相关论文: Fast shimming algorithm based on Bayesian optimiza…
Dark matter is a major constituent of our universe and the axion is a prime candidate. In this article, it is shown that by exploiting the axion induced magnetization in a magnetic rod and the Faraday effect, axions in the mass range $500$…
The axion is a highly motivated elementary particle which could address two fundamental questions in physics - the strong CP problem and the dark matter mystery. Experimental searches for this hypothetical particle have started to reach…
Nuclear fusion using magnetic confinement holds promise as a viable method for sustainable energy. However, most fusion devices have been experimental and as we move towards energy reactors, we are entering into a new paradigm of…
This chapter will cover the search for dark matter axions based on microwave cavity experiments proposed by Pierre Sikivie. The topic begins with a brief overview of halo dark matter and the axion as a candidate. The principle of resonant…
Experimental robot optimization often requires evaluating each candidate policy for seconds to minutes. The chosen evaluation time influences optimization because of a speed-accuracy tradeoff: shorter evaluations enable faster iteration,…
Bayesian coresets have emerged as a promising approach for implementing scalable Bayesian inference. The Bayesian coreset problem involves selecting a (weighted) subset of the data samples, such that the posterior inference using the…
The cavity haloscope provides a highly sensitive method to search for dark matter axions in the microwave regime. Experimental attempts to enhance the sensitivity have focused on improving major aspects, such as producing strong magnetic…
We investigate a search for the oscillating current induced by axion dark matter in an external magnetic field using optically pumped magnetometers (OPMs). This experiment is based upon the LC circuit axion detection concept of Sikivie,…
Bayesian optimization (BO) is a powerful approach for seeking the global optimum of expensive black-box functions and has proven successful for fine tuning hyper-parameters of machine learning models. However, BO is practically limited to…
We present a method to determine the sensitivity of a closed dielectric haloscope to axion dark matter. Dielectric haloscopes aim to probe the theoretically well-motivated axion mass range of ~26 $\mathrm{\mu}$eV to ~500 $\mathrm{\mu}$eV by…
Bayesian optimization is a popular formalism for global optimization, but its computational costs limit it to expensive-to-evaluate functions. A competing, computationally more efficient, global optimization framework is optimistic…
The optimal selection of experimental conditions is essential to maximizing the value of data for inference and prediction, particularly in situations where experiments are time-consuming and expensive to conduct. We propose a general…
We show that a recent constraint on the cosmic birefringence effect due to dark energy can be related to the constraints on the coupling of axion dark matter to photon, by relying on a simple model of two-axion alignment mechanism with…
An understanding of how input parameter uncertainty in the numerical simulation of physical models leads to simulation output uncertainty is a challenging task. Common methods for quantifying output uncertainty, such as performing a grid or…
The aim of magnetorelaxometry imaging is to determine the distribution of magnetic nanoparticles inside a subject by measuring the relaxation of the superposition magnetic field generated by the nanoparticles after they have first been…
Batch Bayesian optimisation (BO) is a successful technique for the optimisation of expensive black-box functions. Asynchronous BO can reduce wallclock time by starting a new evaluation as soon as another finishes, thus maximising resource…
High-dimensional simulation optimization is notoriously challenging. We propose a new sampling algorithm that converges to a global optimal solution and suffers minimally from the curse of dimensionality. The algorithm consists of two…
Bayesian optimization works effectively optimizing parameters in black-box problems. However, this method did not work for high-dimensional parameters in limited trials. Parameters can be efficiently explored by nonlinearly embedding them…
This presentation describes the Bayesian Block algorithm in the context of its application to analysis of time series data from the Fermi Gamma Ray Space Telescope. More generally this algorithm performs optimal segmentation analysis on…
The sensitivity of experimental searches for axion dark matter coupled to photons is typically proportional to the strength of the applied static magnetic field. We demonstrate how a permeable material can be used to enhance the magnitude…