Carnegie Mellon University

Recent Publications

Ho, M., Ntampaka, M., Rau, M.M. et al., The dynamical mass of the Coma cluster from deep learningNat Astron (2022)

Chakravati, P., Kuusela, M., Lei, J., Wasserman, L., Model-Independent Detection of New Physics Signals Using Interpretable Semi-Supervised Classifier Tests. arXiv:2102.07679 (2021)

Rau, M.M., Morrison, C.B., Schmidt, S.J., Wilson, S., Mandelbaum, R., Mao, Y.-Y. and LSST Dark Energy Science Collaboration, A Composite Likelihood Approach for Inference under Photometric Redshift Uncertainty. arXiv:2101.01184 (2021)

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)

Andrews, M., Alison, J., An, S., Bryant, P., Burkle, B., Gleyzer, S., Narain, M., Paulini, M., Poczos, B. and Usai, E., End-to-end jet classification of quarks and gluons with the CMS Open Data. Nucl. Instrum. Meth. A 977, 164304 (2020)

Yin, L., Ni, Y., Croft, R.A.C., Di Matteo, T., Bird, S. and Feng, Y., AI-Assisted Super-Resolution Cosmological Simulations. arXiv:2010:06608 (2020)

Huang. L., Croft, R.A.C. and Arora, H., Deep Forest: Neural Network reconstruction of the Lyman-alpha Forest. arXiv:2009.10673 (2020)

Lanusse, F., Mandelbaum, R., Ravanbakhsh, S., Li, C.-L., Freeman, P. and Poczos, B., Deep Generative Models for Galaxy Image Simulations. arXiv:2008.03833 (2020)

Lin, C.-H., Harnois-Déraps, J., Eifler, T., Pospisil, T., Mandelbaum, R., Lee, A.B., Singh, S., and LSST Dark Energy Science Collaboration, Non-Gaussianity in the weak lensing correlation function likelihood - implications for cosmological parameter biases. MNRAS 499(2):2977–2993 (2020)

Andrews, M., Paulini, M., Gleyzer, S. and Poczos, B., End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC. Comput. Softw. Big Sci. 4, no.1, 6 (2020)

Rau, M.M., Wilson, S., and Mandelbaum, R, Estimating redshift distributions using hierarchical logistic Gaussian processes. MNRAS 491(4):4768–4782 (2020)

Ho, M., Rau, M.M., Ntampaka, M., Farahi, A., Trac, H., and Póczos, B., A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters. Astrophys. J. 887 25 (2019)