012, The first galaxies: theoretical predictions and observational clues, 45

    2012

  166. [174]

    2022, Ieee Access, 10, 47361

    Zeng, C., Ma, C., Wang, K., & Cui, Z. 2022, Ieee Access, 10, 47361

    2022

  167. [175]

    2005, The Astrophysical Journal, 622, 1356 Article number, page 19

    Zygelman, B. 2005, The Astrophysical Journal, 622, 1356 Article number, page 19

    2005

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.