Built on

W. R. Morningstar, L. Perreault Levasseur, Y. D. Hezaveh, R. Blandford, P. Marshall, P. Putzky, T. D. Rueter, R. Wechsler, and M. Welling, “Data-Driven Reconstruction of Gravitationally Lensed Galaxies using Recurrent Inference Machines,” arXiv:1901.01359 [astro-ph.IM]
Original
1901
Earlier work this paper cites.
A. Möller and T. de Boissière, “SuperNNova: an open-source framework for Bayesian, neural network-based supernova classification,” MNRAS 491
Original
1901
Earlier work this paper cites.
D. Ribli, B. A. Pataki, J. M. Zorrilla Matilla, D. Hsu, Z. Haiman, and I. Csabai, “Weak lensing cosmology with convolutional neural networks on noisy data,” Mon. Not. Roy. Astron. Soc
Original
1902
Earlier work this paper cites.
N. Krachmalnicoff and M. Tomasi, “Convolutional neural networks on the HEALPix sphere: a pixel-based algorithm and its application to CMB data analysis,” A&A 628
Original
1902
Earlier work this paper cites.
J. Alsing and B. Wandelt, “Nuisance hardened data compression for fast likelihood-free inference,” Mon. Not. Roy. Astron. Soc
Original
1903
Earlier work this paper cites.
J. Alsing, T. Charnock, S. Feeney, and B. Wandelt, “Fast likelihood-free cosmology with neural density estimators and active learning,” Mon. Not. Roy. Astron. Soc
Original
1903
Earlier work this paper cites.
D. Muthukrishna, G. Narayan, K. S. Mandel, R. Biswas, and R. Hložek, “RAPID: Early Classification of Explosive Transients using Deep Learning,” Publ. Astron. Soc. Pac
Original
1904
Earlier work this paper cites.
J. Fluri, T. Kacprzak, A. Lucchi, A. Refregier, A. Amara, T. Hofmann, and A. Schneider, “Cosmological constraints with deep learning from KiDS-450 weak lensing maps,” Phys. Rev. D
Original
1906
Earlier work this paper cites.
N. Sehgal et al
Original
1906
Earlier work this paper cites.
S. Hassan, S. Andrianomena, and C. Doughty, “Constraining the astrophysics and cosmology from 21 cm tomography using deep learning with the SKA,” Mon. Not. Roy. Astron. Soc
Original
1907
Earlier work this paper cites.
K. Zhang and J. S. Bloom, “deepCR: Cosmic Ray Rejection with Deep Learning,” ApJ 889
Original
1907
Earlier work this paper cites.
Sept., 2019
Original
D. Norman, T. Brandt, Z. Berta-Thompson, N. Lewandowska, K. Knierman, N. Chanover, J. Whitley, A. Venkatesan, K. Coble, A. Burgasser, M. Lemoine-Busserolle, K. Cruz, C. S. Moore, K. Osei-Sarfo, and S. Kannappan, “Tying Research Funding to Progress on Inclusion,” in Bulletin of the American Astronomical Society · 1907
Earlier work this paper cites.
N. Jeffrey, F. Lanusse, O. Lahav, and J.-L. Starck, “Deep learning dark matter map reconstructions from DES SV weak lensing data,” Mon. Not. Roy. Astron. Soc
Original
1908
Earlier work this paper cites.
S. Alexander, S. Gleyzer, E. McDonough, M. W. Toomey, and E. Usai, “Deep Learning the Morphology of Dark Matter Substructure,” Astrophys. J
Original
1909
Earlier work this paper cites.
J. Brehmer, S. Mishra-Sharma, J. Hermans, G. Louppe, and K. Cranmer, “Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning,” Astrophys. J
Original
1909
Earlier work this paper cites.
K. Aylor, M. Haq, L. Knox, Y. Hezaveh, and L. Perreault-Levasseur, “Cleaning our own dust: simulating and separating galactic dust foregrounds with neural networks,” MNRAS 500
Original
1909
Earlier work this paper cites.
F. Villaescusa-Navarro, C. Hahn, E. Massara, A. Banerjee, A. M. Delgado, D. K. Ramanah, T. Charnock, E. Giusarma, Y. Li, E. Allys, A. Brochard, C. Uhlemann, C.-T. Chiang, S. He, A. Pisani, A. Obuljen, Y. Feng, E. Castorina, G. Contardo, C. D. Kreisch, A. Nicola, J. Alsing, R. Scoccimarro, L. Verde, M. Viel, S. Ho, S. Mallat, B. Wandelt, and D. N. Spergel, “The Quijote Simulations,” ApJS 250
Original
1909
Earlier work this paper cites.
M. Chianese, A. Coogan, P. Hofma, S. Otten, and C. Weniger, “Differentiable Strong Lensing: Uniting Gravity and Neural Nets through Differentiable Probabilistic Programming,” Mon. Not. Roy. Astron. Soc
Original
1910
Earlier work this paper cites.
A. Diaz Rivero and C. Dvorkin, “Direct Detection of Dark Matter Substructure in Strong Lens Images with Convolutional Neural Networks,” Phys. Rev. D
Original
1910
Earlier work this paper cites.
K. Cranmer, J. Brehmer, and G. Louppe, “The frontier of simulation-based inference,” Proc. Nat. Acad. Sci
Original
1911
Earlier work this paper cites.
E. Bertin and S. Arnouts, “SExtractor: Software for source extraction.,” A&AS 117
1996
Earlier work this paper cites.
D. Kodi Ramanah, T. Charnock, F. Villaescusa-Navarro, and B. D. Wandelt, “Super-resolution emulator of cosmological simulations using deep physical models,” MNRAS 495
Original
2001
Earlier work this paper cites.
N. Gupta and C. L. Reichardt, “Mass Estimation of Galaxy Clusters with Deep Learning. I. Sunyaev–Zel’dovich Effect,” Astrophys. J
Original
2003
Earlier work this paper cites.
H. Sun, A. V. Dalca, and K. L. Bouman, “Learning a Probabilistic Strategy for Computational Imaging Sensor Selection,” arXiv e-prints
Original
2003
Earlier work this paper cites.
C. Singh, W. Ha, F. Lanusse, V. Boehm, J. Liu, and B. Yu, “Transformation Importance with Applications to Cosmology,” arXiv e-prints
Original
2003
Earlier work this paper cites.
M. A. Petroff, G. E. Addison, C. L. Bennett, and J. L. Weiland, “Full-sky Cosmic Microwave Background Foreground Cleaning Using Machine Learning,” Astrophys. J
Original
2004
Earlier work this paper cites.
A. Ghosh et al
Original
2004
Earlier work this paper cites.
J. a. Caldeira and B. Nord, “Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms,” Sci. Technol
Original
2004
Earlier work this paper cites.
X. Huang, C. Storfer, A. Gu, V. Ravi, A. Pilon, W. Sheu, R. Venguswamy, S. Banka, A. Dey, M. Landriau, D. Lang, A. Meisner, J. Moustakas, A. D. Myers, R. Sajith, E. F. Schlafly, and D. J. Schlegel, “Discovering New Strong Gravitational Lenses in the DESI Legacy Imaging Surveys,” ApJ 909
Original
2005
Earlier work this paper cites.
A. Mootoovaloo, A. F. Heavens, A. H. Jaffe, and F. Leclercq, “Parameter Inference for Weak Lensing using Gaussian Processes and MOPED,” Mon. Not. Roy. Astron. Soc
Original
2005
Earlier work this paper cites.
N. Gupta and C. L. Reichardt, “Mass Estimation of Galaxy Clusters with Deep Learning II. Cosmic Microwave Background Cluster Lensing,” Astrophys. J
Original
2005
Earlier work this paper cites.
H. J. Hortúa, L. Malago, and R. Volpi, “Constraining the Reionization History using Bayesian Normalizing Flows,” Mach. Learn. Sci. Tech
Original
2005
Earlier work this paper cites.
P. Villanueva-Domingo and F. Villaescusa-Navarro, “Removing Astrophysics in 21 cm maps with Neural Networks,” Astrophys. J
Original
2006
Earlier work this paper cites.
M. Cranmer, A. Sanchez-Gonzalez, P. Battaglia, R. Xu, K. Cranmer, D. Spergel, and S. Ho, “Discovering Symbolic Models from Deep Learning with Inductive Biases,” arXiv e-prints
Original
2006
Earlier work this paper cites.
S. Cheng, Y.-S. Ting, B. Ménard, and J. Bruna, “A new approach to observational cosmology using the scattering transform,” MNRAS 499
Original
2006
Earlier work this paper cites.
T. Ishiyama, F. Prada, A. A. Klypin, M. Sinha, R. B. Metcalf, E. Jullo, B. Altieri, S. A. Cora, D. Croton, S. de la Torre, D. E. Millán-Calero, T. Oogi, J. Ruedas, and C. A. Vega-Martínez, “The Uchuu simulations: Data Release 1 and dark matter halo concentrations,” MNRAS 506
Original
2007
Earlier work this paper cites.
A. Diaz Rivero and C. Dvorkin, “Flow-Based Likelihoods for Non-Gaussian Inference,” Phys. Rev. D
Original
2007
Earlier work this paper cites.
S. Alexander, S. Gleyzer, H. Parul, P. Reddy, M. W. Toomey, E. Usai, and R. Von Klar, “Decoding Dark Matter Substructure without Supervision,” arXiv:2008.12731 [astro-ph.CO]
Original
2008
Earlier work this paper cites.
V. A. Villar et al
Original
2008
Earlier work this paper cites.
V. A. Villar, G. Hosseinzadeh, E. Berger, M. Ntampaka, D. O. Jones, P. Challis, R. Chornock, M. R. Drout, R. J. Foley, R. P. Kirshner, R. Lunnan, R. Margutti, D. Milisavljevic, N. Sanders, Y.-C. Pan, A. Rest, D. M. Scolnic, E. Magnier, N. Metcalfe, R. Wainscoat, and C. Waters, “SuperRAENN: A Semisupervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium-Deep Survey Supernovae,” ApJ 905
Original
2008
Earlier work this paper cites.
C. J. Lintott et al
Original
2008
Earlier work this paper cites.
B. Ostdiek, A. Diaz Rivero, and C. Dvorkin, “Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation,” Astrophys. J
Original
2009
Earlier work this paper cites.
B. Ostdiek, A. Diaz Rivero, and C. Dvorkin, “Image segmentation for analyzing galaxy-galaxy strong lensing systems,” Astron. Astrophys
Original
2009
Earlier work this paper cites.
N. Jeffrey, J. Alsing, and F. Lanusse, “Likelihood-free inference with neural compression of DES SV weak lensing map statistics,” Mon. Not. Roy. Astron. Soc
Original
2009
Earlier work this paper cites.

Similar

LSST Dark Energy Science
Original
2010
Cited alongside, same era.
10, 2020
Original
A. Coogan, K. Karchev, and C. Weniger, “Targeted Likelihood-Free Inference of Dark Matter Substructure in Strongly-Lensed Galaxies,” in 34th Conference on Neural Information Processing Systems · 2010
Cited alongside, same era.
Q. He, A. Robertson, J. Nightingale, S. Cole, C. S. Frenk, R. Massey, A. Amvrosiadis, R. Li, X. Cao, and A. Etherington, “A forward-modelling method to infer the dark matter particle mass from strong gravitational lenses,” Mon. Not. Roy. Astron. Soc
Original
2010
Cited alongside, same era.
T. L. Makinen, L. Lancaster, F. Villaescusa-Navarro, P. Melchior, S. Ho, L. Perreault-Levasseur, and D. N. Spergel, “deep21: a deep learning method for 21 cm foreground removal,” JCAP
Original
2010
Cited alongside, same era.

Then

K. Bundy, K. Westfall, N. MacDonald, R. Kupke, M. Savage, C. Poppett, A. Alabi, G. Becker, J. Burchett, P. Capak, et al
Original
2019
Later among the works it cites.
N. Perraudin, M. Defferrard, T. Kacprzak, and R. Sgier, “DeepSphere: Efficient spherical convolutional neural network with HEALPix sampling for cosmological applications,” Astronomy and Computing
Original
2019
Later among the works it cites.
Nov., 2019
D. Norman, “Can Big Data Lead an Inclusion Revolution?,” in Advancing Astronomy for All: ASP 2018 · 2019
Later among the works it cites.
D. Norman, K. Cruz, V. Desai, B. Lundgren, E. Bellm, F. Economou, A. Smith, A. Bauer, B. Nord, C. Schafer, et al
Original
2019
Later among the works it cites.
O. M. Springer, E. O. Ofek, Y. Weiss, and J. Merten, “Weak lensing shear estimation beyond the shape-noise limit: a machine learning approach,” Mon. Not. Roy. Astron. Soc

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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alphaXiv is searching for related work…

Reading the bibliography…

2022

Machine Learning and Cosmology

Dvorkin, Cora, Mishra-Sharma, Siddharth, Nord, Brian et al.

Understand

Methods based on machine learning have recently made substantial inroads in many corners of cosmology.

Through this process, new computational tools, new perspectives on data collection, model development, analysis, and discovery, as well as new communities and educational pathways have emerged.
Despite rapid progress, substantial potential at the intersection of cosmology and machine learning remains untapped.
In this white paper, we summarize current and ongoing developments relating to the application of machine learning within cosmology and provide a set of recommendations aimed at maximizing the scientific impact of these burgeoning tools over the coming decade through both technical development as well as the fostering of emerging communities.

Built on

W. R. Morningstar, L. Perreault Levasseur, Y. D. Hezaveh, R. Blandford, P. Marshall, P. Putzky, T. D. Rueter, R. Wechsler, and M. Welling, “Data-Driven Reconstruction of Gravitationally Lensed Galaxies using Recurrent Inference Machines,” arXiv:1901.01359 [astro-ph.IM]
Original
1901
Earlier work this paper cites.
A. Möller and T. de Boissière, “SuperNNova: an open-source framework for Bayesian, neural network-based supernova classification,” MNRAS 491
Original
1901
Earlier work this paper cites.
D. Ribli, B. A. Pataki, J. M. Zorrilla Matilla, D. Hsu, Z. Haiman, and I. Csabai, “Weak lensing cosmology with convolutional neural networks on noisy data,” Mon. Not. Roy. Astron. Soc
Original
1902
Earlier work this paper cites.
N. Krachmalnicoff and M. Tomasi, “Convolutional neural networks on the HEALPix sphere: a pixel-based algorithm and its application to CMB data analysis,” A&A 628
Original
1902
Earlier work this paper cites.
J. Alsing and B. Wandelt, “Nuisance hardened data compression for fast likelihood-free inference,” Mon. Not. Roy. Astron. Soc
Original
1903
Earlier work this paper cites.
J. Alsing, T. Charnock, S. Feeney, and B. Wandelt, “Fast likelihood-free cosmology with neural density estimators and active learning,” Mon. Not. Roy. Astron. Soc
Original
1903
Earlier work this paper cites.
D. Muthukrishna, G. Narayan, K. S. Mandel, R. Biswas, and R. Hložek, “RAPID: Early Classification of Explosive Transients using Deep Learning,” Publ. Astron. Soc. Pac
Original
1904
Earlier work this paper cites.
J. Fluri, T. Kacprzak, A. Lucchi, A. Refregier, A. Amara, T. Hofmann, and A. Schneider, “Cosmological constraints with deep learning from KiDS-450 weak lensing maps,” Phys. Rev. D
Original
1906
Earlier work this paper cites.
N. Sehgal et al
Original
1906
Earlier work this paper cites.
S. Hassan, S. Andrianomena, and C. Doughty, “Constraining the astrophysics and cosmology from 21 cm tomography using deep learning with the SKA,” Mon. Not. Roy. Astron. Soc
Original
1907
Earlier work this paper cites.
K. Zhang and J. S. Bloom, “deepCR: Cosmic Ray Rejection with Deep Learning,” ApJ 889
Original
1907
Earlier work this paper cites.
Sept., 2019
Original
D. Norman, T. Brandt, Z. Berta-Thompson, N. Lewandowska, K. Knierman, N. Chanover, J. Whitley, A. Venkatesan, K. Coble, A. Burgasser, M. Lemoine-Busserolle, K. Cruz, C. S. Moore, K. Osei-Sarfo, and S. Kannappan, “Tying Research Funding to Progress on Inclusion,” in Bulletin of the American Astronomical Society · 1907
Earlier work this paper cites.
N. Jeffrey, F. Lanusse, O. Lahav, and J.-L. Starck, “Deep learning dark matter map reconstructions from DES SV weak lensing data,” Mon. Not. Roy. Astron. Soc
Original
1908
Earlier work this paper cites.
S. Alexander, S. Gleyzer, E. McDonough, M. W. Toomey, and E. Usai, “Deep Learning the Morphology of Dark Matter Substructure,” Astrophys. J
Original
1909
Earlier work this paper cites.
J. Brehmer, S. Mishra-Sharma, J. Hermans, G. Louppe, and K. Cranmer, “Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning,” Astrophys. J
Original
1909
Earlier work this paper cites.
K. Aylor, M. Haq, L. Knox, Y. Hezaveh, and L. Perreault-Levasseur, “Cleaning our own dust: simulating and separating galactic dust foregrounds with neural networks,” MNRAS 500
Original
1909
Earlier work this paper cites.
F. Villaescusa-Navarro, C. Hahn, E. Massara, A. Banerjee, A. M. Delgado, D. K. Ramanah, T. Charnock, E. Giusarma, Y. Li, E. Allys, A. Brochard, C. Uhlemann, C.-T. Chiang, S. He, A. Pisani, A. Obuljen, Y. Feng, E. Castorina, G. Contardo, C. D. Kreisch, A. Nicola, J. Alsing, R. Scoccimarro, L. Verde, M. Viel, S. Ho, S. Mallat, B. Wandelt, and D. N. Spergel, “The Quijote Simulations,” ApJS 250
Original
1909
Earlier work this paper cites.
M. Chianese, A. Coogan, P. Hofma, S. Otten, and C. Weniger, “Differentiable Strong Lensing: Uniting Gravity and Neural Nets through Differentiable Probabilistic Programming,” Mon. Not. Roy. Astron. Soc
Original
1910
Earlier work this paper cites.
A. Diaz Rivero and C. Dvorkin, “Direct Detection of Dark Matter Substructure in Strong Lens Images with Convolutional Neural Networks,” Phys. Rev. D
Original
1910
Earlier work this paper cites.
K. Cranmer, J. Brehmer, and G. Louppe, “The frontier of simulation-based inference,” Proc. Nat. Acad. Sci
Original
1911
Earlier work this paper cites.
E. Bertin and S. Arnouts, “SExtractor: Software for source extraction.,” A&AS 117
1996
Earlier work this paper cites.
D. Kodi Ramanah, T. Charnock, F. Villaescusa-Navarro, and B. D. Wandelt, “Super-resolution emulator of cosmological simulations using deep physical models,” MNRAS 495
Original
2001
Earlier work this paper cites.
N. Gupta and C. L. Reichardt, “Mass Estimation of Galaxy Clusters with Deep Learning. I. Sunyaev–Zel’dovich Effect,” Astrophys. J
Original
2003
Earlier work this paper cites.
H. Sun, A. V. Dalca, and K. L. Bouman, “Learning a Probabilistic Strategy for Computational Imaging Sensor Selection,” arXiv e-prints
Original
2003
Earlier work this paper cites.
C. Singh, W. Ha, F. Lanusse, V. Boehm, J. Liu, and B. Yu, “Transformation Importance with Applications to Cosmology,” arXiv e-prints
Original
2003
Earlier work this paper cites.
M. A. Petroff, G. E. Addison, C. L. Bennett, and J. L. Weiland, “Full-sky Cosmic Microwave Background Foreground Cleaning Using Machine Learning,” Astrophys. J
Original
2004
Earlier work this paper cites.
A. Ghosh et al
Original
2004
Earlier work this paper cites.
J. a. Caldeira and B. Nord, “Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms,” Sci. Technol
Original
2004
Earlier work this paper cites.
X. Huang, C. Storfer, A. Gu, V. Ravi, A. Pilon, W. Sheu, R. Venguswamy, S. Banka, A. Dey, M. Landriau, D. Lang, A. Meisner, J. Moustakas, A. D. Myers, R. Sajith, E. F. Schlafly, and D. J. Schlegel, “Discovering New Strong Gravitational Lenses in the DESI Legacy Imaging Surveys,” ApJ 909
Original
2005
Earlier work this paper cites.
A. Mootoovaloo, A. F. Heavens, A. H. Jaffe, and F. Leclercq, “Parameter Inference for Weak Lensing using Gaussian Processes and MOPED,” Mon. Not. Roy. Astron. Soc
Original
2005
Earlier work this paper cites.
N. Gupta and C. L. Reichardt, “Mass Estimation of Galaxy Clusters with Deep Learning II. Cosmic Microwave Background Cluster Lensing,” Astrophys. J
Original
2005
Earlier work this paper cites.
H. J. Hortúa, L. Malago, and R. Volpi, “Constraining the Reionization History using Bayesian Normalizing Flows,” Mach. Learn. Sci. Tech
Original
2005
Earlier work this paper cites.
P. Villanueva-Domingo and F. Villaescusa-Navarro, “Removing Astrophysics in 21 cm maps with Neural Networks,” Astrophys. J
Original
2006
Earlier work this paper cites.
M. Cranmer, A. Sanchez-Gonzalez, P. Battaglia, R. Xu, K. Cranmer, D. Spergel, and S. Ho, “Discovering Symbolic Models from Deep Learning with Inductive Biases,” arXiv e-prints
Original
2006
Earlier work this paper cites.
S. Cheng, Y.-S. Ting, B. Ménard, and J. Bruna, “A new approach to observational cosmology using the scattering transform,” MNRAS 499
Original
2006
Earlier work this paper cites.
T. Ishiyama, F. Prada, A. A. Klypin, M. Sinha, R. B. Metcalf, E. Jullo, B. Altieri, S. A. Cora, D. Croton, S. de la Torre, D. E. Millán-Calero, T. Oogi, J. Ruedas, and C. A. Vega-Martínez, “The Uchuu simulations: Data Release 1 and dark matter halo concentrations,” MNRAS 506
Original
2007
Earlier work this paper cites.
A. Diaz Rivero and C. Dvorkin, “Flow-Based Likelihoods for Non-Gaussian Inference,” Phys. Rev. D
Original
2007
Earlier work this paper cites.
S. Alexander, S. Gleyzer, H. Parul, P. Reddy, M. W. Toomey, E. Usai, and R. Von Klar, “Decoding Dark Matter Substructure without Supervision,” arXiv:2008.12731 [astro-ph.CO]
Original
2008
Earlier work this paper cites.
V. A. Villar et al
Original
2008
Earlier work this paper cites.
V. A. Villar, G. Hosseinzadeh, E. Berger, M. Ntampaka, D. O. Jones, P. Challis, R. Chornock, M. R. Drout, R. J. Foley, R. P. Kirshner, R. Lunnan, R. Margutti, D. Milisavljevic, N. Sanders, Y.-C. Pan, A. Rest, D. M. Scolnic, E. Magnier, N. Metcalfe, R. Wainscoat, and C. Waters, “SuperRAENN: A Semisupervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium-Deep Survey Supernovae,” ApJ 905
Original
2008
Earlier work this paper cites.
C. J. Lintott et al
Original
2008
Earlier work this paper cites.
B. Ostdiek, A. Diaz Rivero, and C. Dvorkin, “Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation,” Astrophys. J
Original
2009
Earlier work this paper cites.
B. Ostdiek, A. Diaz Rivero, and C. Dvorkin, “Image segmentation for analyzing galaxy-galaxy strong lensing systems,” Astron. Astrophys
Original
2009
Earlier work this paper cites.
N. Jeffrey, J. Alsing, and F. Lanusse, “Likelihood-free inference with neural compression of DES SV weak lensing map statistics,” Mon. Not. Roy. Astron. Soc
Original
2009
Earlier work this paper cites.

Similar

LSST Dark Energy Science
Original
2010
Cited alongside, same era.
10, 2020
Original
A. Coogan, K. Karchev, and C. Weniger, “Targeted Likelihood-Free Inference of Dark Matter Substructure in Strongly-Lensed Galaxies,” in 34th Conference on Neural Information Processing Systems · 2010
Cited alongside, same era.
Q. He, A. Robertson, J. Nightingale, S. Cole, C. S. Frenk, R. Massey, A. Amvrosiadis, R. Li, X. Cao, and A. Etherington, “A forward-modelling method to infer the dark matter particle mass from strong gravitational lenses,” Mon. Not. Roy. Astron. Soc
Original
2010
Cited alongside, same era.
T. L. Makinen, L. Lancaster, F. Villaescusa-Navarro, P. Melchior, S. Ho, L. Perreault-Levasseur, and D. N. Spergel, “deep21: a deep learning method for 21 cm foreground removal,” JCAP
Original
2010
Cited alongside, same era.

Then

K. Bundy, K. Westfall, N. MacDonald, R. Kupke, M. Savage, C. Poppett, A. Alabi, G. Becker, J. Burchett, P. Capak, et al
Original
2019
Later among the works it cites.
N. Perraudin, M. Defferrard, T. Kacprzak, and R. Sgier, “DeepSphere: Efficient spherical convolutional neural network with HEALPix sampling for cosmological applications,” Astronomy and Computing
Original
2019
Later among the works it cites.
Nov., 2019
D. Norman, “Can Big Data Lead an Inclusion Revolution?,” in Advancing Astronomy for All: ASP 2018 · 2019
Later among the works it cites.
D. Norman, K. Cruz, V. Desai, B. Lundgren, E. Bellm, F. Economou, A. Smith, A. Bauer, B. Nord, C. Schafer, et al
Original
2019
Later among the works it cites.
O. M. Springer, E. O. Ofek, Y. Weiss, and J. Merten, “Weak lensing shear estimation beyond the shape-noise limit: a machine learning approach,” Mon. Not. Roy. Astron. Soc

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

F. Villaescusa-Navarro, D. Anglés-Alcázar, S. Genel, D. N. Spergel, R. S. Somerville, R. Dave, A. Pillepich, L. Hernquist, D. Nelson, P. Torrey, D. Narayanan, Y. Li, O. Philcox, V. La Torre, A. Maria Delgado, S. Ho, S. Hassan, B. Burkhart, D. Wadekar, N. Battaglia, G. Contardo, and G. L. Bryan, “The CAMELS Project: Cosmology and Astrophysics with Machine-learning Simulations,” ApJ 915

Original

2010

Cited alongside, same era.

Y. Li, Y. Ni, R. A. C. Croft, T. Di Matteo, S. Bird, and Y. Feng, “AI-assisted superresolution cosmological simulations,” Proceedings of the National Academy of Science

Original

2010

Cited alongside, same era.

T. Matheson, C. Stubens, N. Wolf, C.-H. Lee, G. Narayan, A. Saha, A. Scott, M. Soraisam, A. S. Bolton, B. Hauger, D. R. Silva, J. Kececioglu, C. Scheidegger, R. Snodgrass, P. D. Aleo, E. Evans-Jacquez, N. Singh, Z. Wang, S. Yang, and Z. Zhao, “The ANTARES Astronomical Time-domain Event Broker,” AJ 161

Original

2011

Cited alongside, same era.

Curran Associates Inc., Red Hook, NY, USA, 2011

A. Graves, “Practical variational inference for neural networks,” in Proceedings of the 24th International Conference on Neural Information Processing Systems · 2011

Cited alongside, same era.

11, 2020

Original

N. Jeffrey and B. D. Wandelt, “Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks,” in 34th Conference on Neural Information Processing Systems · 2011

Cited alongside, same era.

J. Hermans, N. Banik, C. Weniger, G. Bertone, and G. Louppe, “Towards constraining warm dark matter with stellar streams through neural simulation-based inference,” Mon. Not. Roy. Astron. Soc

Original

2011

Cited alongside, same era.

LSST Dark Energy Science

Original

2012

Cited alongside, same era.

D. Wadekar, F. Villaescusa-Navarro, S. Ho, and L. Perreault-Levasseur, “Modeling assembly bias with machine learning and symbolic regression,” arXiv:2012.00111 [astro-ph.CO]

Original

2012

Cited alongside, same era.

Original

2020