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- Moritz Munchmeyer

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I'm a cosmologist working at the interface of cosmological theory and data analysis. The main goal of my research is to find new ways to test and constrain fundamental physics with cosmological data. The initial perturbations of the universe are believed to have been created by a quantum process, and the statistical properties of the resulting density field contain information about the ultra high energy physics of the primordial universe.

Recently I have worked on using the cross correlation of the kinetic Sunyaev Zeldovich effect (kSZ) in the CMB and galaxy surveys to reconstruct the large-scale velocity field of the universe. With my collaborators I have proposed an optimal estimator for this reconstruction and shown that it has high signal-to-noise in upcoming experiments. This velocity reconstruction is a new source of information for cosmologists that has already found multiple interesting applications. In particular I have led the development of a method to constrain primordial non-Gaussianity of the local type using the kSZ velocity field and a tracer of galaxies, which will lead to improved constraints with respect to previous methods on the important fnl parameter.

I also investigate how machine learning methods can be used to solve technical problems in cosmological parameter estimation. It appears likely that machine learning will be a key technology to deal with the non-Gaussian, non-linear physics and large computational challenges of future experiments. However, current work on machine learning in cosmology is often basic, for example using convolutional neural networks developed for image analysis as a "black box" to extract parameters from idealized simulations. I believe that machine learning, combined with forward simulations, will come to dominate cosmological data analysis, but in the form of specialized well-understood tools used at different steps of the analysis. In this way machine learning will be combined with classical statistical methods. In my recent publication 1905.05846, I developed a method to perform Wiener filtering of Gaussian fields with an innovative neural network architecture. My neural network, after training, is able to Wiener filter CMB maps a factor of a 1000 times faster than the standard conjugate gradient method, with minimal loss of optimality. Wiener filtering is the computational bottleneck of optimal analyses of near-Gaussian random fields in cosmology, including power spectrum or non-Gaussianity estimation. In addition to its practical value, the paper demonstrates how physical insight can be included in machine learning analysis.

Another area of my research is finding or constraining massive particles during inflation. In principle the density perturbations created by inflation are sensitive to much higher masses than could ever be probed with a terrestrial collider and thus "cosmological collider physics" could give unique insights into ultra high energy particle physics. In many cases such a measurement would require futuristic experimental data and I have worked on forecasting the sensitivity of different experimental setups. For some theoretical models, even current CMB data can be used to obtain interesting and new constraints on massive states. In previous and ongoing work I am searching for such signatures in Planck CMB data. Cosmological collider physics is still a young field and I believe there will be interesting developments in the years and decades to come.

I am member of the Planck experiment, the Simons Observatory, and CHIME and have contributed data analysis and forecasts to these collaborations. I am also contributing to the Modern Inflationary Cosmology collaboration within the Simons Foundation Origins of the Universe Initiative.

- 2014, 2015 CNES postdoc, Institute d'Astrophysique Paris (IAP)
- 2013 Lagrange Fellowship, Institut Lagrange de Paris, Institute d'Astrophysique Paris (IAP)

- Higher N-point function data analysis techniques for heavy particle production and WMAP results Moritz Münchmeyer, Kendrick M. Smith Phys. Rev. D 100, 123511 (2019) arXiv: 1910.00596
- Transverse Velocities with the Moving Lens Effect Selim C. Hotinli, Joel Meyers, Neal Dalal, Andrew H. Jaffe, Matthew C. Johnson, James B. Mertens, Moritz Münchmeyer, Kendrick M. Smith, Alexander van Engelen arXiv: 1812.03167, Phys. Rev. Lett., no 123, 061301
- Primordial gravitational wave phenomenology with polarized Sunyaev Zel'dovich Tomography Anne-Sylvie Deutsch, Emanuela Dimastrogiovanni, Matteo Fasiello, Matthew C. Johnson, Moritz Münchmeyer, Phys. Rev. D 100, 083538 (2019), arXiv: 1810.09463
- Constraining local non-Gaussianities with kSZ tomography Moritz Münchmeyer, Mathew S. Madhavacheril, Simone Ferraro, Matthew C. Johnson, Kendrick M. Smith, Phys. Rev. D 100, 083508 (2019), arXiv: 1810.13424
- Polarized Sunyaev Zel'dovich tomography Anne-Sylvie Deutsch, Matthew C. Johnson, Moritz Münchmeyer, Alexandra Terrana, Journal of Cosmology and Astroparticle Physics, Volume 2018, April 2018, arXiv: 1705.08907
- Reconstruction of the remote dipole and quadrupole fields from the kinetic Sunyaev Zel'dovich and polarized Sunyaev Zel'dovich effects Anne-Sylvie Deutsch, Emanuela Dimastrogiovanni, Matthew C. Johnson, Moritz Münchmeyer, Alexandra Terrana arXiv: 1707.08129, Phys.Rev. D98 (2018) no.12, 123501
- Prospects for cosmological collider physics, P. Daniel Meerburg, Moritz Munchmeyer, Julian B. Muñoz and Xingang Chen, Journal of Cosmology and Astroparticle Physics, Volume 2017, March 2017, arXiv: 1610.06559
- A bright millisecond-duration radio burst from a Galactic magnetar CHIME FRB collaboration arXiv: 2005.10324
- Packed Ultra-wideband Mapping Array (PUMA): A Radio Telescope for Cosmology and Transients arXiv: 1907.12559
- Research and Development for HI Intensity Mapping Cosmic Vision 21cm collaboration arXiv: 1907.13090
- CMB-S4 Science Case, Reference Design, and Project Plan S4 collaboration, arXiv: 1907.04473
- Fast Wiener filtering of CMB maps with Neural Networks Moritz Münchmeyer, Kendrick M. Smith, arXiv: 1905.05846, Neurips 2019 physics workshop
- Planck 2018 results. IX. Constraints on primordial non-Gaussianity, Planck collaboration, arXiv: 1905.05697
- Cosmic Visions Dark Energy: Inflation and Early Dark Energy with a Stage-II Hydrogen Intensity Mapping Experiment, Cosmic Visions 21 cm Collaboration Cosmic Visions 21 cm Collaboration, arXiv: 1810.09572
- KSZ tomography and the bispectrum Kendrick M. Smith, Mathew S. Madhavacheril, Moritz Münchmeyer, Simone Ferraro, Utkarsh Giri, Matthew C. Johnson, arXiv: 1810.13423, accepted by PRD
- The Simons Observatory Collaboration, The Simons Observatory: Science goals and forecasts arXiv: 1808.07445
- Planck 2018 results X. Constraints on inflation Planck Collaboration arXiv: 1807.06211
- Planck 2018 results I. Overview and the cosmological legacy of Planck Planck Collaboration arXiv: 1807.06205

- Machine learning in CMB physics, Max Planck Institute for Astrophysics
- Machine learning in CMB physics, CITA Toronto
- Machine learning in CMB physics, Gordon Research Conference String Theory and Cosmology
- Machine learning in CMB physics, CosmoGold 2019 (IAP)
- Wiener Filtering with Neural Networks and its applications, AIcosmos 2019
- Sample variance cancellation with CMB and lensing, The CMB in HD, workshop at Simons Center for Computational Astrophysics
- Measuring fnl with kSZ velocity measurements, Simons Origins of the Universe Meeting, IAS Princeton
- Constraining fnl with kSZ tomography, The Nonlinear Universe 2018 conference
- kSZ tomography for cosmology, CCA Flatiron Institute New York
- kSZ tomography for cosmology, Cambridge University
- Velocity reconstruction with the kSZ effect, CITA
- kSZ tomography for cosmology, Imperial College London
- UV signatures in cosmological data, Stanford University
- UV signatures in cosmological data, Berkeley University

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