Latest Research
obscura: A modular C++ tool and library for the direct detection of (sub-GeV) dark matter via nuclear and electron recoils
For the interpretation of past and future direct searches for dark matter (DM) particles, it is important to be able to provide accurate predictions for event rates and spectra under a variety of possible and viable assumptions in a computationally efficient way.
High Energy Physics - Phenomenology
Ab initio predictions link the neutron skin of ${}^{208}$Pb to nuclear forces
This work demonstrates that nuclear forces constrained to light systems extrapolate reliably to even the heaviest nuclei, and that we can make quantitative predictions across the nuclear landscape.
Nuclear Theory High Energy Astrophysical Phenomena Nuclear Experiment
Morphing quantum codes
We introduce a morphing procedure that can be used to generate new quantum codes from existing quantum codes.
Quantum Physics
Probabilistic neural networks for predicting energy dissipation rates in geophysical turbulent flows
Motivated by oceanographic observational datasets, we propose a probabilistic neural network (PNN) model for calculating turbulent energy dissipation rates from vertical columns of velocity and density gradients in density stratified turbulent flows.
Fluid Dynamics Atmospheric and Oceanic Physics 76U60 (Primary) 86A05, 76F45 (Secondary)
Interpretable Machine Learning for Materials Design
Fueled by the widespread adoption of Machine Learning (ML) and the high-throughput screening of materials, the data-centric approach to materials design has asserted itself as a robust and powerful tool for the in-silico prediction of materials properties.
Materials Science
Sterile neutrino dark matter production in presence of non-standard neutrino self-interactions: an EFT approach
Our focus is on mixing with electron neutrinos, which is subject to constraints from several upcoming or running experiments like TRISTAN, ECHo, BeEST and HUNTER.
High Energy Physics - Phenomenology Cosmology and Nongalactic Astrophysics High Energy Physics - Experiment
Computation of Burgers Vectors from Elastic Strain and Lattice Rotation Data
A theoretical framework for computation of Burgers vectors from strain and lattice rotation data is presented, as well as implementation into a computer program to automate the process.
Materials Science
Using deep generative neural networks to account for model errors in Markov chain Monte Carlo inversion
shilevy/sgan_me
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We demonstrate that the SGAN is able to learn the spatial statistics of the model error and that suitable representations of both the subsurface model and model error can be recovered by MCMC.
Geophysics
Combining processing throughput, low latency and timing accuracy in experiment control
We ported the firmware of the ARTIQ experiment control infrastructure to an embedded system based on a commercial Xilinx Zynq-7000 system-on-chip.
Instrumentation and Detectors Quantum Physics
Limiting the Search Space in Optimal Quantum Circuit Mapping
Results confirm that, by limiting the search space, optimal solutions can be determined for instances that timeouted before or speed-ups of up to three orders of magnitude can be achieved.
Quantum Physics Emerging Technologies