Related papers: Magnetic field regression using artificial neural …
We study gradient magnetometry with an ensemble of atoms with arbitrary spin. We calculate precision bounds for estimating the gradient of the magnetic field based on the quantum Fisher information. For quantum states that are invariant…
In cold atomic gases the interactions between the atoms are directly controllable through external magnetic fields. The magnetic field control is typically performed indirectly by stabilizing the current through a pair of Helmholtz coils,…
The existence of magnetic moments of neutrinos points to physics beyond the standard model. Given current upper limits, terrestrial measurements are difficult or completely unfeasible. However, estimates of transition moments can be…
Observational studies of magnetic fields are vital as magnetic fields play a crucial role in various astrophysical processes, including star formation, accretion of matter, transport processes (e.g., transport of heat), and cosmic rays. We…
Measurement of magnetic fields on the few-hundred-kilometer length scale is significant for a variety of geophysical applications including mapping of crustal magnetism and ocean-circulation measurements, yet available techniques for such…
We describe the development of a simple atomic magnetometer using $^{87}$Rb vapor suitable for Earth magnetic field monitoring. The magnetometer is based on time-domain determination of the transient precession frequency of the atomic…
We present a detailed analysis of the usefulness of ultracold atomic collisions for sensing the strength of an external magnetic field as well as its spatial gradient. The core idea of the sensor, which we recently proposed in K. Jachymski…
Neural network force fields have significantly advanced ab initio atomistic simulations across diverse fields. However, their application in the realm of magnetic materials is still in its early stage due to challenges posed by the subtle…
Machine learning algorithms relying on deep neural networks recently allowed a great leap forward in artificial intelligence. Despite the popularity of their applications, the efficiency of these algorithms remains largely unexplained from…
Measuring the magnetic field of permanent magnets can be challenging, but recent research has demonstrated the potential of using deformed diamagnetic liquids to estimate the magnetic field. In this paper, we explore two methods for…
The problem of detection of magnetic fields using NV centers, that is, to check whether a weak magnetic field is present or not, can be tackled using quantum state discrimination theory. In this regard, we find the POVMs that maximize the…
We demonstrate a minimally-destructive in situ technique for measuring and stabilizing slowly-drifting magnetic fields in ultracold-atom experiments. While conventional magnetic-field sensors such as Hall, giant magnetoresistive, or…
Atom interferometers deployed in space are excellent tools for high precision measurements, navigation, or Earth observation. In particular, differential interferometric setups feature common-mode noise suppression and enable reliable…
We present a theory for the estimation of a scalar or a vector magnetic field by its influence on an ensemble of trapped spin polarized atoms. The atoms interact off-resonantly with a continuous laser field, and the measurement of the…
Probing magnetic fields in astrophysical environments is both important and challenging. The Gradient Technique (GT) is a new tool for tracing magnetic fields, rooted in the properties of magnetohydrodynamic (MHD) turbulence and turbulent…
We present a theoretical analysis of the ability of atomic magnetometers to estimate a fluctuating magnetic field. Our analysis makes use of a Gaussian state description of the atoms and the probing field, and it presents the estimator of…
The low magnetic field measurement has been utilized since ancient times in order to find economic resources, to detect magnetic anomalies, etc. In this case, the vector magnetic survey can simultaneously obtain the modulus and direction…
In this paper, we demonstrate the expressibility of artificial neural networks (ANNs) in quantum many-body physics by showing that a feed-forward neural network with a small number of hidden layers can be trained to approximate with high…
Normal estimation for unstructured point clouds is an important task in 3D computer vision. Current methods achieve encouraging results by mapping local patches to normal vectors or learning local surface fitting using neural networks.…
Surface based geometries of microfabricated wires or patterned magnetic films can be used to magnetically trap and manipulate ultracold neutral atoms or Bose-Einstein condensates. We investigate the magnetic properties of such atom chips…