We installed a source for ultracold neutrons at a new, dedicated spallation target at TRIUMF. The source was originally developed in Japan and uses a superfluid-helium converter cooled to 0.9 K. During an extensive test campaign in November 2017, we extracted up to 325 000 ultracold neutrons after a one-minute irradiation of the target, over three times more than previously achieved with this source. The corresponding ultracold-neutron density in the whole production and guide volume is 5.3 cm −3 . The storage lifetime of ultracold neutrons in the source was initially 37 s and dropped to 24 s during the eighteen days of operation. During continuous irradiation of the spallation target, we were able to detect a sustained ultracold-neutron rate of up to 1500 s −1 .Simulations of UCN production, UCN transport, temperature-dependent UCN yield, and temperature-dependent storage lifetime show excellent agreement with the experimental data and confirm that the ultracold-neutron-upscattering rate in superfluid helium is proportional to T 7 .
We discuss a new neutron EDM measurement of 10 −28 e cm. For the improved UCN density, we will apply a new spallation UCN source of superfluid He. For magnetometry, 129 Xe nuclear spins are injected into a EDM cell, to supress GPE. Performance of the prototype KEK-RCNP UCN source, and the obtained Ramsey resonance spectra are presented.
A fast-switching, high-repetition-rate magnet and power supply have been developed for and operated at TRIUMF, to deliver a proton beam to the new ultracold neutron (UCN) facility. The facility possesses unique operational requirements: a time-averaged beam current of 40 µA with the ability to switch the beam on or off for several minutes. These requirements are in conflict with the typical operation mode of the TRIUMF cyclotron which delivers nearly continuous beam to multiple users. To enable the creation of the UCN facility, a beam-sharing arrangement with another facility was made. The beam sharing is accomplished by the fast-switching (kicker) magnet which is ramped in 50 µs to a current of 193 A, held there for approximately 1 ms, then ramped down in the same short period of time. This achieves a 12 mrad deflection which is sufficient to switch the proton beam between the two facilities. The kicker magnet relies on a high-current, lowinductance coil connected to a fast-switching power supply that is based on insulated-gate bipolar transistors (IGBTs). The design and performance of the kicker magnet system and initial beam delivery results are reported.
Machine learning tasks often require a significant amount of training data for the resultant network to perform suitably for a given problem in any domain. In agriculture, dataset sizes are further limited by phenotypical differences between two plants of the same genotype, often as a result of differing growing conditions. Synthetically-augmented datasets have shown promise in improving existing models when real data is not available. In this paper, we employ a contrastive unpaired translation (CUT) generative adversarial network (GAN) and simple image processing techniques to translate indoor plant images to appear as field images. While we train our network to translate an image containing only a single plant, we show that our method is easily extendable to produce multiple-plant field images. Furthermore, we use our synthetic multiplant images to train several YoloV5 nano object detection models to perform the task of plant detection and measure the accuracy of the model on real field data images. Including training data generated by the CUT-GAN leads to better plant detection performance compared to a network trained solely on real data.
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