M. Link

ORCID: 0000-0001-6996-6258
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About
Contact & Profiles
Research Areas
  • Particle physics theoretical and experimental studies
  • High-Energy Particle Collisions Research
  • Quantum Chromodynamics and Particle Interactions
  • Cold Atom Physics and Bose-Einstein Condensates
  • Atomic and Subatomic Physics Research
  • Quantum, superfluid, helium dynamics
  • Neutrino Physics Research
  • Dark Matter and Cosmic Phenomena
  • Cosmology and Gravitation Theories
  • Particle Detector Development and Performance
  • Computational Physics and Python Applications
  • Physics of Superconductivity and Magnetism
  • Distributed and Parallel Computing Systems
  • Generative Adversarial Networks and Image Synthesis
  • Nuclear Physics and Applications
  • Nuclear reactor physics and engineering
  • Magnetic and transport properties of perovskites and related materials
  • Advanced Frequency and Time Standards
  • Machine Learning in Materials Science
  • Quantum many-body systems
  • Advanced Neural Network Applications
  • Human Pose and Action Recognition

Karlsruhe Institute of Technology
2023-2025

A. Alikhanyan National Laboratory
2024

Institute of High Energy Physics
2023-2024

University of Antwerp
2024

University of Bonn
2018-2023

We determine the phase diagram of strongly correlated fermions in crossover from Bose-Einstein condensates molecules (BEC) to Cooper pairs (BCS) utilizing an artificial neural network. By applying advanced image recognition techniques momentum distribution fermions, a quantity which has been widely considered as featureless for providing information about condensed state, we measure critical temperature and show that it exhibits maximum on bosonic side crossover. Additionally, backanalyze...

10.1103/physrevlett.130.203401 article EN Physical Review Letters 2023-05-16

The authors investigate the response of a strongly-interacting superfluid Fermi gas to spin-flip and corresponding change interaction strength. They observe decay revival condensed fraction shape oscillations gas.

10.1103/physrevresearch.3.023205 article EN cc-by Physical Review Research 2021-06-11

Cold atom experiments commonly use broad magnetic Feshbach resonances to manipulate the interaction between atoms. In order induce quantum dynamics by a change in strength, rapid (∼μs) field changes over several tens of Gauss are required. Here, we present compact design coil and its control circuit for up 36 G 3 µs. The setup comprises two concentric solenoids with minimal space requirements, which can be readily added existing apparatuses. This makes observation non-equilibrium physics accessible.

10.1063/5.0049518 article EN Review of Scientific Instruments 2021-09-01

We study the critical temperature of superfluid phase transition strongly interacting fermions in crossover regime between a Bardeen-Cooper-Schrieffer superconductor and Bose-Einstein condensate dimers. To this end, we employ technique unsupervised machine learning using an autoencoder neural network, which directly apply to time-of-flight images fermions. extract from trend changes data distribution revealed latent space bottleneck.

10.1103/physreva.108.063303 article EN Physical review. A/Physical review, A 2023-12-04

We present a novel approach to estimating physical properties of objects from video. Our consists physics engine and correction estimator. Starting the initial observed state, object behavior is simulated forward in time. Based on behavior, estimator then determines refined parameters for each object. The method can be iterated increased precision. generic, as it allows use an arbitrary-not necessarily differentiable-physics For latter, we evaluate both gradient-free hyperparameter...

10.1109/ijcnn55064.2022.9891961 article EN 2022 International Joint Conference on Neural Networks (IJCNN) 2022-07-18

We study the critical temperature of superfluid phase transition strongly-interacting fermions in crossover regime between a Bardeen-Cooper-Schrieffer (BCS) superconductor and Bose-Einstein condensate (BEC) dimers. To this end, we employ technique unsupervised machine learning using an autoencoder neural network which directly apply to time-of-flight images fermions. extract from trend changes data distribution revealed latent space bottleneck.

10.48550/arxiv.2310.15989 preprint EN cc-by arXiv (Cornell University) 2023-01-01

We determine the phase diagram of strongly correlated fermions in crossover from Bose-Einstein condensates molecules (BEC) to Cooper pairs (BCS) utilizing an artificial neural network. By applying advanced image recognition techniques momentum distribution fermions, a quantity which has been widely considered as featureless for providing information about condensed state, we measure critical temperature and show that it exhibits maximum on bosonic side crossover. Additionally, back-analyze...

10.48550/arxiv.2310.16006 preprint EN cc-by arXiv (Cornell University) 2023-01-01
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