Feldman, Hume A.

Professor, Chair, Physics & Astronomy
Primary office:
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


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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