Abstract Polyatomic molecules have been identified as sensitive probes of charge-parity violating and parity violating physics beyond the Standard Model (BSM). For example, many linear triatomic molecules are both laser-coolable and have parity doublets in the ground electronic state arising from the bending vibration, both features that can greatly aid BSM searches. Understanding the state is a crucial prerequisite to precision measurements with linear polyatomic molecules. Here, we characterize the fundamental bending vibration of YbOH using high-resolution optical spectroscopy on the nominally forbidden transition at 588 nm. We assign 39 transitions originating from the lowest rotational levels of the state, and accurately model the state’s structure with an effective Hamiltonian using best-fit parameters. Additionally, we perform Stark and Zeeman spectroscopy on the state and fit the molecule-frame dipole moment to Dand the effective electrong-factor to . Further, we use an empirical model to explain observed anomalous line intensities in terms of interference from spin–orbit and vibronic perturbations in the excited state. Our work is an essential step toward searches for BSM physics in YbOH and other linear polyatomic molecules.
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Deep multi-task mining Calabi–Yau four-folds
Abstract We continue earlier efforts in computing the dimensions of tangent space cohomologies of Calabi–Yau manifolds using deep learning. In this paper, we consider the dataset of all Calabi–Yau four-folds constructed as complete intersections in products of projective spaces. Employing neural networks inspired by state-of-the-art computer vision architectures, we improve earlier benchmarks and demonstrate that all four non-trivial Hodge numbers can be learned at the same time using a multi-task architecture. With 30% (80%) training ratio, we reach an accuracy of 100% for and 97% for (100% for both), 81% (96%) for , and 49% (83%) for . Assuming that the Euler number is known, as it is easy to compute, and taking into account the linear constraint arising from index computations, we get 100% total accuracy.
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- Award ID(s):
- 2019786
- PAR ID:
- 10303335
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Machine Learning: Science and Technology
- Volume:
- 3
- Issue:
- 1
- ISSN:
- 2632-2153
- Page Range / eLocation ID:
- Article No. 015006
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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