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, ApJ 883
Original
1901
Earlier work this paper cites.
S. Alexander, J. J. Bramburger, and E. McDonough, Dark Disk Substructure and Superfluid Dark Matter, Phys. Lett. B 797
Original
1901
Earlier work this paper cites.
A. Drlica-Wagner et al. (LSST Dark Matter Group), Probing the Fundamental Nature of Dark Matter with the Large Synoptic Survey Telescope, arXiv e-prints , arXiv:1902.01055 (2019), arXiv:1902.01055 [astro-ph.CO]
Original
1902
Earlier work this paper cites.
M. Ntampaka et al. , The Role of Machine Learning in the Next Decade of Cosmology, Bull. Am. Astron. Soc 51
Original
1902
Earlier work this paper cites.
A. Verma, T. Collett, G. P. Smith, Strong Lensing Science Collaboration, and the DESC Strong Lensing Science Working Group, Strong Lensing considerations for the LSST observing strategy, arXiv e-prints , arXiv:1902.05141 (2019), arXiv:1902.05141 [astro-ph.GA]
Original
1902
Earlier work this paper cites.
M. Aaboud et al. (ATLAS), Constraints on mediator-based dark matter and scalar dark energy models using s = 13 \sqrt{s}=13 TeV p p pp collision data collected by the ATLAS detector, JHEP 05
Original
1903
Earlier work this paper cites.
J. Simon et al. , Testing the Nature of Dark Matter with Extremely Large Telescopes, Bull. Am. Astron. Soc 51
Original
1903
Earlier work this paper cites.
G. Carleo et al. , Machine learning and the physical sciences, Reviews of Modern Physics 91
Original
1903
Earlier work this paper cites.
A. Mishra, P. Reddy, and R. Nigam, Baryon density extraction and isotropy analysis of Cosmic Microwave Background using Deep Learning, arXiv e-prints , arXiv:1903.12253 (2019a), arXiv:1903.12253 [astro-ph.CO]
Original
1903
Earlier work this paper cites.
A. Blance, M. Spannowsky, and P. Waite, Adversarially-trained autoencoders for robust unsupervised new physics searches, JHEP 10
Original
1905
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 100
Original
1906
Earlier work this paper cites.
S. Hassan, S. Andrianomena, and C. Doughty, Constraining the astrophysics and cosmology from 21cm tomography using deep learning with the SKA, MNRAS 494
Original
1907
Earlier work this paper cites.
S. Pan et al. , Cosmological parameter estimation from large-scale structure deep learning, arXiv e-prints , arXiv:1908.10590 (2019), arXiv:1908.10590 [astro-ph.CO]
Original
1908
Earlier work this paper cites.
A. Mishra, P. Reddy, and R. Nigam, CMB-GAN: Fast Simulations of Cosmic Microwave background anisotropy maps using Deep Learning, arXiv e-prints , arXiv:1908.04682 (2019b), arXiv:1908.04682 [astro-ph.CO]
Original
1908
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. 886
Original
1909
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. 893
Original
1909
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 101
Original
1910
Earlier work this paper cites.
A. S. Bolton, S. Burles, L. V. E. Koopmans, T. Treu, R. Gavazzi, L. A. Moustakas, R. Wayth, and D. J. Schlegel, The Sloan Lens ACS Survey. V. The Full ACS Strong-Lens Sample, ApJ 682
Original
1931
Earlier work this paper cites.
S. Vegetti, L. V. E. Koopmans, A. Bolton, T. Treu, and R. Gavazzi, Detection of a dark substructure through gravitational imaging, MNRAS 408
Original
1969
Earlier work this paper cites.
S. Vegetti et al. , Detection of a dark substructure through gravitational imaging, MNRAS 408
Original
1969
Earlier work this paper cites.
R. D. Peccei and H. R. Quinn, CP Conservation in the Presence of Instantons, Phys. Rev. Lett. 38
1977
Earlier work this paper cites.
F. Wilczek, Problem of Strong P P and T T Invariance in the Presence of Instantons, Phys. Rev. Lett. 40
1978
Earlier work this paper cites.
S. Weinberg, A New Light Boson?, Phys. Rev. Lett. 40
1978
Earlier work this paper cites.
J. Preskill, M. B. Wise, and F. Wilczek, Cosmology of the Invisible Axion, Phys. Lett. B120
1982
Earlier work this paper cites.
L. F. Abbott and P. Sikivie, A Cosmological Bound on the Invisible Axion, Phys. Lett. B120
1982
Earlier work this paper cites.
M. Dine and W. Fischler, The Not So Harmless Axion, Phys. Lett. B120
1982
Earlier work this paper cites.
G. E. Hinton and T. J. Sejnowski, Analyzing cooperative computation, Proceedings of the Fifth Annual Conference of the Cognitive Science Society, Rochester NY (1983)
1983
Earlier work this paper cites.
G. Steigman and M. S. Turner, Cosmological Constraints on the Properties of Weakly Interacting Massive Particles, Nucl. Phys. B 253
1985
Earlier work this paper cites.
M. W. Goodman and E. Witten, Detectability of Certain Dark Matter Candidates, Phys. Rev. D31
1985
Earlier work this paper cites.
A. K. Drukier, K. Freese, and D. N. Spergel, Detecting Cold Dark Matter Candidates, Phys. Rev. D33
1986
Earlier work this paper cites.
D. E. Rumelhart, G. E. Hinton and R. J. Williams, Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1. chap. Learning Internal Representations by Error Propagation, pp. 318–362., MIT Press, Cambridge, MA, USA (1986)
1986
Earlier work this paper cites.
G. Kauffmann, S. D. M. White, and B. Guiderdoni, The Formation and Evolution of Galaxies Within Merging Dark Matter Haloes, MNRAS 264
1993
Earlier work this paper cites.
S. Seitz, P. Schneider, and J. Ehlers, Light propagation in arbitrary spacetimes and the gravitational lens approximation, Classical and Quantum Gravity 11
1994
Earlier work this paper cites.
S.-J. Sin, Late time cosmological phase transition and galactic halo as Bose liquid, Phys. Rev. D50
1994
Earlier work this paper cites.
R. H. Brandenberger, Topological defects and structure formation, Int. J. Mod. Phys. A9
1994
Earlier work this paper cites.
A. Burkert, The Structure of Dark Matter Halos in Dwarf Galaxies, ApJL 447
1995
Earlier work this paper cites.
J.F. Navarro, C.S. Frenk and S.D.M. White, The Structure of Cold Dark Matter Halos, ApJ 462
1996
Earlier work this paper cites.
S. Mao and P. Schneider, Evidence for Substructure in lens galaxies?, MNRAS 295
1998
Earlier work this paper cites.
B. Moore, S. Ghigna, F. Governato, G. Lake, T. Quinn, J. Stadel, and P. Tozzi, Dark matter substructure within galactic halos, The Astrophysical Journal 524
1999
Earlier work this paper cites.
A. Klypin, A. V. Kravtsov, O. Valenzuela, and F. Prada, Where are the missing galactic satellites?, The Astrophysical Journal 522
1999
Earlier work this paper cites.
W. Hu, R. Barkana, and A. Gruzinov, Cold and fuzzy dark matter, Phys. Rev. Lett. 85
2000
Earlier work this paper cites.
K. Yi, Y. Guo, Y. Fan, J. Hamann, and Y. G. Wang, CosmoVAE: Variational Autoencoder for CMB Image Inpainting, arXiv e-prints , arXiv:2001.11651 (2020), arXiv:2001.11651 [eess.IV]
Original
2001
Earlier work this paper cites.
N. Dalal and C.S. Kochanek, Direct Detection of CDM Substructure, ApJ 572
2002
Earlier work this paper cites.
F. List and G. F. Lewis, A unified framework for 21 cm tomography sample generation and parameter inference with progressively growing GANs, MNRAS 493
Original
2002
Earlier work this paper cites.
M. P. Silverman and R. L. Mallett, Dark matter as a cosmic Bose-Einstein condensate and possible superfluid, Gen. Rel. Grav. 34
2002
Earlier work this paper cites.
G. E. Hinton, Training products of experts by minimizing contrastive divergence, Neural Computation 14
2002
Earlier work this paper cites.
F. Froborg and A. R. Duffy, Annual Modulation in Direct Dark Matter Searches, arXiv e-prints , arXiv:2003.04545 (2020), arXiv:2003.04545 [astro-ph.CO]
Original
2003
Earlier work this paper cites.
J. Rico, Gamma-Ray Dark Matter Searches in Milky Way Satellites—A Comparative Review of Data Analysis Methods and Current Results, Galaxies 8
Original
2003
Earlier work this paper cites.

Similar

S. Mishra-Sharma, K. Van Tilburg, and N. Weiner, Power of halometry, Phys. Rev. D 102
Original
2003
Cited alongside, same era.
F. Farsian, N. Krachmalnicoff, and C. Baccigalupi, Foreground model recognition through Neural Networks for CMB B-mode observations, JCAP 07
Original
2003
Cited alongside, same era.
D. Clowe, A. Gonzalez, and M. Markevitch, Weak lensing mass reconstruction of the interacting cluster 1E0657-558: Direct evidence for the existence of dark matter, Astrophys. J. 604
2004
Cited alongside, same era.
R. Canameras, S. Schuldt, S. H. Suyu, S. Taubenberger, T. Meinhardt, L. Leal-Taixe, C. Lemon, K. Rojas, and E. Savary, HOLISMOKES – II. Identifying galaxy-scale strong gravitational lenses in Pan-STARRS using convolutional neural networks, arXiv e-prints , arXiv:2004.13048 (2020), arXiv:2004.13048 [astro-ph.GA]
Original
2004
Cited alongside, same era.

Then

K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, arXiv e-prints , arXiv:1512.03385 (2015), arXiv:1512.03385 [cs.CV]
Original
2015
Later among the works it cites.
A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey, Adversarial Autoencoders, arXiv e-prints , arXiv:1511.05644 (2015), arXiv:1511.05644 [cs.LG]
Original
2015
Later among the works it cites.
MAGIC Collaboration, Limits to dark matter annihilation cross-section from a combined analysis of MAGIC and Fermi-LAT observations of dwarf satellite galaxies, J. Cosmol. Astropart. Phys. 2016
Original
2016
Later among the works it cites.
Planck Collaboration, Planck 2015 results. XIII. Cosmological parameters, A&A 594
Original
2016
Later among the works it cites.

Beyond the bibliography

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Reading the bibliography…

2020

Decoding Dark Matter Substructure without Supervision

Alexander, Stephon, Gleyzer, Sergei, Parul, Hanna et al.

Understand

The identity of dark matter remains one of the most pressing questions in physics today.

While many promising dark matter candidates have been put forth over the last half-century, to date the true identity of dark matter remains elusive.
While it is possible that one of the many proposed candidates may turn out to be dark matter, it is at least equally likely that the correct physical description has yet to be proposed.
To address this challenge, novel applications of machine learning can help physicists gain insight into the dark sector from a theory agnostic perspective.

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, ApJ 883
Original
1901
Earlier work this paper cites.
S. Alexander, J. J. Bramburger, and E. McDonough, Dark Disk Substructure and Superfluid Dark Matter, Phys. Lett. B 797
Original
1901
Earlier work this paper cites.
A. Drlica-Wagner et al. (LSST Dark Matter Group), Probing the Fundamental Nature of Dark Matter with the Large Synoptic Survey Telescope, arXiv e-prints , arXiv:1902.01055 (2019), arXiv:1902.01055 [astro-ph.CO]
Original
1902
Earlier work this paper cites.
M. Ntampaka et al. , The Role of Machine Learning in the Next Decade of Cosmology, Bull. Am. Astron. Soc 51
Original
1902
Earlier work this paper cites.
A. Verma, T. Collett, G. P. Smith, Strong Lensing Science Collaboration, and the DESC Strong Lensing Science Working Group, Strong Lensing considerations for the LSST observing strategy, arXiv e-prints , arXiv:1902.05141 (2019), arXiv:1902.05141 [astro-ph.GA]
Original
1902
Earlier work this paper cites.
M. Aaboud et al. (ATLAS), Constraints on mediator-based dark matter and scalar dark energy models using s = 13 \sqrt{s}=13 TeV p p pp collision data collected by the ATLAS detector, JHEP 05
Original
1903
Earlier work this paper cites.
J. Simon et al. , Testing the Nature of Dark Matter with Extremely Large Telescopes, Bull. Am. Astron. Soc 51
Original
1903
Earlier work this paper cites.
G. Carleo et al. , Machine learning and the physical sciences, Reviews of Modern Physics 91
Original
1903
Earlier work this paper cites.
A. Mishra, P. Reddy, and R. Nigam, Baryon density extraction and isotropy analysis of Cosmic Microwave Background using Deep Learning, arXiv e-prints , arXiv:1903.12253 (2019a), arXiv:1903.12253 [astro-ph.CO]
Original
1903
Earlier work this paper cites.
A. Blance, M. Spannowsky, and P. Waite, Adversarially-trained autoencoders for robust unsupervised new physics searches, JHEP 10
Original
1905
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 100
Original
1906
Earlier work this paper cites.
S. Hassan, S. Andrianomena, and C. Doughty, Constraining the astrophysics and cosmology from 21cm tomography using deep learning with the SKA, MNRAS 494
Original
1907
Earlier work this paper cites.
S. Pan et al. , Cosmological parameter estimation from large-scale structure deep learning, arXiv e-prints , arXiv:1908.10590 (2019), arXiv:1908.10590 [astro-ph.CO]
Original
1908
Earlier work this paper cites.
A. Mishra, P. Reddy, and R. Nigam, CMB-GAN: Fast Simulations of Cosmic Microwave background anisotropy maps using Deep Learning, arXiv e-prints , arXiv:1908.04682 (2019b), arXiv:1908.04682 [astro-ph.CO]
Original
1908
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. 886
Original
1909
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. 893
Original
1909
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 101
Original
1910
Earlier work this paper cites.
A. S. Bolton, S. Burles, L. V. E. Koopmans, T. Treu, R. Gavazzi, L. A. Moustakas, R. Wayth, and D. J. Schlegel, The Sloan Lens ACS Survey. V. The Full ACS Strong-Lens Sample, ApJ 682
Original
1931
Earlier work this paper cites.
S. Vegetti, L. V. E. Koopmans, A. Bolton, T. Treu, and R. Gavazzi, Detection of a dark substructure through gravitational imaging, MNRAS 408
Original
1969
Earlier work this paper cites.
S. Vegetti et al. , Detection of a dark substructure through gravitational imaging, MNRAS 408
Original
1969
Earlier work this paper cites.
R. D. Peccei and H. R. Quinn, CP Conservation in the Presence of Instantons, Phys. Rev. Lett. 38
1977
Earlier work this paper cites.
F. Wilczek, Problem of Strong P P and T T Invariance in the Presence of Instantons, Phys. Rev. Lett. 40
1978
Earlier work this paper cites.
S. Weinberg, A New Light Boson?, Phys. Rev. Lett. 40
1978
Earlier work this paper cites.
J. Preskill, M. B. Wise, and F. Wilczek, Cosmology of the Invisible Axion, Phys. Lett. B120
1982
Earlier work this paper cites.
L. F. Abbott and P. Sikivie, A Cosmological Bound on the Invisible Axion, Phys. Lett. B120
1982
Earlier work this paper cites.
M. Dine and W. Fischler, The Not So Harmless Axion, Phys. Lett. B120
1982
Earlier work this paper cites.
G. E. Hinton and T. J. Sejnowski, Analyzing cooperative computation, Proceedings of the Fifth Annual Conference of the Cognitive Science Society, Rochester NY (1983)
1983
Earlier work this paper cites.
G. Steigman and M. S. Turner, Cosmological Constraints on the Properties of Weakly Interacting Massive Particles, Nucl. Phys. B 253
1985
Earlier work this paper cites.
M. W. Goodman and E. Witten, Detectability of Certain Dark Matter Candidates, Phys. Rev. D31
1985
Earlier work this paper cites.
A. K. Drukier, K. Freese, and D. N. Spergel, Detecting Cold Dark Matter Candidates, Phys. Rev. D33
1986
Earlier work this paper cites.
D. E. Rumelhart, G. E. Hinton and R. J. Williams, Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1. chap. Learning Internal Representations by Error Propagation, pp. 318–362., MIT Press, Cambridge, MA, USA (1986)
1986
Earlier work this paper cites.
G. Kauffmann, S. D. M. White, and B. Guiderdoni, The Formation and Evolution of Galaxies Within Merging Dark Matter Haloes, MNRAS 264
1993
Earlier work this paper cites.
S. Seitz, P. Schneider, and J. Ehlers, Light propagation in arbitrary spacetimes and the gravitational lens approximation, Classical and Quantum Gravity 11
1994
Earlier work this paper cites.
S.-J. Sin, Late time cosmological phase transition and galactic halo as Bose liquid, Phys. Rev. D50
1994
Earlier work this paper cites.
R. H. Brandenberger, Topological defects and structure formation, Int. J. Mod. Phys. A9
1994
Earlier work this paper cites.
A. Burkert, The Structure of Dark Matter Halos in Dwarf Galaxies, ApJL 447
1995
Earlier work this paper cites.
J.F. Navarro, C.S. Frenk and S.D.M. White, The Structure of Cold Dark Matter Halos, ApJ 462
1996
Earlier work this paper cites.
S. Mao and P. Schneider, Evidence for Substructure in lens galaxies?, MNRAS 295
1998
Earlier work this paper cites.
B. Moore, S. Ghigna, F. Governato, G. Lake, T. Quinn, J. Stadel, and P. Tozzi, Dark matter substructure within galactic halos, The Astrophysical Journal 524
1999
Earlier work this paper cites.
A. Klypin, A. V. Kravtsov, O. Valenzuela, and F. Prada, Where are the missing galactic satellites?, The Astrophysical Journal 522
1999
Earlier work this paper cites.
W. Hu, R. Barkana, and A. Gruzinov, Cold and fuzzy dark matter, Phys. Rev. Lett. 85
2000
Earlier work this paper cites.
K. Yi, Y. Guo, Y. Fan, J. Hamann, and Y. G. Wang, CosmoVAE: Variational Autoencoder for CMB Image Inpainting, arXiv e-prints , arXiv:2001.11651 (2020), arXiv:2001.11651 [eess.IV]
Original
2001
Earlier work this paper cites.
N. Dalal and C.S. Kochanek, Direct Detection of CDM Substructure, ApJ 572
2002
Earlier work this paper cites.
F. List and G. F. Lewis, A unified framework for 21 cm tomography sample generation and parameter inference with progressively growing GANs, MNRAS 493
Original
2002
Earlier work this paper cites.
M. P. Silverman and R. L. Mallett, Dark matter as a cosmic Bose-Einstein condensate and possible superfluid, Gen. Rel. Grav. 34
2002
Earlier work this paper cites.
G. E. Hinton, Training products of experts by minimizing contrastive divergence, Neural Computation 14
2002
Earlier work this paper cites.
F. Froborg and A. R. Duffy, Annual Modulation in Direct Dark Matter Searches, arXiv e-prints , arXiv:2003.04545 (2020), arXiv:2003.04545 [astro-ph.CO]
Original
2003
Earlier work this paper cites.
J. Rico, Gamma-Ray Dark Matter Searches in Milky Way Satellites—A Comparative Review of Data Analysis Methods and Current Results, Galaxies 8
Original
2003
Earlier work this paper cites.

Similar

S. Mishra-Sharma, K. Van Tilburg, and N. Weiner, Power of halometry, Phys. Rev. D 102
Original
2003
Cited alongside, same era.
F. Farsian, N. Krachmalnicoff, and C. Baccigalupi, Foreground model recognition through Neural Networks for CMB B-mode observations, JCAP 07
Original
2003
Cited alongside, same era.
D. Clowe, A. Gonzalez, and M. Markevitch, Weak lensing mass reconstruction of the interacting cluster 1E0657-558: Direct evidence for the existence of dark matter, Astrophys. J. 604
2004
Cited alongside, same era.
R. Canameras, S. Schuldt, S. H. Suyu, S. Taubenberger, T. Meinhardt, L. Leal-Taixe, C. Lemon, K. Rojas, and E. Savary, HOLISMOKES – II. Identifying galaxy-scale strong gravitational lenses in Pan-STARRS using convolutional neural networks, arXiv e-prints , arXiv:2004.13048 (2020), arXiv:2004.13048 [astro-ph.GA]
Original
2004
Cited alongside, same era.

Then

K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, arXiv e-prints , arXiv:1512.03385 (2015), arXiv:1512.03385 [cs.CV]
Original
2015
Later among the works it cites.
A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey, Adversarial Autoencoders, arXiv e-prints , arXiv:1511.05644 (2015), arXiv:1511.05644 [cs.LG]
Original
2015
Later among the works it cites.
MAGIC Collaboration, Limits to dark matter annihilation cross-section from a combined analysis of MAGIC and Fermi-LAT observations of dwarf satellite galaxies, J. Cosmol. Astropart. Phys. 2016
Original
2016
Later among the works it cites.
Planck Collaboration, Planck 2015 results. XIII. Cosmological parameters, A&A 594
Original
2016
Later among the works it cites.

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

A. V. Sadr and F. Farsian, Inpainting via Generative Adversarial Networks for CMB data analysis, arXiv e-prints , arXiv:2004.04177 (2020), arXiv:2004.04177 [astro-ph.CO]

Original

2004

Cited alongside, same era.

S. Yoshiura, H. Shimabukuro, K. Hasegawa, and K. Takahashi, Predicting 21cm-line map from Lyman α \alpha emitter distribution with Generative Adversarial Networks, arXiv e-prints , arXiv:2004.09206 (2020), arXiv:2004.09206 [astro-ph.CO]

Original

2004

Cited alongside, same era.

L.V.E. Koopmans, Gravitational imaging of cold dark matter substructures, MNRAS 363

2005

Cited alongside, same era.

S. Varma, M. Fairbairn, and J. Figueroa, Dark Matter Subhalos, Strong Lensing and Machine Learning, arXiv e-prints , arXiv:2005.05353 (2020), arXiv:2005.05353 [astro-ph.CO]

Original

2005

Cited alongside, same era.

K. Kannike, M. Raidal, H. Veermäe, A. Strumia, and D. Teresi, Dark Matter and the XENON1T electron recoil excess, arXiv e-prints , arXiv:2006.10735 (2020), arXiv:2006.10735 [hep-ph]

Original

2006

Cited alongside, same era.

J. Buch, M. A. Buen-Abad, J. Fan, and J. S. Chau Leung, Galactic Origin of Relativistic Bosons and XENON1T Excess, arXiv e-prints , arXiv:2006.12488 (2020), arXiv:2006.12488 [hep-ph]

Original

2006

Cited alongside, same era.

M. C. Romao, N. Castro, and R. Pedro, Finding New Physics without learning about it: Anomaly Detection as a tool for Searches at Colliders, arXiv e-prints , arXiv:2006.05432 (2020), arXiv:2006.05432 [hep-ph]

Original

2006

Cited alongside, same era.

A. Çağan Şengül et al. , Quantifying the Line-of-Sight Halo Contribution to the Dark Matter Convergence Power Spectrum from Strong Gravitational Lenses, arXiv e-prints , arXiv:2006.07383 (2020), arXiv:2006.07383 [astro-ph.CO]

Original

2006

Cited alongside, same era.

R. G. Carlberg, Modeling GD-1 Gaps in a Milky Way Potential, Astrophys. J. 820

Original

2016