The dynamical mass of the Coma cluster from deep learning

Zwicky, F. Die rotverschiebung von extragalaktischen nebeln. Helv. Phys. Acta 6, 110–127 (1933).ADS 
MATH 

Google Scholar 
Biviano, A. Our greatest buddy, the Coma cluster (a historic evaluation). In Untangling Coma Berenices: A New Vision of an Old Cluster, 1 (eds Mazure, A. et al.) (1998).Kubo, J. M. et al. The mass of the Coma cluster from weak lensing in the Sloan Digital Sky Survey. Astrophys. J. 671, 1466–1470 (2007).ADS 
Article 

Google Scholar 
Gavazzi, R. et al. A weak lensing examine of the Coma cluster. Astron. Astrophys. 498, L33–L36 (2009).ADS 
Article 

Google Scholar 
Hughes, J. P. The mass of the Coma cluster: mixed X-ray and optical outcomes. Astrophys. J. 337, 21–33 (1989).ADS 
Article 

Google Scholar 
The, L. S. & White, S. D. M. The mass of the Coma cluster. Astron. J. 92, 1248–1253 (1986).ADS 
Article 

Google Scholar 
Geller, M. J., Diaferio, A. & Kurtz, M. J. The mass profile of the Coma galaxy cluster. Astrophys. J. Lett. 517, L23–L26 (1999).ADS 
Article 

Google Scholar 
Falco, M. et al. A brand new methodology to measure the mass of galaxy clusters. Mon. Not. R. Astron. Soc. 442, 1887–1896 (2014).ADS 
Article 

Google Scholar 
Allen, S. W., Evrard, A. E. & Mantz, A. B. Cosmological parameters from observations of galaxy clusters. Annu. Rev. Astron. Astrophys. 49, 409–470 (2011).ADS 
Article 

Google Scholar 
Dodelson, S. et al. Cosmic visions darkish power: science. Preprint at https://doi.org/10.48550/arXiv.1604.07626 (2016).Binney, J. & Tremaine, S. Galactic Dynamics Vol. 13 (Princeton Univ. Press, 2011).Old, L. et al. Galaxy Cluster Mass Reconstruction Project. III. The impression of dynamical substructure on cluster mass estimates. Mon. Not. R. Astron. Soc. 475, 853–866 (2018).ADS 
Article 

Google Scholar 
Wojtak, R. et al. Galaxy Cluster Mass Reconstruction Project. IV. Understanding the results of imperfect membership on cluster mass estimation. Mon. Not. R. Astron. Soc. 481, 324–340 (2018).ADS 
Article 

Google Scholar 
Ho, M. et al. A strong and environment friendly deep learning methodology for dynamical mass measurements of galaxy clusters. Astrophys. J. 887, 25 (2019).ADS 
Article 

Google Scholar 
Ho, M., Farahi, A., Rau, M. M. & Trac, H. Approximate Bayesian uncertainties on deep learning dynamical mass estimates of galaxy clusters. Astrophys. J. 908, 204 (2021).ADS 
Article 

Google Scholar 
Kodi Ramanah, D., Wojtak, R., Ansari, Z., Gall, C. & Hjorth, J. Dynamical mass inference of galaxy clusters with neural flows. Mon. Not. R. Astron. Soc. 499, 1985–1997 (2020).ADS 
Article 

Google Scholar 
Scott, D. W. Multivariate Density Estimation: Theory, Practice, and Visualization (Wiley, 2015).Gal, Y. & Ghahramani, Z. Bayesian convolutional neural networks with Bernoulli approximate variational inference. Preprint at https://doi.org/10.48550/arXiv.1506.02158 (2015).LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning utilized to doc recognition. Proc. IEEE 86, 2278–2324 (1998).Article 

Google Scholar 
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).ADS 
Article 

Google Scholar 
Gal, Y. & Ghahramani, Z. Dropout as a Bayesian approximation: representing mannequin uncertainty in deep learning. In Proc. thirty third International Conference on Machine Learning (eds Balcan, M. F. & Weinberger, Okay. Q.) 1050-1059 (PMLR, 2016); https://proceedings.mlr.press/v48/gal16.htmlSrivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: a easy method to stop neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014).MathSciNet 
MATH 

Google Scholar 
Kodi Ramanah, D., Wojtak, R. & Arendse, N. Simulation-based inference of dynamical galaxy cluster plenty with 3D convolutional neural networks. Mon. Not. R. Astron. Soc. 501, 4080–4091 (2021).ADS 
Article 

Google Scholar 
Ishiyama, T. et al. The Uchuu simulations: Data Release 1 and darkish matter halo concentrations. Mon. Not. R. Astron. Soc. 506, 4210–4231 (2021).ADS 
Article 

Google Scholar 
Klypin, A., Yepes, G., Gottlöber, S., Prada, F. & Heß, S. MultiDark simulations: the story of darkish matter halo concentrations and density profiles. Mon. Not. R. Astron. Soc. 457, 4340–4359 (2016).ADS 
Article 

Google Scholar 
Behroozi, P., Wechsler, R. H., Hearin, A. P. & Conroy, C. UNIVERSEMACHINE: the correlation between galaxy progress and darkish matter halo meeting from z = 0–10. Mon. Not. R. Astron. Soc. 488, 3143–3194 (2019).ADS 
Article 

Google Scholar 
van Dokkum, P. G. & van der Marel, R. P. The star formation epoch of the most huge early-type galaxies. Astrophys. J. 655, 30–50 (2007).ADS 
Article 

Google Scholar 
Alam, S. et al. The eleventh and twelfth information releases of the Sloan Digital Sky Survey: last information from SDSS-III. Astrophys. J. Suppl. Ser. 219, 12 (2015).ADS 
Article 

Google Scholar 
Abell, G. O., Corwin, J., Harold, G. & Olowin, R. P. A catalog of wealthy clusters of galaxies. Astrophys. J. Suppl. Ser. 70, 1–138 (1989).ADS 
Article 

Google Scholar 
Maraston, C. Evolutionary inhabitants synthesis: fashions, evaluation of the substances and software to high-z galaxies. Mon. Not. R. Astron. Soc. 362, 799–825 (2005).ADS 
Article 

Google Scholar 
Łokas, E. L. & Mamon, G. A. Dark matter distribution in the Coma cluster from galaxy kinematics: breaking the mass-anisotropy degeneracy. Mon. Not. R. Astron. Soc. 343, 401–412 (2003).ADS 
Article 

Google Scholar 
Planck Collaboration et al. Planck 2013 outcomes. XVI. Cosmological parameters. Astron. Astrophys. 571, A16 (2014).Article 

Google Scholar 
Villaescusa-Navarro, F. et al. Robust marginalization of baryonic results for cosmological inference at the area stage. Preprint at https://doi.org/10.48550/arXiv.2109.10360 (2021).Bishop, M. A. Mixture Density Networks Technical Report NCRG/94/004 (Aston Univ., 1994); https://publications.aston.ac.uk/id/eprint/373/1/NCRG_94_004.pdfPlanck Collaboration et al. Planck 2015 outcomes. XXIV. Cosmology from Sunyaev–Zeldovich cluster counts. Astron. Astrophys. 594, A24 (2016).Article 

Google Scholar 
Behroozi, P. S., Wechsler, R. H. & Wu, H.-Y. The ROCKSTAR phase-space temporal halo finder and the velocity offsets of cluster cores. Astrophys. J. 762, 109 (2013).ADS 
Article 

Google Scholar 
Navarro, J. F., Frenk, C. S. & White, S. D. M. A common density profile from hierarchical clustering. Astrophys. J. 490, 493–508 (1997).ADS 
Article 

Google Scholar 

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