@article{OtteBobkovaTrinscheketal.2022,
  author    = {Otte, Eileen and Bobkova, Valeria and Trinschek, Sarah and Rosales-Guzm{\´a}n, Carmelo and Denz, Cornelia},
  title     = {Single-shot all-digital approach for measuring the orbital angular momentum spectrum of light},
  series = {APL Photonics},
  volume    = {2022},
  journal   = {APL Photonics},
  number    = {7},
  doi       = {10.1063/5.0086536},
  pages     = {086105},
  year      = {2022},
  abstract  = {Light fields carrying orbital angular momentum (OAM) offer a broad variety of applications in which especially an accurate determination of the respective OAM spectrum, i.e., unraveling the content of OAM by its topological charge ℓ, has become a main subject. Even though various techniques have been proposed to measure the OAM spectrum of such modes, many of them fail if optical vortices have to be considered in perturbed or dynamically changing experimental systems. Here, we put forward a novel technique capable of determining the OAM spectrum of light by a single measurement shot, which specifically applies to those fields that have been distorted. Experimentally, our technique only requires to interfere the perturbed light field with a reference field. From the resulting intensity pattern, the accurate OAM spectrum is determined in an all-digital way. We demonstrate our novel approach by numerical simulations and a proof-of-concept experiment employing a model ball lens as an exemplary disturbing object.},
  language  = {en}
}
@article{HomrighausenHorsthemkePogorzelskietal.2023,
  author    = {Homrighausen, Jonas and Horsthemke, Ludwig and Pogorzelski, Jens and Trinschek, Sarah and Gl{\"o}sek{\"o}tter, Peter and Gregor, Markus},
  title     = {Edge-Machine-Learning-Assisted Robust Magnetometer Based on Randomly Oriented NV-Ensembles in Diamond},
  series = {Sensors},
  volume    = {23},
  journal   = {Sensors},
  number    = {3},
  doi       = {10.3390/s23031119},
  year      = {2023},
  abstract  = {Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in nano- or micro-diamonds is a promising technology for precise magnetic-field sensors. Here, we propose a new, low-cost and stand-alone sensor setup that employs machine learning on an embedded device, so-called edge machine learning. We train an artificial neural network with data acquired from a continuous-wave ODMR setup and subsequently use this pre-trained network on the sensor device to deduce the magnitude of the magnetic field from recorded ODMR spectra. In our proposed sensor setup, a low-cost and low-power ESP32 microcontroller development board is employed to control data recording and perform inference of the network. In a proof-of-concept study, we show that the setup is capable of measuring magnetic fields with high precision and has the potential to enable robust and accessible sensor applications with a wide measuring range.},
  language  = {en}
}
@article{KielTrinschekKuchmizhaketal.2024,
  author    = {Kiel, Frederik and Trinschek, Sarah and Kuchmizhak, Aleksandr and Gurevich, Evgeny},
  title     = {Colouration of diamond surfaces by laser-induced periodic surface structuring},
  series = {Optics \& Laser Technology},
  journal   = {Optics \& Laser Technology},
  number    = {168},
  doi       = {10.1016/j.optlastec.2023.109882},
  pages     = {109882},
  year      = {2024},
  language  = {en}
}