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Precise and accurate atomic mass data provide crucial information for applications in a wide range of fields in physics and beyond, including astrophysics, nuclear structure, particle and neutrino physics, fundamental symmetries, chemistry, and metrology. The most precise atomic mass measurements are performed on charged particles confined in a Penning trap. Here, we describe the development, status, and outlook of CHIP-TRAP: the Central Michigan University high-precision Penning trap. CHIP-TRAP aims to perform ultra-high precision (∼1 part in 1011 fractional precision) mass measurements on stable and long-lived isotopes produced with external ion sources and transported to the Penning traps. Along the way, ions of a particular m/q are selected with a multi-reflection time-of-flight mass separator (MR-TOF-MS), with further filtering performed in a cylindrical capture trap before the ions are transported to a pair of hyperbolic measurement traps. In this paper, we report on the design and status of CHIP-TRAP and present results from the commissioning of the ion sources, MR-TOF-MS, and capture trap. We also provide an outlook on the continued development and commissioning of CHIP-TRAP.
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Applications of Machine Learning and Neural Networks for FT-ICR Mass Measurements with SIPT
The single-ion Penning trap (SIPT) at the Low-Energy Beam Ion Trapping Facility has been developed to perform precision Penning trap mass measurements of single ions, ideal for the study of exotic nuclei available only at low rates at the Facility for Rare Isotope Beams (FRIB). Single-ion signals are very weak—especially if the ion is singly charged—and the few meaningful ion signals must be disentangled from an often larger noise background. A useful approach for simulating Fourier transform ion cyclotron resonance signals is outlined and shown to be equivalent to the established yet computationally intense method. Applications of supervised machine learning algorithms for classifying background signals are discussed, and their accuracies are shown to be ≈65% for the weakest signals of interest to SIPT. Additionally, a deep neural network capable of accurately predicting important characteristics of the ions observed by their image charge signal is discussed. Signal classification on an experimental noise dataset was shown to have a false-positive classification rate of 10.5%, and 3.5% following additional filtering. The application of the deep neural network to an experimental 85Rb+ dataset is presented, suggesting that SIPT is sensitive to single-ion signals. Lastly, the implications for future experiments are discussed.
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- Award ID(s):
- 2111185
- PAR ID:
- 10521955
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Atoms
- Volume:
- 11
- Issue:
- 10
- ISSN:
- 2218-2004
- Page Range / eLocation ID:
- 126
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.3390/atoms11100126
