Pith. sign in

REVIEW 4 major objections 5 minor 2 cited by

Learning Optimal and Interpretable Summary Statistics of Galaxy Catalogs with SBI

T0 review · 4 major / 5 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 This paper claims that jointly training a graph neural network and a neural density estimator produces summary statistics from galaxy catalogs that maximize the mutual information with the cosmological parameters $\Omega_m$ and…

desk verdict A solid, honest SBI paper that overreaches on 'optimal' summary statistics but deserves a serious referee. read the letter →

arxiv 2411.08957 v1 pith:TPUHO7FF submitted 2024-11-13 astro-ph.CO

Kai Lehman , Sven Krippendorf , Jochen Weller , Klaus Dolag This is my paper · ORCID
classification astro-ph.CO
keywords simulation-basedinferencesummarystatisticsgraphneuralnetworkscosmologicalparametergalaxycatalogsbaryonicfeedbackposteriorestimationmutualinformation
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 argues that a galaxy catalog can be compressed into a short vector of learned summary statistics that is information-optimal for inferring the cosmological parameters $\Omega_m$ and $\sigma_8$, without writing down a likelihood. The authors train a graph neural network and a neural density estimator jointly, so the loss is the expected Kullback-Leibler divergence; they claim a fully converged joint network maximizes the mutual information between summary and parameters. On hydrodynamical simulations with three different baryonic feedback implementations, the resulting summaries recover $\Omega_m$ well, fail to constrain $\sigma_8$ strongly (attributed to the small simulation box), remain interpretable via a handful of principal components, and let the authors identify the scales that matter. The point of the paper is that hand-crafted statistics can be replaced by learned, low-dimensional summaries that are also a diagnostic tool for baryonic physics.

What carries the argument

The machine is a joint network built from two parts: a graph neural network compression network $h_\lambda$ that maps a galaxy graph to a vector $\mathbf{t}$, and a masked autoregressive flow $q_\phi(\theta|\mathbf{t})$ that estimates the posterior. The galaxy graph encodes galaxy positions in the edges (via separation and two rotation-invariant angle cosines), the $z$-component of peculiar velocity as the sole node feature, and $\log_{10}$ of the galaxy number as the global feature. The flow's first affine block takes the compressed vector as a conditioning input, and backpropagation through the expected negative log posterior trains both networks together. That end-to-end KL loss is what carries the information-optimality argument: no separate compression loss, no covariance estimate, and no fiducial point in parameter space.

What would settle it

Train the same joint network on galaxy catalogs that add the full three-dimensional velocity and stellar mass as node features while keeping everything else fixed, and compare the width of the $\Omega_m$ and $\sigma_8$ posteriors on the same validation boxes; noticeably tighter posteriors would show the original input representation threw away usable information, contradicting the optimality claim as stated.

Watch

Extended reading notes

Core claim

The central claim is that automatic compression and inference can be solved as one optimization: instead of choosing a loss for the compression network, the graph network's output is fed directly into a masked autoregressive flow that estimates the posterior $p(\theta|\mathrm{summary})$, and the expected KL divergence is minimized end-to-end. A fully converged joint network therefore maximizes the mutual information between the summary statistic and the cosmological parameters over the prior volume, making the learned summaries optimal within the chosen data representation. The authors demonstrate this on galaxy catalogs from three hydrodynamical simulation suites: $\Omega_m$ is inferred reliably, $\sigma_8$ is not (they attribute this to the small $25\,h^{-1}\,\mathrm{Mpc}$ box lacking large-scale modes), and the summaries encode baryonic feedback parameters even though those never enter the loss. They also show the summaries are low-dimensional in effect, with about five principal components explaining most variance, and that the physical scales the network uses include modes around $k = 5\!-\!30\,h/\mathrm{Mpc}$.

Load-bearing premise

The learned summaries are optimal only for the information already placed in the galaxy graph, which contains positions, the $z$-component of peculiar velocity, and galaxy count; if quantities omitted from this representation (such as the full velocity vector, stellar mass, or environment) carry information about $\Omega_m$ or $\sigma_8$, the summaries cannot be truly optimal.

Editorial extensions

If this is right

  • The learned summaries can be used for likelihood-free cosmological parameter inference from galaxy catalogs, with performance at least competitive with existing machine-learning inference that predicts only means and standard deviations.
  • Because the summaries are low-dimensional and reproducible across training runs, they provide a candidate replacement for hand-crafted statistics in analyses where the likelihood is unknown.
  • The correlations between summary principal components and simulation parameters give a quantitative handle on which baryonic feedback processes matter for cosmological inference, and which do not.
  • Mapping simulations in summary space lets one compare feedback models directly; the observation that one suite occupies a larger volume explains why models trained on it generalize to the others.
  • Emulating the summary as a function of cosmology alone produces a baryon-marginalized summary that lies on a hypersurface in summary space, offering a route to baryon-robust inference.

Reading between the lines

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

  • If the optimality claim holds, any hand-crafted summary (power spectrum, bispectrum, counts-in-cells) can be at most as informative for $\Omega_m$ and $\sigma_8$ within this graph representation; a direct comparison of posterior widths would make that concrete.
  • The same joint-training scheme should transfer to survey-like catalogs with selection effects, redshift-space distortions, and masks, provided the simulator reproduces them; the summaries would then be optimal for the survey rather than for an idealized box.
  • The sharply cut-off forbidden region in summary space, which the authors leave unexplained, is likely a prior boundary or a physical limit of the feedback model space; identifying it could sharpen the interpretation of the summaries.
  • The baryon-marginalized emulator could be inverted to calibrate subgrid feedback parameters against observations, in the spirit of simulation calibration but now in an information-optimal summary space.
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 paper presents a simulation-based inference pipeline in which a graph neural network (CosmoGraphNet) compresses galaxy catalogs from the CAMELS simulations into a low-dimensional summary vector, and a masked autoregressive flow estimates the posterior of the cosmological parameters Omega_m and sigma_8. The compression and inference networks are trained jointly using the expected KL divergence, so that, in the limit of perfect convergence, the summary should maximize the mutual information between the compressed representation and the parameters. The authors validate their posteriors with simulation-based calibration, compare performance with earlier moment-network work, interpret the summaries through principal-component correlations with cosmological and astrophysical parameters and with power spectra, and use Isomap and t-SNE embeddings to compare the three CAMELS feedback models. They also train an emulator to construct summaries marginalized over baryonic parameters.

Significance. If the optimality claim were established for full galaxy catalogs, the approach would be a valuable addition to simulation-based cosmological inference, particularly for non-Gaussian, non-linear scales where hand-crafted summaries are known to be insufficient. The paper has notable strengths: it uses publicly available simulations, holds out a validation set, applies SBC, performs systematic hyperparameter optimization, checks robustness across 30 trained models, and includes a useful n-body control (Figure 10e) for astrophysical correlations. However, the headline 'optimal' result is conditional on the specific graph representation, and the demonstrated inference is effectively one-dimensional: sigma_8 is not constrained beyond the prior. The paper is therefore best read as a proof-of-concept for joint GNN-compression and flow-based posterior estimation, with the optimality claim needing substantial qualification.

major comments (4)
  1. [Sections 3.2 and 3.3] The optimality claim in Section 3.3 ('A fully converged compression/inference joint network therefore maximizes the mutual information between the summary statistic and the cosmological parameters') is valid only for the fixed input representation defined in Section 3.2. The graph encodes galaxy positions through edges (Eqs. 3.9-3.11), the z-component of peculiar velocity as the sole node feature, and log10 of the galaxy count as the global feature. The paper itself acknowledges in Section 3.2 that the construction of the galaxy catalog and the assembly of the graph are two lossy steps preceding the compression network. If information relevant to Omega_m or sigma_8 is contained in observables omitted from this graph (e.g., stellar mass, full velocity vector, or environmental measures), the learned summary cannot be information-optimal for the galaxy catalog, only for the chosen graph. Because the abstract and title claim 'optimal summary statistics' without this qualification, the central claim overstates the result. I recommend either revising the wording to 'optimal within the specified catalog and graph representation' or demonstrating empirically that adding such observables does not improve inference.
  2. [Section 4.1, Figures 2-4, and Table 3] The pipeline does not constrain sigma_8: the marginal posteriors in Figure 4 span essentially the full prior range, and the text states that sigma_8 'shows a large degree of bias learning only the mean of the dataset.' The reported chi^2_red values close to 1 for sigma_8 in Table 3 are exactly what a posterior equal to the prior would produce, so they do not indicate successful inference. Since sigma_8 is one of the two parameters of interest, the paper does not demonstrate that the learned summaries are informative for half of the stated inference task. The authors attribute this to the small simulation box, which is plausible, but the consequence for the central claim should be made explicit: the learned summaries are not shown to be optimal summaries for sigma_8 on the simulated catalogs. Please report posterior contraction relative to the prior (e.g., the ratio of posterior to prior standard deviation) for both parameters, and either remove sigma_8 from the headline claims or restrict the optimality discussion to Omega_m.
  3. [Sections 3.4 and 4.1, Figure 6] The simulation-based calibration test is used to support posterior validity, but the paper itself notes in Section 3.4 that a posterior estimate equal to the prior will pass the rank test. This is precisely the situation for sigma_8: if the learned posterior for sigma_8 is essentially the prior, the near-linear empirical CDF in Figure 6 for sigma_8 carries no evidence of informative calibration. The SBC result for sigma_8 should therefore be presented as a necessary but trivial consistency check, not as validation of a useful posterior. Additional diagnostics sensitive to informativeness, such as posterior contraction or coverage conditional on summary values, are needed before the sigma_8 posterior can be described as validated.
  4. [Section 4.2, Figures 7 and 10] The correlations between the learned summary principal components and the stellar feedback parameters ASN1 and ASN2 are described as being 'implicitly learned' even though these parameters never enter the loss function. Because ASN1 and ASN2 are varied in the training simulations and directly affect the galaxy catalogs, it is expected that summaries sensitive to the catalog will correlate with them; this does not by itself show that the network has learned to encode these parameters. The n-body control in Figure 10e is a good check that the Latin-hypercube layout was not memorized, but it does not distinguish between (a) the summary containing physical information about feedback processes and (b) correlations arising through degeneracies with Omega_m or sigma_8 in the finite training set. Please rephrase the interpretation, or provide a direct test such as training a regressor on the summary to predict ASN1/ASN2 and comparing with the null distribution from the n-body runs.
minor comments (5)
  1. [Section 3.3] In the bullet list, the sentence 'It is does not rely on either numerical derivatives...' contains a grammatical error and should read 'It does not rely on either numerical derivatives...'.
  2. [Section 4.4] The phrase 'baryon-robust summery' should be 'baryon-robust summary'.
  3. [Figure 2 caption] The phrase 'The sigma8 contour is not overcoming the prior' is awkward; consider rephrasing to 'The sigma8 posterior is not significantly narrower than the prior'.
  4. [Equation (4.1)] The definition of chi^2_red uses the index i both for the simulation index and implicitly for the parameter; please clarify that the sum runs over validation simulations and that theta_i, mu_i, and sigma_i refer to the scalar parameter value, posterior mean, and posterior standard deviation for that simulation.
  5. [References] Reference [90] appears to duplicate Reference [78]; please consolidate or distinguish them.

Circularity Check

0 steps flagged · score 0.0 of 10

No material circularity: the optimality claim follows from the stated training loss with an explicit convergence caveat, and the main inference results are benchmarked externally.

full rationale

I find no circular step in the paper's derivation chain. The central 'optimality' claim in Section 3.3 is a direct property of the training objective: minimizing the expected KL divergence (Eq. 3.8) is equivalent at a fully converged optimum to minimizing the conditional entropy H(theta|t), hence maximizing the mutual information I(theta;t) over the chosen function class. The paper explicitly attributes this property to Ref. [107] (BayesFlow) rather than deriving it from its own fitted results, and it hedges the claim with 'fully converged'. The optimality is also explicitly conditional on the input representation: the paper states that 'there are two steps preceeding the asymptotically optimal compression step h_lambda: the construction of the galaxy catalog and the assembly of the galaxy graph', so it does not pretend the graph is lossless. This is a correctness caveat about representation sufficiency, not a circular reduction. The inference results are validated on held-out validation sets and compared with an external benchmark (Ref. [117]); the quoted scores are from the published paper and provide independent context even though one author overlaps. The reported correlations of the learned summaries with ASN1 and ASN2 are not forced by the loss, because those parameters never enter the training loss, and the gravity-only control in Figure 10e shows the correlations vanish when astrophysical parameters have no physical effect. The baryon-marginalization emulator in Section 4.4 is a textbook application of the mean-squared-error conditional-mean property (Eqs. 4.4-4.7), not a renamed or fitted version of the claimed result. The numerous self-citations involving overlapping authors (e.g., Refs. [115,117,118,121] with K. Dolag) are methodological context, architecture choices, and benchmark references; none is load-bearing for the paper's central claims, and no uniqueness theorem is imported from the authors' prior work. The paper is self-contained against external simulations and benchmarks, so the appropriate finding is no significant circularity.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim of optimality relies on the BayesFlow theorem and on the sufficiency of the chosen galaxy graph representation. The method itself introduces no new physical entities. The free parameters are machine learning hyperparameters and the galaxy selection threshold, all tuned on validation data.

free parameters (4)
  • Galaxy mass threshold = 1.95e8 M_sun/h
    Chosen by hand based on the mean value used in Ref. [117]; affects the galaxy catalog composition and therefore the summary statistics. Mentioned in Section 3.2.
  • Summary statistic dimension N = Optimized between 2 and 200 via optuna
    The number of learned summary components is a hyperparameter; the best model's value is not explicitly reported. Mentioned in Section 4 and Table 2.
  • Graph linking length r = Optimized between 5e-3 and 5e-1 box length
    Controls graph connectivity and hence the information available to the GNN. Mentioned in Table 2.
  • GNN and flow hyperparameters = See Table 2 ranges
    Learning rate, batch size, layers, hidden channels, transforms, and hidden features are tuned on the validation set; exact best values not given in the text.
assumptions (5)
  • standard math Minimizing the expected KL divergence between true and approximate posterior maximizes the mutual information between the summary statistic and the parameters (BayesFlow theorem).
    Invoked in Section 3.3 for the information optimality claim; proven in Ref. [107].
  • domain assumption The CAMELS simulations with their subgrid feedback models are a sufficiently faithful representation of the universe for developing and testing these summaries.
    The analysis is entirely based on CAMELS boxes of side 25 Mpc/h; the authors note the box size limits large-scale modes and sigma_8 constraints (Sections 2 and 4.1).
  • domain assumption The galaxy graph constructed from positions, z-component of velocity, and galaxy count contains all information relevant for the claimed optimal inference.
    Optimality is conditional on this input representation; Section 3.2 begins compression at the galaxy level and discards the full phase space.
  • standard math The emulator trained with MSE loss converges to the conditional mean, marginalizing over baryonic parameters.
    Used in Section 4.4; based on the minimum risk estimator property (Ref. [101]).
  • domain assumption Neural networks are trained to (near) convergence and generalize from 900 to 2700 training simulations.
    The optimality and calibration results rely on the trained networks being good approximations; they use early stopping and validation but no guarantee of global convergence.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Learning Optimal and Interpretable Summary Statistics of Galaxy Catalogs with SBI." pith.science (2026). https://pith.science/paper/TPUHO7FF

@misc{pith2026241108957,
  author       = {Pith},
  title        = {Pith review of: Learning Optimal and Interpretable Summary Statistics of Galaxy Catalogs with SBI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TPUHO7FF}},
  note         = {Machine review of arXiv:2411.08957}
}
abstract

How much cosmological information can we reliably extract from existing and upcoming large-scale structure observations? Many summary statistics fall short in describing the non-Gaussian nature of the late-time Universe in comparison to existing and upcoming measurements. In this article we demonstrate that we can identify optimal summary statistics and that we can link them with existing summary statistics. Using simulation based inference (SBI) with automatic data-compression, we learn summary statistics for galaxy catalogs in the context of cosmological parameter estimation. By construction these summary statistics do not require the ability to write down an explicit likelihood. We demonstrate that they can be used for efficient parameter inference. These summary statistics offer a new avenue for analyzing different simulation models for baryonic physics with respect to their relevance for the resulting cosmological features. The learned summary statistics are low-dimensional, feature the underlying simulation parameters, and are similar across different network architectures. To link our models, we identify the relevant scales associated to our summary statistics (e.g. in the range of modes between $k= 5 - 30 h/\mathrm{Mpc}$) and we are able to match the summary statistics to underlying simulation parameters across various simulation models.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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

  1. Learning Intrinsic Alignments from Local Galaxy Environments

    astro-ph.CO 2025-06 conditional novelty 7.0 of 10

    DELTA uses an equivariant graph neural network with a probabilistic orientation output to recover injected galaxy intrinsic alignments from mock catalogs dominated by noise.

  2. Cosmological gravity on all scales V: MCMC forecasts combining large scale structure and CMB lensing for binned phenomenological modified gravity

    astro-ph.CO 2026-03 unverdicted novelty 6.0 of 10

    Emulation of binned modified gravity power spectra to <1% accuracy enables MCMC forecasts that constrain μ and η via LSST large-scale structure combined with CMB lensing, with best sensitivity along the lensing combination Σ.

Reference graph

Works this paper leans on

170 extracted references · 13 canonical work pages · cited by 2 Pith papers

  1. [117]

    Natalí S. M. de Santi, Helen Shao, Francisco Villaescusa-Navarro, L. Raul Abramo, Romain Teyssier, Pablo Villanueva-Domingo, Yueying Ni, Daniel Anglés-Alcázar, Shy Genel, Elena Hernández-Martínez, Ulrich P. Steinwandel, Christopher C. Lovell, Klaus Dolag, Tiago Castro, and Mark Vogelsberger. Robust Field-level Likelihood-free Inference with Galaxies. ApJ,...

    2023

  2. [1]

    L. E. Bleem, B. Stalder, T. de Haan, K. A. Aird, S. W. Allen, D. E. Applegate, M. L. N. Ashby, M. Bautz, M. Bayliss, B. A. Benson, S. Bocquet, M. Brodwin, J. E. Carlstrom, C. L. Chang, I. Chiu, H. M. Cho, A. Clocchiatti, T. M. Crawford, A. T. Crites, S. Desai, J. P. Dietrich, M. A. Dobbs, R. J. Foley, W. R. Forman, E. M. George, M. D. Gladders, A. H. Gonz...

    2015

  3. [2]

    G. J. Hill, K. Gebhardt, E. Komatsu, N. Drory, P. J. MacQueen, J. Adams, G. A. Blanc, R. Koehler, M. Rafal, M. M. Roth, A. Kelz, C. Gronwall, R. Ciardullo, and D. P. Schneider. The Hobby-Eberly Telescope Dark Energy Experiment (HETDEX): Description and Early Pilot Survey Results. In T. Kodama, T. Yamada, and K. Aoki, editors,Panoramic Views of – 27 – Gala...

    2008

  4. [3]

    Jelte T. A. de Jong, Gijs A. Verdoes Kleijn, Konrad H. Kuijken, and Edwin A. Valentijn. The Kilo-Degree Survey. Experimental Astronomy, 35(1-2):25–44, January 2013

    2013

  5. [4]

    Gunn, Hiroshi Karoji, Hiroaki Aihara, Hitoshi Murayama, and Masahiro Takada

    Satoshi Miyazaki, Masamune Oguri, Takashi Hamana, Masayuki Tanaka, Lance Miller, Yousuke Utsumi, Yutaka Komiyama, Hisanori Furusawa, Junya Sakurai, Satoshi Kawanomoto, Fumiaki Nakata, Fumihiro Uraguchi, Michitaro Koike, Daigo Tomono, Robert Lupton, James E. Gunn, Hiroshi Karoji, Hiroaki Aihara, Hitoshi Murayama, and Masahiro Takada. Properties of Weak Len...

    2015

  6. [5]

    Abbott, F

    Dark Energy Survey Collaboration, T. Abbott, F. B. Abdalla, J. Aleksić, S. Allam, A. Amara, D. Bacon, E. Balbinot, M. Banerji, K. Bechtol, A. Benoit-Lévy, G. M. Bernstein, E. Bertin, J. Blazek, C. Bonnett, S. Bridle, D. Brooks, R. J. Brunner, E. Buckley-Geer, D. L. Burke, G. B. Caminha, D. Capozzi, J. Carlsen, A. Carnero-Rosell, M. Carollo, M. Carrasco-Ki...

    2016

  7. [6]

    Dawson, David J

    Kyle S. Dawson, David J. Schlegel, Christopher P. Ahn, Scott F. Anderson, Éric Aubourg, Stephen Bailey, Robert H. Barkhouser, Julian E. Bautista, Alessandra Beifiori, Andreas A. Berlind, Vaishali Bhardwaj, Dmitry Bizyaev, Cullen H. Blake, Michael R. Blanton, Michael Blomqvist, Adam S. Bolton, Arnaud Borde, Jo Bovy, W. N. Brandt, Howard Brewington, Jon Bri...

    2013

  8. [7]

    Ross, Sarah Shandera, Will J

    Gong-Bo Zhao, Yuting Wang, Ashley J. Ross, Sarah Shandera, Will J. Percival, Kyle S. Dawson, Jean-Paul Kneib, Adam D. Myers, Joel R. Brownstein, Johan Comparat, Timothée Delubac, Pengyuan Gao, Alireza Hojjati, Kazuya Koyama, Cameron K. McBride, Andrés Meza, Jeffrey A. Newman, Nathalie Palanque-Delabrouille, Levon Pogosian, Francisco Prada, Graziano Rossi,...

    2016

Show all 170 references
  1. [8]

    Predehl, R

    P. Predehl, R. Andritschke, V. Arefiev, V. Babyshkin, O. Batanov, W. Becker, H. Böhringer, A. Bogomolov, T. Boller, K. Borm, W. Bornemann, H. Bräuninger, M. Brüggen, H. Brunner, M. Brusa, E. Bulbul, M. Buntov, V. Burwitz, W. Burkert, N. Clerc, E. Churazov, D. Coutinho, T. Daus...

    2021

  2. [9]

    The DESI Experiment, a whitepaper for Snowmass 2013.arXiv e-prints, page arXiv:1308.0847, August 2013

    Michael Levi, Chris Bebek, Timothy Beers, Robert Blum, Robert Cahn, Daniel Eisenstein, Brenna Flaugher, Klaus Honscheid, Richard Kron, Ofer Lahav, Patrick McDonald, Natalie Roe, David Schlegel, and representing the DESI collaboration. The DESI Experiment, a whitepaper for Snow...

    2013 arXiv

  3. [10]

    Abell, Julius Allison, Scott F

    LSST Science Collaboration, Paul A. Abell, Julius Allison, Scott F. Anderson, John R. Andrew, J. Roger P. Angel, Lee Armus, David Arnett, S. J. Asztalos, Tim S. Axelrod, Stephen Bailey, D. R. Ballantyne, Justin R. Bankert, Wayne A. Barkhouse, Jeffrey D. Barr, L. Felipe Barrien...

    2009 arXiv

  4. [11]

    Laureijs, J

    R. Laureijs, J. Amiaux, S. Arduini, J. L. Auguères, J. Brinchmann, R. Cole, M. Cropper, C. Dabin, L. Duvet, A. Ealet, B. Garilli, P. Gondoin, L. Guzzo, J. Hoar, H. Hoekstra, R. Holmes, T. Kitching, T. Maciaszek, Y. Mellier, F. Pasian, W. Percival, J. Rhodes, G. Saavedra Criado...

    arXiv

  5. [12]

    Roelof S. de Jong, Olga Bellido-Tirado, Cristina Chiappini, Éric Depagne, Roger Haynes, Diana Johl, Olivier Schnurr, Axel Schwope, Jakob Walcher, Frank Dionies, Dionne Haynes, Andreas Kelz, Francisco S. Kitaura, Georg Lamer, Ivan Minchev, Volker Müller, Sebastián. E. Nuza, Jea...

    2012

  6. [13]

    Ellis, Masashi Chiba, Jenny E

    Masahiro Takada, Richard S. Ellis, Masashi Chiba, Jenny E. Greene, Hiroaki Aihara, Nobuo Arimoto, Kevin Bundy, Judith Cohen, Olivier Doré, Genevieve Graves, James E. Gunn, Timothy Heckman, Christopher M. Hirata, Paul Ho, Jean-Paul Kneib, Olivier Le Fèvre, Lihwai Lin, Surhud Mo...

    2014

  7. [14]

    Abdalla, Matt Jarvis, and Mario G

    Roy Maartens, Filipe B. Abdalla, Matt Jarvis, and Mario G. Santos. Cosmology with the SKA – overview.arXiv e-prints, page arXiv:1501.04076, January 2015

    2015 arXiv

  8. [15]

    Spergel, N

    D. Spergel, N. Gehrels, C. Baltay, D. Bennett, J. Breckinridge, M. Donahue, A. Dressler, B. S. Gaudi, T. Greene, O. Guyon, C. Hirata, J. Kalirai, N. J. Kasdin, B. Macintosh, W. Moos, S. Perlmutter, M. Postman, B. Rauscher, J. Rhodes, Y. Wang, D. Weinberg, – 31 – D. Benford, M....

    2015 arXiv

  9. [16]

    Rudd, Andrew R

    Douglas H. Rudd, Andrew R. Zentner, and Andrey V. Kravtsov. Effects of Baryons and Dissipation on the Matter Power Spectrum. ApJ, 672(1):19–32, January 2008

    2008

  10. [17]

    Mead, Shahab Joudaki, Pedro G

    Nora Elisa Chisari, Alexander J. Mead, Shahab Joudaki, Pedro G. Ferreira, Aurel Schneider, Joseph Mohr, Tilman Tröster, David Alonso, Ian G. McCarthy, Sergio Martin-Alvarez, Julien Devriendt, Adrianne Slyz, and Marcel P. van Daalen. Modelling baryonic feedback for survey cosmo...

    2019

  11. [18]

    van Daalen, Ian G

    Marcel P. van Daalen, Ian G. McCarthy, and Joop Schaye. Exploring the effects of galaxy formation on matter clustering through a library of simulation power spectra. MNRAS, 491(2):2424–2446, January 2020

    2020

  12. [19]

    Spergel, Rachel S

    Francisco Villaescusa-Navarro, Daniel Anglés-Alcázar, Shy Genel, David N. Spergel, Rachel S. Somerville, Romeel Dave, Annalisa Pillepich, Lars Hernquist, Dylan Nelson, Paul Torrey, Desika Narayanan, Yin Li, Oliver Philcox, Valentina La Torre, Ana Maria Delgado, Shirley Ho, Sul...

    2021

  13. [20]

    Bryan, Romeel Dave, Lars Hernquist, and Mark Vogelsberger

    Emily Moser, Nicholas Battaglia, Daisuke Nagai, Erwin Lau, Luis Fernando Machado Poletti Valle, Francisco Villaescusa-Navarro, Stefania Amodeo, Daniel Anglés-Alcázar, Greg L. Bryan, Romeel Dave, Lars Hernquist, and Mark Vogelsberger. The Circumgalactic Medium from the CAMELS S...

    2022

  14. [21]

    Colin Hill, Shivam Pandey, Francisco Villaescusa-Navarro, David N

    Digvijay Wadekar, Leander Thiele, J. Colin Hill, Shivam Pandey, Francisco Villaescusa-Navarro, David N. Spergel, Miles Cranmer, Daisuke Nagai, Daniel Anglés-Alcázar, Shirley Ho, and Lars Hernquist. The SZ flux-mass (Y-M) relation at low-halo masses: improvements with symbolic ...

    2023

  15. [22]

    Baxter, Yueying Ni, Daniel Anglés-Alcázar, Shy Genel, Francisco Villaescusa-Navarro, Ana Maria Delgado, and Tiziana di Matteo

    Shivam Pandey, Kai Lehman, Eric J. Baxter, Yueying Ni, Daniel Anglés-Alcázar, Shy Genel, Francisco Villaescusa-Navarro, Ana Maria Delgado, and Tiziana di Matteo. Inferring the impact of feedback on the matter distribution using the Sunyaev Zel’dovich effect: insights from CAME...

    2023

  16. [23]

    Somerville, Michelle Ntampaka, Shy Genel, Francisco Villaescusa-Navarro, and Lars Hernquist

    Ana Maria Delgado, Daniel Anglés-Alcázar, Leander Thiele, Shivam Pandey, Kai Lehman, Rachel S. Somerville, Michelle Ntampaka, Shy Genel, Francisco Villaescusa-Navarro, and Lars Hernquist. Predicting the impact of feedback on matter clustering with machine learning in CAMELS. M...

    2023

  17. [24]

    Weinberg

    Chun-Hao To, Shivam Pandey, Elisabeth Krause, Nihar Dalal, Dhayaa Anbajagane, and David H. Weinberg. Deciphering baryonic feedback with galaxy clusters.arXiv e-prints, page arXiv:2402.00110, January 2024

    2024 arXiv

  18. [25]

    Bigwood, A

    L. Bigwood, A. Amon, A. Schneider, J. Salcido, I. G. McCarthy, C. Preston, D. Sanchez, D. Sijacki, E. Schaan, S. Ferraro, N. Battaglia, A. Chen, S. Dodelson, A. Roodman, A. Pieres, A. Ferte, A. Alarcon, A. Drlica-Wagner, A. Choi, A. Navarro-Alsina, A. Campos, A. J. Ross, A. Ca...

    2024 arXiv

  19. [26]

    McCarthy, Alexandra Amon, Joop Schaye, Emmanuel Schaan, Raul E

    Ian G. McCarthy, Alexandra Amon, Joop Schaye, Emmanuel Schaan, Raul E. Angulo, Jaime Salcido, Matthieu Schaller, Leah Bigwood, Willem Elbers, Roi Kugel, John C. Helly, Victor J. Forouhar Moreno, Carlos S. Frenk, Robert J. McGibbon, Lurdes Ondaro-Mallea, and Marcel P. van Daale...

    arXiv

  20. [27]

    Y. E. Bahar, E. Bulbul, V. Ghirardini, J. S. Sanders, X. Zhang, A. Liu, N. Clerc, E. Artis, F. Balzer, V. Biffi, S. Bose, J. Comparat, K. Dolag, C. Garrel, B. Hadzhiyska, C. Hernández-Aguayo, L. Hernquist, M. Kluge, S. Krippendorf, A. Merloni, K. Nandra, R. Pakmor, P. Popesso,...

    2024 arXiv

  21. [28]

    Galaxy dispersion measured by Fast Radio Bursts as a probe of baryonic feedback models.arXiv e-prints, page arXiv:2403.08611, March 2024

    Alexander Theis, Steffen Hagstotz, Robert Reischke, and Jochen Weller. Galaxy dispersion measured by Fast Radio Bursts as a probe of baryonic feedback models.arXiv e-prints, page arXiv:2403.08611, March 2024

    2024 arXiv

  22. [29]

    Probing the Circum-Galactic Medium with Fast Radio Bursts: Insights from the CAMELS Simulations.arXiv e-prints, page arXiv:2403.02313, March 2024

    Isabel Medlock, Daisuke Nagai, Priyanka Singh, Benjamin Oppenheimer, Daniel Anglés Alcázar, and Francisco Villaescusa-Navarro. Probing the Circum-Galactic Medium with Fast Radio Bursts: Insights from the CAMELS Simulations.arXiv e-prints, page arXiv:2403.02313, March 2024

    2024 arXiv

  23. [30]

    Position-dependent power spectrum of the large-scale structure: a novel method to measure the squeezed-limit bispectrum

    Chi-Ting Chiang, Christian Wagner, Fabian Schmidt, and Eiichiro Komatsu. Position-dependent power spectrum of the large-scale structure: a novel method to measure the squeezed-limit bispectrum. J. Cosmology Astropart. Phys., 2014(5):048, May 2014

    2014

  24. [31]

    Sánchez, Fabian Schmidt, and Eiichiro Komatsu

    Chi-Ting Chiang, Christian Wagner, Ariel G. Sánchez, Fabian Schmidt, and Eiichiro Komatsu. Position-dependent correlation function from the SDSS-III Baryon Oscillation Spectroscopic Survey Data Release 10 CMASS sample. J. Cosmology Astropart. Phys., 2015(9):028–028, September 2015

    2015

  25. [32]

    Anik Halder, Oliver Friedrich, Stella Seitz, and Tamas N. Varga. The integrated three-point correlation function of cosmic shear. MNRAS, 506(2):2780–2803, September 2021

    2021

  26. [33]

    Beyond 3×2-point cosmology: the integrated shear and galaxy 3-point correlation functions

    Anik Halder, Zhengyangguang Gong, Alexandre Barreira, Oliver Friedrich, Stella Seitz, and Daniel Gruen. Beyond 3×2-point cosmology: the integrated shear and galaxy 3-point correlation functions. J. Cosmology Astropart. Phys., 2023(10):028, October 2023

    2023

  27. [34]

    Friedrich, D

    O. Friedrich, D. Gruen, J. DeRose, D. Kirk, E. Krause, T. McClintock, E. S. Rykoff, S. Seitz, R. H. Wechsler, G. M. Bernstein, J. Blazek, C. Chang, S. Hilbert, B. Jain, A. Kovacs, O. Lahav, F. B. Abdalla, S. Allam, J. Annis, K. Bechtol, A. Benoit-Lévy, E. Bertin, D. Brooks, A....

    2018

  28. [35]

    Gruen, O

    D. Gruen, O. Friedrich, E. Krause, J. DeRose, R. Cawthon, C. Davis, J. Elvin-Poole, E. S. Rykoff, R. H. Wechsler, A. Alarcon, G. M. Bernstein, J. Blazek, C. Chang, J. Clampitt, M. Crocce, J. De Vicente, M. Gatti, M. S. S. Gill, W. G. Hartley, S. Hilbert, B. Hoyle, B. Jain, M. ...

    2018

  29. [36]

    How much information can be extracted from galaxy clustering at the field level?arXiv e-prints, page arXiv:2403.03220, March 2024

    Nhat-Minh Nguyen, Fabian Schmidt, Beatriz Tucci, Martin Reinecke, and Andrija Kostić. How much information can be extracted from galaxy clustering at the field level?arXiv e-prints, page arXiv:2403.03220, March 2024

    2024 arXiv

  30. [37]

    Fast likelihood-free cosmology with neural density estimators and active learning

    Justin Alsing, Tom Charnock, Stephen Feeney, and Benjamin Wandelt. Fast likelihood-free cosmology with neural density estimators and active learning. MNRAS, 488(3):4440–4458, September 2019

    2019

  31. [38]

    The frontier of simulation-based inference

    Kyle Cranmer, Johann Brehmer, and Gilles Louppe. The frontier of simulation-based inference. Proceedings of the National Academy of Science, 117(48):30055–30062, December 2020

    2020

  32. [39]

    Masked Autoregressive Flow for Density Estimation

    George Papamakarios, Theo Pavlakou, and Iain Murray. Masked Autoregressive Flow for Density Estimation. arXiv e-prints, page arXiv:1705.07057, May 2017

    2017 arXiv

  33. [40]

    Donald B. Rubin. Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician. The Annals of Statistics, 12(4):1151 – 1172, 1984

    1984

  34. [41]

    Approximate bayesian computation in population genetics.Genetics, 162(4):2025–2035, December 2002

    Mark A Beaumont, Wenyang Zhang, and David J Balding. Approximate bayesian computation in population genetics.Genetics, 162(4):2025–2035, December 2002

    2025

  35. [42]

    Approximate Bayesian computation for forward modeling in cosmology

    Joël Akeret, Alexandre Refregier, Adam Amara, Sebastian Seehars, and Caspar Hasner. Approximate Bayesian computation for forward modeling in cosmology. J. Cosmology Astropart. Phys., 2015(8):043–043, August 2015

    2015

  36. [43]

    Normalizing Flows for Probabilistic Modeling and Inference.arXiv e-prints, page arXiv:1912.02762, December 2019

    George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan. Normalizing Flows for Probabilistic Modeling and Inference.arXiv e-prints, page arXiv:1912.02762, December 2019

    1912 arXiv

  37. [44]

    Simulation-Based Inference of Strong Gravitational Lensing Parameters

    Ronan Legin, Yashar Hezaveh, Laurence Perreault Levasseur, and Benjamin Wandelt. Simulation-Based Inference of Strong Gravitational Lensing Parameters. InMachine Learning and the Physical Sciences Workshop, page 95, January 2021. – 34 –

    2021

  38. [45]

    Detection is truncation: studying source populations with truncated marginal neural ratio estimation.arXiv e-prints, page arXiv:2211.04291, November 2022

    Noemi Anau Montel and Christoph Weniger. Detection is truncation: studying source populations with truncated marginal neural ratio estimation.arXiv e-prints, page arXiv:2211.04291, November 2022

    2022 arXiv

  39. [46]

    Accelerated Bayesian SED Modeling Using Amortized Neural Posterior Estimation

    ChangHoon Hahn and Peter Melchior. Accelerated Bayesian SED Modeling Using Amortized Neural Posterior Estimation. ApJ, 938(1):11, October 2022

    2022

  40. [47]

    DIGS: deep inference of galaxy spectra with neural posterior estimation.Machine Learning: Science and Technology, 3(4):04LT04, December 2022

    Gourav Khullar, Brian Nord, Aleksandra Ćiprijanović, Jason Poh, and Fei Xu. DIGS: deep inference of galaxy spectra with neural posterior estimation.Machine Learning: Science and Technology, 3(4):04LT04, December 2022

    2022

  41. [48]

    What to do when things get crowded? Scalable joint analysis of overlapping gravitational wave signals

    James Alvey, Uddipta Bhardwaj, Samaya Nissanke, and Christoph Weniger. What to do when things get crowded? Scalable joint analysis of overlapping gravitational wave signals. arXiv e-prints, page arXiv:2308.06318, August 2023

    2023 arXiv

  42. [49]

    Albatross: a scalable simulation-based inference pipeline for analysing stellar streams in the Milky Way

    James Alvey, Mathis Gerdes, and Christoph Weniger. Albatross: a scalable simulation-based inference pipeline for analysing stellar streams in the Milky Way. MNRAS, 525(3):3662–3681, November 2023

    2023

  43. [50]

    Sequential simulation-based inference for gravitational wave signals

    Uddipta Bhardwaj, James Alvey, Benjamin Kurt Miller, Samaya Nissanke, and Christoph Weniger. Sequential simulation-based inference for gravitational wave signals. Phys. Rev. D, 108(4):042004, August 2023

    2023

  44. [51]

    Neural posterior estimation with guaranteed exact coverage: The ringdown of GW150914

    Marco Crisostomi, Kallol Dey, Enrico Barausse, and Roberto Trotta. Neural posterior estimation with guaranteed exact coverage: The ringdown of GW150914. Phys. Rev. D, 108(4):044029, August 2023

    2023

  45. [52]

    Darc, Clecio R

    P. Darc, Clecio R. Bom, Bernardo M. O. Fraga, and Charlie D. Kilpatrick. Kilonova Spectral Inverse Modelling with Simulation-Based Inference: An Amortized Neural Posterior Estimation Analysis. arXiv e-prints, page arXiv:2311.09471, November 2023

    2023 arXiv

  46. [53]

    Green, Jonathan Gair, Michael Pürrer, Jonas Wildberger, Jakob H

    Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, and Bernhard Schölkopf. Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference. Phys. Rev. Lett., 130(17):171403, April 2023

    2023

  47. [54]

    Dupourqué, N

    S. Dupourqué, N. Clerc, E. Pointecouteau, D. Eckert, S. Ettori, and F. Vazza. Investigating the turbulent hot gas in X-COP galaxy clusters. A&A, 673:A91, May 2023

    2023

  48. [55]

    Gebhard, Jonas Wildberger, Maximilian Dax, Daniel Angerhausen, Sascha P

    Timothy D. Gebhard, Jonas Wildberger, Maximilian Dax, Daniel Angerhausen, Sascha P. Quanz, and Bernhard Schölkopf. Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling.arXiv e-prints, page arXiv:2312.08295, December 2023

    2023 arXiv

  49. [56]

    Isolated pulsar population synthesis with simulation-based inference.arXiv e-prints, page arXiv:2312.14848, December 2023

    Vanessa Graber, Michele Ronchi, Celsa Pardo-Araujo, and Nanda Rea. Isolated pulsar population synthesis with simulation-based inference.arXiv e-prints, page arXiv:2312.14848, December 2023

    2023 arXiv

  50. [57]

    ChangHoon Hahn, Connor Bottrell, and Khee-Gan Lee.HALOFLOW I: Neural Inference of Halo Mass from Galaxy Photometry and Morphology.arXiv e-prints, page arXiv:2310.04503, October 2023

    2023 arXiv

  51. [58]

    Exploring the likelihood of the 21-cm power spectrum with simulation-based inference

    David Prelogović and Andrei Mesinger. Exploring the likelihood of the 21-cm power spectrum with simulation-based inference. MNRAS, 524(3):4239–4255, September 2023

    2023

  52. [59]

    Neural posterior estimation for exoplanetary atmospheric retrieval

    Malavika Vasist, François Rozet, Olivier Absil, Paul Mollière, Evert Nasedkin, and Gilles Louppe. Neural posterior estimation for exoplanetary atmospheric retrieval. A&A, 672:A147, April 2023

    2023

  53. [60]

    Ashley Villar, and Joshua S

    Bingjie Wang, Joel Leja, V. Ashley Villar, and Joshua S. Speagle. SBI++: Flexible, Ultra-fast Likelihood-free Inference Customized for Astronomical Applications. ApJ, 952(1):L10, July 2023. – 35 –

    2023

  54. [61]

    Xiaosheng Zhao, Yi Mao, Shifan Zuo, and Benjamin D. Wandelt. Simulation-based Inference of Reionization Parameters from 3D Tomographic 21 cm Light-cone Images – II: Application of Solid Harmonic Wavelet Scattering Transform.arXiv e-prints, page arXiv:2310.17602, October 2023

    arXiv

  55. [62]

    Simulation-based inference for stochastic gravitational wave background data analysis

    James Alvey, Uddipta Bhardwaj, Valerie Domcke, Mauro Pieroni, and Christoph Weniger. Simulation-based inference for stochastic gravitational wave background data analysis. Phys. Rev. D, 109(8):083008, April 2024

    2024

  56. [63]

    Baxter, and Jason Kumar

    Katharena Christy, Eric J. Baxter, and Jason Kumar. Applying Simulation-Based Inference to Spectral and Spatial Information from the Galactic Center Gamma-Ray Excess.arXiv e-prints, page arXiv:2402.04549, February 2024

    2024 arXiv

  57. [64]

    Grootes, Francesco Nattino, and Christoph Weniger

    Adam Coogan, Noemi Anau Montel, Konstantin Karchev, Meiert W. Grootes, Francesco Nattino, and Christoph Weniger. The effect of the perturber population on subhalo measurements in strong gravitational lenses. MNRAS, 527(1):66–78, January 2024

    2024

  58. [65]

    Simulation-based inference of deep fields: galaxy population model and redshift distributions

    Beatrice Moser, Tomasz Kacprzak, Silvan Fischbacher, Alexandre Refregier, Dominic Grimm, and Luca Tortorelli. Simulation-based inference of deep fields: galaxy population model and redshift distributions. arXiv e-prints, page arXiv:2401.06846, January 2024

    2024 arXiv

  59. [66]

    Tian-Yang Sun, Chun-Yu Xiong, Shang-Jie Jin, Yu-Xin Wang, Jing-Fei Zhang, and Xin Zhang. Efficient parameter inference for gravitational wave signals in the presence of transient noises using temporal and time-spectral fusion normalizing flow.Chinese Physics C, 48(4):045108, A...

    2024

  60. [67]

    Robust inference of gravitational wave source parameters in the presence of noise transients using normalizing flows

    Chun-Yu Xiong, Tian-Yang Sun, Jing-Fei Zhang, and Xin Zhang. Robust inference of gravitational wave source parameters in the presence of noise transients using normalizing flows. arXiv e-prints, page arXiv:2405.09475, May 2024

    2024 arXiv

  61. [68]

    Androniki Dimitriou, Christoph Weniger, and Camila A. Correa. Towards reconstructing the halo clustering and halo mass function of N-body simulations using neural ratio estimation. arXiv e-prints, page arXiv:2206.11312, June 2022

    2022 arXiv

  62. [69]

    Abidi.SIMBIG: A Forward Modeling Approach To Analyzing Galaxy Clustering

    ChangHoon Hahn, Michael Eickenberg, Shirley Ho, Jiamin Hou, Pablo Lemos, Elena Massara, Chirag Modi, Azadeh Moradinezhad Dizgah, Bruno Régaldo-Saint Blancard, and Muntazir M. Abidi.SIMBIG: A Forward Modeling Approach To Analyzing Galaxy Clustering. arXiv e-prints, page arXiv:2...

    2022 arXiv

  63. [70]

    Estimating Cosmological Constraints from Galaxy Cluster Abundance using Simulation-Based Inference

    Moonzarin Reza, Yuanyuan Zhang, Brian Nord, Jason Poh, Aleksandra Ciprijanovic, and Louis Strigari. Estimating Cosmological Constraints from Galaxy Cluster Abundance using Simulation-Based Inference. InMachine Learning for Astrophysics, page 20, July 2022

    2022

  64. [71]

    Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation

    Gemma Zhang, Siddharth Mishra-Sharma, and Cora Dvorkin. Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation. MNRAS, 517(3):4317–4326, December 2022

    2022

  65. [72]

    Xiaosheng Zhao, Yi Mao, and Benjamin D. Wandelt. Implicit Likelihood Inference of Reionization Parameters from the 21 cm Power Spectrum. ApJ, 933(2):236, July 2022

    2022

  66. [73]

    Estimating the warm dark matter mass from strong lensing images with truncated marginal neural ratio estimation

    Noemi Anau Montel, Adam Coogan, Camila Correa, Konstantin Karchev, and Christoph Weniger. Estimating the warm dark matter mass from strong lensing images with truncated marginal neural ratio estimation. MNRAS, 518(2):2746–2760, January 2023

    2023

  67. [74]

    ChangHoon Hahn, Pablo Lemos, Liam Parker, Bruno Régaldo-Saint Blancard, Michael Eickenberg, Shirley Ho, Jiamin Hou, Elena Massara, Chirag Modi, Azadeh Moradinezhad Dizgah, and David Spergel.SIMBIG: The First Cosmological Constraints from Non-Gaussian and Non-Linear Galaxy Clus...

    arXiv

  68. [75]

    SIMBIG: The First Cosmological Constraints from the Non-Linear Galaxy Bispectrum

    ChangHoon Hahn, Michael Eickenberg, Shirley Ho, Jiamin Hou, Pablo Lemos, Elena Massara, Chirag Modi, Azadeh Moradinezhad Dizgah, Liam Parker, and Bruno Régaldo-Saint – 36 – Blancard. SIMBIG: The First Cosmological Constraints from the Non-Linear Galaxy Bispectrum. arXiv e-prin...

    arXiv

  69. [76]

    SIMBIG: Galaxy Clustering Analysis with the Wavelet Scattering Transform

    Bruno Régaldo-Saint Blancard, ChangHoon Hahn, Shirley Ho, Jiamin Hou, Pablo Lemos, Elena Massara, Chirag Modi, Azadeh Moradinezhad Dizgah, Liam Parker, Yuling Yao, and Michael Eickenberg. SIMBIG: Galaxy Clustering Analysis with the Wavelet Scattering Transform. arXiv e-prints,...

    arXiv

  70. [77]

    Debiasing standard siren inference of the Hubble constant with marginal neural ratio estimation

    Samuel Gagnon-Hartman, John Ruan, and Daryl Haggard. Debiasing standard siren inference of the Hubble constant with marginal neural ratio estimation. MNRAS, 520(1):1–13, March 2023

    2023

  71. [79]

    SICRET: Supernova Ia Cosmology with truncated marginal neural Ratio EsTimation

    Konstantin Karchev, Roberto Trotta, and Christoph Weniger. SICRET: Supernova Ia Cosmology with truncated marginal neural Ratio EsTimation. MNRAS, 520(1):1056–1072, March 2023

    2023

  72. [80]

    Parker, ChangHoon Hahn, Shirley Ho, Michael Eickenberg, Jiamin Hou, Elena Massara, Chirag Modi, Azadeh Moradinezhad Dizgah, Bruno Régaldo-Saint Blancard, and David Spergel

    Pablo Lemos, Liam H. Parker, ChangHoon Hahn, Shirley Ho, Michael Eickenberg, Jiamin Hou, Elena Massara, Chirag Modi, Azadeh Moradinezhad Dizgah, Bruno Régaldo-Saint Blancard, and David Spergel. SimBIG: Field-level Simulation-based Inference of Large-scale Structure. In Machine...

    2023

  73. [81]

    Bayesian Simulation-based Inference for Cosmological Initial Conditions.arXiv e-prints, page arXiv:2310.19910, October 2023

    Florian List, Noemi Anau Montel, and Christoph Weniger. Bayesian Simulation-based Inference for Cosmological Initial Conditions.arXiv e-prints, page arXiv:2310.19910, October 2023

    2023 arXiv

  74. [82]

    Sensitivity Analysis of Simulation-Based Inference for Galaxy Clustering

    Chirag Modi, Shivam Pandey, Matthew Ho, ChangHoon Hahn, Bruno R’egaldo-Saint Blancard, and Benjamin Wandelt. Sensitivity Analysis of Simulation-Based Inference for Galaxy Clustering. arXiv e-prints, page arXiv:2309.15071, September 2023

    2023 arXiv

  75. [83]

    Chirag Modi and Oliver H. E. Philcox. Hybrid SBI or How I Learned to Stop Worrying and Learn the Likelihood.arXiv e-prints, page arXiv:2309.10270, September 2023

    2023 arXiv

  76. [84]

    Daniel Meerburg, Christoph Weniger, and Samuel J

    Anchal Saxena, Alex Cole, Simon Gazagnes, P. Daniel Meerburg, Christoph Weniger, and Samuel J. Witte. Constraining the X-ray heating and reionization using 21-cm power spectra with Marginal Neural Ratio Estimation. MNRAS, 525(4):6097–6111, November 2023

    2023

  77. [85]

    Renzini, and Mairi Sakellariadou

    Yanyan Zheng, Nikolaos Kouvatsos, Jacob Golomb, Marco Cavaglià, Arianna I. Renzini, and Mairi Sakellariadou. Angular Power Spectrum of Gravitational-Wave Transient Sources as a Probe of the Large-Scale Structure. Phys. Rev. Lett., 131(17):171403, October 2023

    2023

  78. [86]

    Baxter and Shivam Pandey

    Eric J. Baxter and Shivam Pandey. Inferring galaxy cluster masses from cosmic microwave background lensing with neural simulation based inference.arXiv e-prints, page arXiv:2401.08910, January 2024

    2024 arXiv

  79. [87]

    SIMBIG: Cosmological Constraints from the Redshift-Space Galaxy Skew Spectra

    Jiamin Hou, Azadeh Moradinezhad Dizgah, ChangHoon Hahn, Michael Eickenberg, Shirley Ho, Pablo Lemos, Elena Massara, Chirag Modi, Liam Parker, and Bruno Régaldo-Saint Blancard. SIMBIG: Cosmological Constraints from the Redshift-Space Galaxy Skew Spectra. arXiv e-prints, page ar...

    2024 arXiv

  80. [88]

    SimBIG: Cosmological Constraints using Simulation-Based Inference of Galaxy Clustering with Marked Power Spectra.arXiv e-prints, page arXiv:2404.04228, April 2024

    Elena Massara, ChangHoon Hahn, Michael Eickenberg, Shirley Ho, Jiamin Hou, Pablo Lemos, Chirag Modi, Azadeh Moradinezhad Dizgah, Liam Parker, and Bruno Régaldo-Saint Blancard. SimBIG: Cosmological Constraints using Simulation-Based Inference of Galaxy Clustering with Marked Po...

    2024 arXiv

  81. [89]

    EFTofLSS meets simulation-based inference:σ8 from biased tracers

    Beatriz Tucci and Fabian Schmidt. EFTofLSS meets simulation-based inference:σ8 from biased tracers. arXiv e-prints, page arXiv:2310.03741, October 2023. – 37 –

    2023 arXiv

  82. [90]

    Somerville, Francisco Villaescusa-Navarro, Greg L

    Yongseok Jo, Shy Genel, Benjamin Wandelt, Rachel S. Somerville, Francisco Villaescusa-Navarro, Greg L. Bryan, Daniel Anglés-Alcázar, Daniel Foreman-Mackey, Dylan Nelson, and Ji-hoon Kim. Calibrating Cosmological Simulations with Implicit Likelihood Inference Using Galaxy Growt...

    2023

  83. [91]

    Davies, Sarah E

    Frederick B. Davies, Sarah E. I. Bosman, Prakash Gaikwad, Fahad Nasir, Joseph F. Hennawi, George D. Becker, Martin G. Haehnelt, Valentina D’Odorico, Manuela Bischetti, Anna-Christina Eilers, Laura C. Keating, Girish Kulkarni, Samuel Lai, Chiara Mazzucchelli, Yuxiang Qin, Sindh...

    2024

  84. [92]

    Gatti, N

    M. Gatti, N. Jeffrey, L. Whiteway, J. Williamson, B. Jain, V. Ajani, D. Anbajagane, G. Giannini, C. Zhou, A. Porredon, J. Prat, M. Yamamoto, J. Blazek, T. Kacprzak, S. Samuroff, A. Alarcon, A. Amon, K. Bechtol, M. Becker, G. Bernstein, A. Campos, C. Chang, R. Chen, A. Choi, C....

    2024

  85. [93]

    Gatti, G

    M. Gatti, G. Campailla, N. Jeffrey, L. Whiteway, A. Porredon, J. Prat, J. Williamson, M. Raveri, B. Jain, V. Ajani, G. Giannini, M. Yamamoto, C. Zhou, J. Blazek, D. Anbajagane, S. Samuroff, T. Kacprzak, A. Alarcon, A. Amon, K. Bechtol, M. Becker, G. Bernstein, A. Campos, C. Ch...

    2024 arXiv

  86. [94]

    Jeffrey, L

    N. Jeffrey, L. Whiteway, M. Gatti, J. Williamson, J. Alsing, A. Porredon, J. Prat, C. Doux, B. Jain, C. Chang, T. Y. Cheng, T. Kacprzak, P. Lemos, A. Alarcon, A. Amon, K. Bechtol, M. R. Becker, G. M. Bernstein, A. Campos, A. Carnero Rosell, R. Chen, A. Choi, J. DeRose, A. Drli...

    2024 arXiv

  87. [95]

    Optimal, fast, and robust inference of reionization-era cosmology with the 21cmPIE-INN.arXiv e-prints, page arXiv:2401.04174, January 2024

    Benedikt Schosser, Caroline Heneka, and Tilman Plehn. Optimal, fast, and robust inference of reionization-era cosmology with the 21cmPIE-INN.arXiv e-prints, page arXiv:2401.04174, January 2024

    2024 arXiv

  88. [96]

    Maximilian von Wietersheim-Kramsta, Kiyam Lin, Nicolas Tessore, Benjamin Joachimi, Arthur Loureiro, Robert Reischke, and Angus H. Wright. KiDS-SBI: Simulation-Based Inference Analysis of KiDS-1000 Cosmic Shear.arXiv e-prints, page arXiv:2404.15402, April 2024

    2024 arXiv

  89. [97]

    M. G. B. Blum, M. A. Nunes, D. Prangle, and S. A. Sisson. A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation.arXiv e-prints, page arXiv:1202.3819, February 2012

    2012 arXiv

  90. [98]

    M. G. B. Blum and O. Francois. Non-linear regression models for Approximate Bayesian Computation. arXiv e-prints, page arXiv:0809.4178, September 2008

    2008 arXiv

  91. [99]

    Constructing Summary Statistics for Approximate Bayesian Computation: Semi-automatic ABC.arXiv e-prints, page arXiv:1004.1112, April 2010

    Paul Fearnhead and Dennis Prangle. Constructing Summary Statistics for Approximate Bayesian Computation: Semi-automatic ABC.arXiv e-prints, page arXiv:1004.1112, April 2010

    2010 arXiv

  92. [100]

    Bai Jiang, Tung-yu Wu, Charles Zheng, and Wing H. Wong. Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network.arXiv e-prints, page arXiv:1510.02175, October 2015

    2015 arXiv

  93. [101]

    Murphy.Probabilistic Machine Learning: An introduction

    Kevin P. Murphy.Probabilistic Machine Learning: An introduction. MIT Press, 2022

    2022

  94. [102]

    Tom Charnock, Guilhem Lavaux, and Benjamin D. Wandelt. Automatic physical inference with information maximizing neural networks. Phys. Rev. D, 97(8):083004, April 2018

    2018

  95. [103]

    Heavens, Raul Jimenez, and Ofer Lahav

    Alan F. Heavens, Raul Jimenez, and Ofer Lahav. Massive lossless data compression and multiple parameter estimation from galaxy spectra. MNRAS, 317(4):965–972, October 2000

    2000

  96. [104]

    How informative are summaries of the cosmic 21-cm signal? arXiv e-prints, page arXiv:2401.12277, January 2024

    David Prelogović and Andrei Mesinger. How informative are summaries of the cosmic 21-cm signal? arXiv e-prints, page arXiv:2401.12277, January 2024

    2024 arXiv

  97. [105]

    Lucas Makinen, Alan Heavens, Natalia Porqueres, Tom Charnock, Axel Lapel, and Benjamin D

    T. Lucas Makinen, Alan Heavens, Natalia Porqueres, Tom Charnock, Axel Lapel, and Benjamin D. Wandelt. Hybrid summary statistics: neural weak lensing inference beyond the power spectrum. arXiv e-prints, page arXiv:2407.18909, July 2024

    2024 arXiv

  98. [106]

    Likelihood-free inference with neural compression of DES SV weak lensing map statistics

    Niall Jeffrey, Justin Alsing, and François Lanusse. Likelihood-free inference with neural compression of DES SV weak lensing map statistics. MNRAS, 501(1):954–969, February 2021

    2021

  99. [107]

    Radev, Ulf K

    Stefan T. Radev, Ulf K. Mertens, Andreas Voss, Lynton Ardizzone, and Ullrich Köthe. BayesFlow: Learning complex stochastic models with invertible neural networks.arXiv e-prints, page arXiv:2003.06281, March 2020

    2003 arXiv

  100. [108]

    A comparative study of cosmological constraints from weak lensing using Convolutional Neural Networks.arXiv e-prints, page arXiv:2403.03490, March 2024

    Divij Sharma, Biwei Dai, and Uros Seljak. A comparative study of cosmological constraints from weak lensing using Convolutional Neural Networks.arXiv e-prints, page arXiv:2403.03490, March 2024. – 39 –

    2024 arXiv

  101. [109]

    Lucas Makinen, Alexandre Boucaud, Jean-Luc Starck, and François Lanusse

    Denise Lanzieri, Justine Zeghal, T. Lucas Makinen, Alexandre Boucaud, Jean-Luc Starck, and François Lanusse. Optimal Neural Summarisation for Full-Field Weak Lensing Cosmological Implicit Inference. arXiv e-prints, page arXiv:2407.10877, July 2024

    2024 arXiv

  102. [110]

    Lucas Makinen, Ce Sui, Benjamin D

    T. Lucas Makinen, Ce Sui, Benjamin D. Wandelt, Natalia Porqueres, and Alan Heavens. Hybrid Summary Statistics.arXiv e-prints, page arXiv:2410.07548, October 2024

    2024

  103. [111]

    Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković

    Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković. Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.arXiv e-prints, page arXiv:2104.13478, April 2021

    2021 arXiv

  104. [112]

    Battaglia, Jessica B

    Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey ...

    2018 arXiv

  105. [113]

    Cosmology from Galaxy Redshift Surveys with PointNet

    Sotiris Anagnostidis, Arne Thomsen, Tomasz Kacprzak, Tilman Tröster, Luca Biggio, Alexandre Refregier, and Thomas Hofmann. Cosmology from Galaxy Redshift Surveys with PointNet. arXiv e-prints, page arXiv:2211.12346, November 2022

    2022 arXiv

  106. [114]

    Learning Cosmology and Clustering with Cosmic Graphs

    Pablo Villanueva-Domingo and Francisco Villaescusa-Navarro. Learning Cosmology and Clustering with Cosmic Graphs. ApJ, 937(2):115, October 2022

    2022

  107. [115]

    Garrison, Marco Gatti, Derek Inman, Yueying Ni, Ulrich P

    Helen Shao, Francisco Villaescusa-Navarro, Pablo Villanueva-Domingo, Romain Teyssier, Lehman H. Garrison, Marco Gatti, Derek Inman, Yueying Ni, Ulrich P. Steinwandel, Mihir Kulkarni, Eli Visbal, Greg L. Bryan, Daniel Angles-Alcazar, Tiago Castro, Elena Hernandez-Martinez, and ...

    2022 arXiv

  108. [116]

    Lucas Makinen, Tom Charnock, Pablo Lemos, Natalia Porqueres, Alan F

    T. Lucas Makinen, Tom Charnock, Pablo Lemos, Natalia Porqueres, Alan F. Heavens, and Benjamin D. Wandelt. The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues.The Open Journal of Astrophysics, 5(1):18, December 2022

    2022

  109. [118]

    Natalí S. M. de Santi, Francisco Villaescusa-Navarro, L. Raul Abramo, Helen Shao, Lucia A. Perez, Tiago Castro, Yueying Ni, Christopher C. Lovell, Elena Hernandez-Martinez, Federico Marinacci, David N. Spergel, Klaus Dolag, Lars Hernquist, and Mark Vogelsberger. Field-level si...

    arXiv

  110. [119]

    Percival

    Elena Massara, Francisco Villaescusa-Navarro, and Will J. Percival. Predicting interloper fraction with graph neural networks. J. Cosmology Astropart. Phys., 2023(12):012, December 2023

    2023

  111. [120]

    Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets.arXiv e-prints, page arXiv:2311.01588, November 2023

    Andrea Roncoli, Aleksandra Ćiprijanović, Maggie Voetberg, Francisco Villaescusa-Navarro, and Brian Nord. Domain Adaptive Graph Neural Networks for Constraining Cosmological Parameters Across Multiple Data Sets.arXiv e-prints, page arXiv:2311.01588, November 2023

    2023 arXiv

  112. [121]

    Helen Shao, Natalí S. M. de Santi, Francisco Villaescusa-Navarro, Romain Teyssier, Yueying Ni, Daniel Anglés-Alcázar, Shy Genel, Ulrich P. Steinwandel, Elena Hernández-Martínez, Klaus Dolag, Christopher C. Lovell, Lehman H. Garrison, Eli Visbal, Mihir Kulkarni, Lars – 40 – Her...

    2023

  113. [122]

    Niall Jeffrey and Benjamin D. Wandelt. Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks.arXiv e-prints, page arXiv:2011.05991, November 2020

    2011 arXiv

  114. [123]

    Uncovering dark matter density profiles in dwarf galaxies with graph neural networks

    Tri Nguyen, Siddharth Mishra-Sharma, Reuel Williams, and Lina Necib. Uncovering dark matter density profiles in dwarf galaxies with graph neural networks. Phys. Rev. D, 107(4):043015, February 2023

    2023

  115. [124]

    Bartlett, Nicolas Chartier, Carolina Cuesta-Lazaro, Simon Ding, Axel Lapel, Pablo Lemos, Christopher C

    Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier, Carolina Cuesta-Lazaro, Simon Ding, Axel Lapel, Pablo Lemos, Christopher C. Lovell, T. Lucas Makinen, Chirag Modi, Viraj Pandya, Shivam Pandey, Lucia A. Perez, Benjamin Wandelt, and Greg L. Bryan. LtU-ILI: An All-in-One Framew...

    2024 arXiv

  116. [125]

    Peiris, Andrew Pontzen, Brian Nord, Jeyan Thiyagalingam, and Davide Piras

    Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen, Brian Nord, Jeyan Thiyagalingam, and Davide Piras. Discovering the building blocks of dark matter halo density profiles with neural networks. Phys. Rev. D, 105(10):103533, May 2022

    2022

  117. [126]

    C3NN: Cosmological Correlator Convolutional Neural Network an Interpretable Machine-learning Framework for Cosmological Analyses

    Zhengyangguang Gong, Anik Halder, Annabelle Bohrdt, Stella Seitz, and David Gebauer. C3NN: Cosmological Correlator Convolutional Neural Network an Interpretable Machine-learning Framework for Cosmological Analyses. ApJ, 971(2):156, August 2024

    2024

  118. [127]

    A deep-learning model for the density profiles of subhaloes in IllustrisTNG

    Luisa Lucie-Smith, Giulia Despali, and Volker Springel. A deep-learning model for the density profiles of subhaloes in IllustrisTNG. MNRAS, 532(1):164–176, July 2024

    2024

  119. [128]

    Representation learning approach to probe for dynamical dark energy in matter power spectra

    Davide Piras and Lucas Lombriser. Representation learning approach to probe for dynamical dark energy in matter power spectra. Phys. Rev. D, 110(2):023514, July 2024

    2024

  120. [129]

    Simulating galaxy formation with black hole driven thermal and kinetic feedback

    Rainer Weinberger, Volker Springel, Lars Hernquist, Annalisa Pillepich, Federico Marinacci, Rüdiger Pakmor, Dylan Nelson, Shy Genel, Mark Vogelsberger, Jill Naiman, and Paul Torrey. Simulating galaxy formation with black hole driven thermal and kinetic feedback. MNRAS, 465(3):...

    2017

  121. [130]

    Naiman, Federico Marinacci, and Mark Vogelsberger

    Annalisa Pillepich, Dylan Nelson, Lars Hernquist, Volker Springel, Rüdiger Pakmor, Paul Torrey, Rainer Weinberger, Shy Genel, Jill P. Naiman, Federico Marinacci, and Mark Vogelsberger. First results from the IllustrisTNG simulations: the stellar mass content of groups and clus...

    2018

  122. [131]

    E pur si muove: Galilean-invariant cosmological hydrodynamical simulations on a moving mesh

    Volker Springel. E pur si muove: Galilean-invariant cosmological hydrodynamical simulations on a moving mesh. MNRAS, 401(2):791–851, January 2010

    2010

  123. [132]

    The AREPO Public Code Release

    Rainer Weinberger, Volker Springel, and Rüdiger Pakmor. The AREPO Public Code Release. ApJS, 248(2):32, June 2020

    2020

  124. [133]

    Cosmological smoothed particle hydrodynamics simulations: a hybrid multiphase model for star formation

    Volker Springel and Lars Hernquist. Cosmological smoothed particle hydrodynamics simulations: a hybrid multiphase model for star formation. MNRAS, 339(2):289–311, February 2003

    2003

  125. [134]

    Rafieferantsoa, and Sarah Appleby

    Romeel Davé, Daniel Anglés-Alcázar, Desika Narayanan, Qi Li, Mika H. Rafieferantsoa, and Sarah Appleby. SIMBA: Cosmological simulations with black hole growth and feedback. MNRAS, 486(2):2827–2849, June 2019

    2019

  126. [135]

    Philip F. Hopkins. A new class of accurate, mesh-free hydrodynamic simulation methods. MNRAS, 450(1):53–110, June 2015

    2015

  127. [136]

    Heckman and Philip N

    Timothy M. Heckman and Philip N. Best. The Coevolution of Galaxies and Supermassive Black Holes: Insights from Surveys of the Contemporary Universe. ARA&A, 52:589–660, August 2014. – 41 –

    2014

  128. [137]

    The ASTRID simulation: galaxy formation and reionization

    Simeon Bird, Yueying Ni, Tiziana Di Matteo, Rupert Croft, Yu Feng, and Nianyi Chen. The ASTRID simulation: galaxy formation and reionization. MNRAS, 512(3):3703–3716, May 2022

    2022

  129. [138]

    Mp-gadget/mp-gadget: A tag for getting a doi, October 2018

    Yu Feng, Simeon Bird, Lauren Anderson, Andreu Font-Ribera, and Chris Pedersen. Mp-gadget/mp-gadget: A tag for getting a doi, October 2018

    2018

  130. [139]

    The ASTRID simulation: the evolution of supermassive black holes

    Yueying Ni, Tiziana Di Matteo, Simeon Bird, Rupert Croft, Yu Feng, Nianyi Chen, Michael Tremmel, Colin DeGraf, and Yin Li. The ASTRID simulation: the evolution of supermassive black holes. MNRAS, 513(1):670–692, June 2022

    2022

  131. [140]

    Yueying Ni, Shy Genel, Daniel Anglés-Alcázar, Francisco Villaescusa-Navarro, Yongseok Jo, Simeon Bird, Tiziana Di Matteo, Rupert Croft, Nianyi Chen, Natalí S. M. de Santi, Matthew Gebhardt, Helen Shao, Shivam Pandey, Lars Hernquist, and Romeel Dave. The CAMELS Project: Expandi...

    2023

  132. [141]

    Volker Springel, Simon D. M. White, Giuseppe Tormen, and Guinevere Kauffmann. Populating a cluster of galaxies - I. Results at z=0. MNRAS, 328(3):726–750, December 2001

    2001

  133. [142]

    Dolag, S

    K. Dolag, S. Borgani, G. Murante, and V. Springel. Substructures in hydrodynamical cluster simulations. MNRAS, 399(2):497–514, October 2009

    2009

  134. [143]

    sbi: A toolkit for simulation-based inference

    Alvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro Gonçalves, David Greenberg, and Jakob Macke. sbi: A toolkit for simulation-based inference. The Journal of Open Source Software, 5(52):2505, August 2020

    2020

  135. [144]

    Christopher M. Bishop. Mixture density networks. Workingpaper, Aston University, 1994

    1994

  136. [145]

    Density estimation using Real NVP

    Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using Real NVP. arXiv e-prints, page arXiv:1605.08803, May 2016

    2016 arXiv

  137. [146]

    Neural Spline Flows

    Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios. Neural Spline Flows. arXiv e-prints, page arXiv:1906.04032, June 2019

    1906 arXiv

  138. [147]

    MADE: Masked Autoencoder for Distribution Estimation.arXiv e-prints, page arXiv:1502.03509, February 2015

    Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle. MADE: Masked Autoencoder for Distribution Estimation.arXiv e-prints, page arXiv:1502.03509, February 2015

    2015 arXiv

  139. [148]

    Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology

    Justin Alsing, Benjamin Wandelt, and Stephen Feeney. Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology. MNRAS, 477(3):2874–2885, July 2018

    2018

  140. [149]

    Fastϵ-free Inference of Simulation Models with Bayesian Conditional Density Estimation.arXiv e-prints, page arXiv:1605.06376, May 2016

    George Papamakarios and Iain Murray. Fastϵ-free Inference of Simulation Models with Bayesian Conditional Density Estimation.arXiv e-prints, page arXiv:1605.06376, May 2016

    2016 arXiv

  141. [150]

    Goncalves, Giacomo Bassetto, Kaan Öcal, Marcel Nonnenmacher, and Jakob H

    Jan-Matthis Lueckmann, Pedro J. Goncalves, Giacomo Bassetto, Kaan Öcal, Marcel Nonnenmacher, and Jakob H. Macke. Flexible statistical inference for mechanistic models of neural dynamics. arXiv e-prints, page arXiv:1711.01861, November 2017

    2017 arXiv

  142. [151]

    Sterratt, and Iain Murray

    George Papamakarios, David C. Sterratt, and Iain Murray. Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows.arXiv e-prints, page arXiv:1805.07226, May 2018

    2018 arXiv

  143. [152]

    Jan-Matthis Lueckmann, Giacomo Bassetto, Theofanis Karaletsos, and Jakob H. Macke. Likelihood-free inference with emulator networks.arXiv e-prints, page arXiv:1805.09294, May 2018

    2018 arXiv

  144. [153]

    Greenberg, Marcel Nonnenmacher, and Jakob H

    David S. Greenberg, Marcel Nonnenmacher, and Jakob H. Macke. Automatic Posterior Transformation for Likelihood-Free Inference.arXiv e-prints, page arXiv:1905.07488, May 2019. – 42 –

    1905 arXiv

  145. [154]

    Kullback and R

    S. Kullback and R. A. Leibler. On Information and Sufficiency.The Annals of Mathematical Statistics, 22(1):79 – 86, 1951

    1951

  146. [155]

    Jordan, Zoubin Ghahramani, Tommi S

    Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, and Lawrence K. Saul. An introduction to variational methods for graphical models.Machine Learning, 37(2):183–233, Nov 1999

    1999

  147. [156]

    Variational Inference with Normalizing Flows

    Danilo Jimenez Rezende and Shakir Mohamed. Variational Inference with Normalizing Flows. arXiv e-prints, page arXiv:1505.05770, May 2015

    2015 arXiv

  148. [157]

    Principal Neighbourhood Aggregation for Graph Nets.arXiv e-prints, page arXiv:2004.05718, April 2020

    Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Veličković. Principal Neighbourhood Aggregation for Graph Nets.arXiv e-prints, page arXiv:2004.05718, April 2020

    2004 arXiv

  149. [158]

    Visualizing the Loss Landscape of Neural Nets.arXiv e-prints, page arXiv:1712.09913, December 2017

    Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein. Visualizing the Loss Landscape of Neural Nets.arXiv e-prints, page arXiv:1712.09913, December 2017

    2017 arXiv

  150. [159]

    Miller, Samuel J

    Alex Cole, Benjamin K. Miller, Samuel J. Witte, Maxwell X. Cai, Meiert W. Grootes, Francesco Nattino, and Christoph Weniger. Fast and credible likelihood-free cosmology with truncated marginal neural ratio estimation. J. Cosmology Astropart. Phys., 2022(9):004, September 2022

    2022

  151. [160]

    Validating Bayesian Inference Algorithms with Simulation-Based Calibration.arXiv e-prints, page arXiv:1804.06788, April 2018

    Sean Talts, Michael Betancourt, Daniel Simpson, Aki Vehtari, and Andrew Gelman. Validating Bayesian Inference Algorithms with Simulation-Based Calibration.arXiv e-prints, page arXiv:1804.06788, April 2018

    2018 arXiv

  152. [161]

    Optuna: A next-generation hyperparameter optimization framework

    Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. Optuna: A next-generation hyperparameter optimization framework. InProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2019

    2019

  153. [162]

    Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance.arXiv e-prints, page arXiv:2304.11127, April 2023

    Shuhei Watanabe. Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance.arXiv e-prints, page arXiv:2304.11127, April 2023

    2023 arXiv

  154. [163]

    Algorithms for hyper-parameter optimization

    James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. Algorithms for hyper-parameter optimization. In J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K.Q. Weinberger, editors,Advances in Neural Information Processing Systems, volume 24. Curran Associates, Inc., 2011

    2011

  155. [164]

    Bergstra, D

    J. Bergstra, D. Yamins, and D. D. Cox. Making a Science of Model Search.arXiv e-prints, page arXiv:1209.5111, September 2012

    2012 arXiv

  156. [165]

    Pedregosa, G

    F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python.Journal of Machine Learning ...

    2011

  157. [166]

    Tenenbaum, Vin de Silva, and John C

    Joshua B. Tenenbaum, Vin de Silva, and John C. Langford. A Global Geometric Framework for Nonlinear Dimensionality Reduction.Science, 290(5500):2319–2323, December 2000

    2000

  158. [167]

    Visualizing data using t-sne.Journal of Machine Learning Research, 9(86):2579–2605, 2008

    Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne.Journal of Machine Learning Research, 9(86):2579–2605, 2008

    2008

  159. [168]

    J. D. Hunter. Matplotlib: A 2d graphics environment.Computing in Science & Engineering, 9(3):90–95, 2007

    2007

  160. [169]

    GetDist: a Python package for analysing Monte Carlo samples.arXiv e-prints, 2019

    Antony Lewis. GetDist: a Python package for analysing Monte Carlo samples.arXiv e-prints, 2019

    2019

  161. [170]

    Pytorch: An imperative style, high-performance deep learning library

    Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, – 43 – Benoit Stein...

    2019

  162. [171]

    Harris, K

    Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fe...

    2020

Pith tools

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