The dynamic mass of the Coma cluster resulting from deep learning

  • Zwicky, F. Die rotverschiebung von extragalaktischen nebeln. Helv. Phys. Deed 6110–127 (1933).

    MATH ADS Google Scholar

  • Biviano, A. Our best friend, the Coma cluster (a historical review). In Unraveling Coma Berenices: A New Vision of an Old Cluster1 (eds Mazure, A. et al.) (1998).

  • Kubo, JM et al. The mass of the Coma cluster from a faint lens in the Sloan Digital Sky Survey. Astrophysic. J 6711466–1470 (2007).

    Article on Google Scholar Ads

  • Gavazzi, R. et al. A weak lens study of the Coma cluster. Star. Astrophysic. 498L33–L36 (2009).

    Article on Google Scholar Ads

  • Hughes, JP The mass of the Coma cluster: combined X-ray and optical results. Astrophysic. J 33721–33 (1989).

    Article on Google Scholar Ads

  • The, LS & White, SDM The mass of the Coma cluster. Star. J 921248-1253 (1986).

    Article on Google Scholar Ads

  • Geller, MJ, Diaferio, A. & Kurtz, MJ The mass profile of the Coma galaxy cluster. Astrophysic. J. Lett. 517L23–L26 (1999).

    Article on Google Scholar Ads

  • Falco, M. et al. A new method for measuring the mass of galaxy clusters. Mon. No. R.Astron. Soc. 4421887–1896 (2014).

    Article on Google Scholar Ads

  • Allen, SW, Evrard, AE & Mantz, AB Cosmological parameters from observations of galaxy clusters. Ann. Reverend Astron. Astrophysic. 49409–470 (2011).

    Article on Google Scholar Ads

  • Dodelson, S. et al. Dark energy cosmic visions: science. Preprint at https://doi.org/10.48550/arXiv.1604.07626 (2016).

  • Binney, J. & Tremaine, S. Galactic Dynamics Flight. 13 (Princeton Univ. Press, 2011).

  • Old, L. et al. Project of massive reconstruction of clusters of galaxies. III. The impact of dynamic substructure on cluster mass estimates. Mon. No. R.Astron. Soc. 475853–866 (2018).

    Article on Google Scholar Ads

  • Wojtak, R. et al. Project of massive reconstruction of clusters of galaxies. IV. Understand the effects of imperfect membership on cluster mass estimation. Mon. No. R.Astron. Soc. 481324–340 (2018).

    Article on Google Scholar Ads

  • Ho, M. et al. A robust and efficient deep learning method for dynamic mass measurements of galaxy clusters. Astrophysic. J 88725 (2019).

    Article on Google Scholar Ads

  • Ho, M., Farahi, A., Rau, MM & Trac, H. Approximate Bayesian Uncertainties in Deep Learning Dynamical Mass Estimates of Galaxy Clusters. Astrophysic. J 908204 (2021).

    Article on Google Scholar Ads

  • Kodi Ramanah, D., Wojtak, R., Ansari, Z., Gall, C. & Hjorth, J. Dynamic Mass Inference of Galaxy Clusters with Neural Flows. Mon. No. R.Astron. Soc. 4991985–1997 (2020).

    Article on Google Scholar Ads

  • Scott, DW Multivariate Density Estimation: Theory, Practice and Visualization (Wiley, 2015).

  • Gal, Y. & Ghahramani, Z. Bayesian convolutional neural networks with approximate Bernoulli variational inference. Preprint at https://doi.org/10.48550/arXiv.1506.02158 (2015).

  • LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. proc. IEEE 862278–2324 (1998).

    Google Scholar article

  • LeCun, Y., Bengio, Y. & Hinton, G. Deep Learning. Nature 521436–444 (2015).

    Article on Google Scholar Ads

  • Gal, Y. & Ghahramani, Z. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. In proc. 33rd International Conference on Machine Learning (eds Balcan, MF & Weinberger, KQ) 1050-1059 (PMLR, 2016); https://proceedings.mlr.press/v48/gal16.html

  • Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: A simple way to prevent neural networks from overfitting. J.Mach. Learn. Res. 151929-1958 (2014).

    MathSciNet MATHGoogle Scholar

  • Kodi Ramanah, D., Wojtak, R. & Arendse, N. Simulation-Based Dynamic Mass Inference of Galaxy Clusters with 3D Convolutional Neural Networks. Mon. No. R.Astron. Soc. 5014080–4091 (2021).

    Article on Google Scholar Ads

  • Ishiyama, T. et al. The Uchuu simulations: Data Release 1 and dark matter halo concentrations. Mon. No. R.Astron. Soc. 5064210–4231 (2021).

    Article on Google Scholar Ads

  • Klypin, A., Yepes, G., Gottlöber, S., Prada, F. & Heß, S. MultiDark Simulations: The Story of Dark Matter Halo Concentrations and Density Profiles. Mon. No. R.Astron. Soc. 4574340–4359 (2016).

    Article on Google Scholar Ads

  • Behroozi, P., Wechsler, RH, Hearin, AP & Conroy, C. UNIVERSEMACHINE: The correlation between galaxy growth and dark matter halo assembly from z = 0–10. Mon. No. R.Astron. Soc. 4883143–3194 (2019).

    Article on Google Scholar Ads

  • van Dokkum, PG & van der Marel, RP The star formation epoch of the most massive early-type galaxies. Astrophysic. J 65530–50 (2007).

    Article on Google Scholar Ads

  • Alam, S. et al. The eleventh and twelfth releases of data from the Sloan Digital Sky Survey: final data from SDSS-III. Astrophysic. J. Suppl. Ser. 21912 (2015).

    Article on Google Scholar Ads

  • Abell, GO, Corwin, J., Harold, G. & Olowin, RP A catalog of rich galaxy clusters. Astrophysic. J. Suppl. Ser. 701–138 (1989).

    Article on Google Scholar Ads

  • Maraston, C. Evolutionary synthesis of populations: models, analysis of ingredients and application toz galaxies. Mon. No. R.Astron. Soc. 362799–825 (2005).

    Article on Google Scholar Ads

  • Łokas, EL & Mamon, GA Distribution of dark matter in the Coma cluster from the kinematics of galaxies: breaking mass-anisotropy degeneracy. Mon. No. R.Astron. Soc. 343401–412 (2003).

    Article on Google Scholar Ads

  • Collaboration Planck et al. Planck results 2013. XVI. Cosmological parameters. Star. Astrophysic. 571A16 (2014).

    Google Scholar article

  • Villaescusa-Navarro, F. et al. Robust marginalization of baryonic effects for field-level cosmological inference. Preprint at https://doi.org/10.48550/arXiv.2109.10360 (2021).

  • Bishop, MA Mixing density networksTechnical Report NCRG/94/004 (Aston Univ., 1994); https://publications.aston.ac.uk/id/eprint/373/1/NCRG_94_004.pdf

  • Collaboration Planck et al. Planck results 2015. XXIV. Cosmology of Sunyaev–Zeldovich cluster counts. Star. Astrophysic. 594A24 (2016).

    Google Scholar article

  • Behroozi, PS, Wechsler, RH & Wu, H.-Y. The ROCKSTAR phase-space time halo seeker and cluster nuclei velocity shifts. Astrophysic. J 762109 (2013).

    Article on Google Scholar Ads

  • Navarro, JF, Frenk, CS & White, SDM A universal density profile from hierarchical clustering. Astrophysic. J 490493–508 (1997).

    Article on Google Scholar Ads

  • About Johnnie Gross

    Check Also

    Sun-like star discovered orbiting closest black hole to Earth

    Imagine if our Sun were orbiting a black hole, perhaps spiraling into it. Admittedly, the …