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Jose Martinez

Publications and source records attributed to Jose Martinez.

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Bayesian Optimization Algorithms for Accelerator Physics

Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics community has recognized the advantages of Bayesian optimization algorithms, which leverage statistical surrogate models of objective functions to effectively address complex optimization challenges, especially in the presence of noise during accelerator operation and in resource-intensive physics simulations. In this review article, we offer a conceptual overview of applying Bayesian optimization techniques towards solving optimization problems in accelerator physics. We begin by providing a straightforward explanation of the essential components that make up Bayesian optimization techniques. We then give an overview of current and previous work applying and modifying these techniques to solve accelerator physics challenges. Finally, we explore practical implementation strategies for Bayesian optimization algorithms to maximize their performance, enabling users to effectively address complex optimization challenges in real-time beam control and accelerator design.

physics.acc-ph

Introducing explainable supervised machine learning into interactive feedback loops for statistical production system

Statistical production systems cover multiple steps from the collection, aggregation, and integration of data to tasks like data quality assurance and dissemination. While the context of data quality assurance is one of the most promising fields for applying machine learning, the lack of curated and labeled training data is often a limiting factor. The statistical production system for the Centralised Securities Database features an interactive feedback loop between data collected by the European Central Bank and data quality assurance performed by data quality managers at National Central Banks. The quality assurance feedback loop is based on a set of rule-based checks for raising exceptions, upon which the user either confirms the data or corrects an actual error. In this paper we use the information received from this feedback loop to optimize the exceptions presented to the National Central Banks thereby improving the quality of exceptions generated and the time consumed on the system by the users authenticating those exceptions. For this approach we make use of explainable supervised machine learning to (a) identify the types of exceptions and (b) to prioritize which exceptions are more likely to require an intervention or correction by the NCBs. Furthermore, we provide an explainable AI taxonomy aiming to identify the different explainable AI needs that arose during the project.

cs.LG

The hunt for new pulsars with the Green Bank Telescope

The Green Bank Telescope (GBT) is the largest fully steerable radio telescope in the world and is one of our greatest tools for discovering and studying radio pulsars. Over the last decade, the GBT has successfully found over 100 new pulsars through large-area surveys. Here I discuss the two most recent---the GBT 350 MHz Drift-scan survey and the Green Bank North Celestial Cap survey. The primary science goal of both surveys is to find interesting individual pulsars, including young pulsars, rotating radio transients, exotic binary systems, and especially bright millisecond pulsars (MSPs) suitable for inclusion in Pulsar Timing Arrays, which are trying to directly detect gravitational waves. These two surveys have combined to discover 85 pulsars to date, among which are 14 MSPs and many unique and fascinating systems. I present highlights from these surveys and discuss future plans. I also discuss recent results from targeted GBT pulsar searches of globular clusters and Fermi sources.

astro-ph.HE