Feldman, Hume A.

Professor, Chair, Physics & Astronomy
Primary office:
785-864-4626
Malott Hall
Room 1082
1251 Wescoe Hall Dr.
Lawrence, KS 66045-7582


KU Faculty: 1996 to presentResearch Prof.,Gravity Group, Department of Physics, Princeton University, 1994 - 1996Research Associate: Department of Physics, University of Michigan, 1991 - 1994Postdoctoral Fellow, CITA, University of Toronto, Canada, 1989-1991Ph.D. Institute for Theoretical Physics, Stony Brook, NY, 1989MA, Institute for Theoretical Physics, Stony Brook, NY, 1986BA honors, University of California, Santa Cruz, 1984

Research

My research interests include Astrophysics and Cosmology. I study the large scale structure of the universe, developing and implementing dynamical and statistical tools. In particular I analyze proper distance surveys to find the distribution of mass in the Universe. I am looking at the kinetic Sunayev-Zeldovich signature of clusters to study dark energy and the evolution of the Hubble parameter and the distribution, properties and substructure of void-supercluster network. I also study the effects of neutrino mass on the power spectrum of mass fluctuation in the linear and non-linear regimes.

Selected Publications

Watkins, R. & Feldman, H. (2015). An Unbiased Estimator of Peculiar Velocity with Gaussian Distributed Errors for Precision Cosmology. Monthly Notices of the Royal Astronomical Society, 450(2), 1868-1873. DOI: 10.1093/mnras/stv651 http://mnras.oxfordjournals.org/content/450/2/1868
Watkins, R. & Feldman, H. (2015). Large-scale bulk flows from the Cosmicflows-2 catalogue. Monthly Notices of the Royal Astronomical Society, 447, 132-139. DOI:10.1093/mnras/stu2414
Agarwal, S. Abdalla, F. Feldman, H. Lahav, O. & Thomas, S. (2014). PkANN - II. A non-linear matter power spectrum interpolator developed using artificial neural networks. Monthly Notices of the Royal Astronomical Society, 439, 2102-2121. DOI:10.1093/mnras/stu090
Feldman, H. A. (2013). PKANN: A non-linear matter power spectrum interpolator developed using artificial neural networks. http://zuserver2.star.ucl.ac.uk/~fba/PkANN/
Agarwal, S. & Feldman, H. A. (2013). The Cosmic Mach Number: Comparison from Observations, Numerical Simulations and Nonlinear Predictions. MNRAS, 432(1), 307-317. DOI:10.1093/mnras/stt464
Turnbull, S. J., Hudson, M. J., Feldman, H. A., Hicken, M. Kirshner, R. P., & Watkins, R. (2012). Cosmic flows in the nearby universe from Type Ia Supernovae. MNRAS, 420, 447-454.
Macaulay, E. Feldman, H. A., Ferreira, P. G., Jaffe, A. H., Agarwal, S. Hudson, M. J., & Watkins, R. (2012). Power Spectrum Estimation from Peculiar Velocity Catalogues: Power spectra from peculiar velocity catalogues. Monthly Notices of the Royal Astronomical Society, 425(3), 1709-1717. DOI:10.1111/J.1365-2966.2012.21629.X
Agarwal, S. Abdalla, F. B., Feldman, H. A., Lahav, O. & Thomas, S. A. (2011). Constraining Cosmological Parameters, Including Neutrino Mass, Using N-body Large Scale Simulations and Artificial Neural Networks. In American Astronomical Society, AAS Meeting #219 (pp. #143.09).
Macaulay, E. Feldman, H. A., Ferreira, P. G., Hudson, M. J., & Watkins, R. (2011). A Slight Excess of Large Scale Power from Moments of the Peculiar Velocity Field: Large-scale power from velocity moments. MNRAS (Monthly Notices of the Royal Astronomical Society), 414(1), 621-626. DOI:10.1111/j.1365-2966.2011.18426.x
Feldman, H. A., Watkins, R. & Hudson, M. J. (2010). Cosmic Flows on 100 Mpc/h Scales: Standardized Minimum Variance Bulk Flow, Shear and Octupole Moments: Cosmic flows minimum variance moments. MNRAS (Monthly Notices of the Royal Astronomical Society), 407(4), 2328-2338. DOI:10.1111/j.1365-2966.2010.17052.x
Juszkiewicz, R. Feldman, H. A., Fry, J. N., & Jaffe, A. H. (2010). Nonlinear Effects in the Amplitude of Cosmological Density Fluctuations. JCAP, 02, 021.
Agarwal, S. & Feldman, H. A. (2010). The Effect of Massive Neutrinos on the Matter Power Spectrum: Massive neutrinos and matter power spectrum. MNRAS (Monthly Notices of the Royal Astronomical Society), 410, 1647-1654. DOI:10.1111/j.1365-2966.2010.17546.x
Watkins, R. Feldman, H. A., & Hudson, M. J. (2009). Consistently Large Cosmic Flows on Scales of 100 h-1Mpc: a Challenge for the Standard ΛCDM Cosmology. MNRAS, 392, 743-756.
Colberg, J. M., Pearce, F. Foster, C. Platen, E. Brunino, R. Neyrinck, M. Basilakos, S. Fairall, A. Feldman, H. A., Gottloeber, S. Hahn, O. Hoyle, F. Mueller, V. Nelson, L. Plionis, M. Porciaini, C. Shandarin, S. Vogeley, M. S., & van de Weygaert, R. (2008). The Aspen-Amsterdam Void Finder Comparison Project. MNRAS, 387, 933-944.
Shandarin, S. Feldman, H. A., Heitmann, K. & Habib, S. (2006). Shapes and Sizes of Voids in the LCDM Universe: Excursion Set Approach. MNRAS, 367, 1629-1640.
Feldman, H. A., Juszkiewicz, R. Ferreira, P. Davis, M. Gaztanaga, E. Fry, J. N., Jaffe, A. Chambers, S. W., da Costa, L. Bernandi, M. Giovanelli, R. Haynes, M. P., & Wegner, G. (2003). An estimate of Ωm without priors. ApJ, 596, L131-L134.
Feldman, H. A., Frieman, J. A., Fry, J. N., & Scoccimarro, R. (2001). Constraints on Galaxy Bias, Ωm, and Primordial Non-Gaussianity from the IRAS-PSCz Survey Bispectrum. PRL, 86, 1434-1437.
Scoccimarro, R. Feldman, H. A., Frieman, J. & Fry, J. (2001). The Bispectrum of Redshift Catalogs. ApJ, 546, 652-664.
Juszkiewicz, R. Ferreira, P. G., Feldman, H. A., Jaffe, A. H., & Davis, M. (2000). Relative Velocities of Galaxies Suggest a Low Density Universe. Science, 287, 109-112.
Feldman, H. A., & Watkins, R. (1995). Interpreting New Data on Large Scale Bulk Flows. ApJL, 453, L72–76.
Feldman, H. A., Kaiser, N. & Peacock, J. (1994). Power Spectrum Analysis of Three-Dimensional Redshift Surveys. ApJ, 426, 23-37.
Feldman, H. A., & Watkins, R. (1994). Theoretical Expectations for Bulk Flows in Large Scale Surveys. ApJL, 430, L17-20.
Sahni, V. Feldman, H. A., & Stebbins, A. (1992). Loitering Universes. ApJ, 385, 1-8.
Brandenberger, R. Feldman, H. A., & Mukhanov, V. F. (1992). Theory of Cosmological Perturbations. Part I: Classical Perturbations. Phys. Rep., 215, 206-256.

Click on the images below to learn more about the individual topics.

 Redshift Distortions

 

                           Voids and Superclusters

 

​  Minimal Variance Weights

 


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