The Influence of Didymosphenia geminate on Fisheries Resources in Rapid Creek, South Dakota – An Eight Year History

The aquatic nuisance diatom Didymosphenia geminata was established in Rapid Creek in the Black Hills of South Dakota in 2002. Shortly thereafter, large declines (\u3e50%) of the naturalized brown trout Salmo trutta population were observed. We evaluated the influence of water resources and D. geminata on (1) declines in brown trout biomass, (2) changes in food resources, and (3) diet of brown trout in Black Hills streams. Drought conditions were largely responsible for trout declines in Black Hills streams. However, comparison of brown trout sizestructure between the pre-D. geminata and post-D. geminata periods revealed that juvenile brown trout abundance increased while adult abundance decreased in Rapid Creek. Changes in food resources in D. geminata-impacted areas were thought to favor juvenile brown trout and negatively impact adults. In the presence of D. geminata, macroinvertebrate abundance was composed of fewer, larger taxa and higher numbers of smaller taxa (i.e., chironomids). Brown trout in Rapid Creek consumed fewer ephemeropterans and a high amount of dipterans. Nonetheless, diet analysis showed that brown trout in Rapid Creek consumed as much or more prey than trout from two other streams unaffected by D. geminata. Moreover, relative weight of brown trout from Rapid Creek was high (\u3e100), implying that food availability was not limiting. These findings imply that D. geminata did not negatively impact feeding and condition of brown trout in Rapid Creek, although mechanisms affecting size-structure in Rapid Creek remain unknown

Object Detection with Deep Learning for Rare Event Search in the GADGET II TPC

International audienceIn the pursuit of identifying rare two-particle events within the GADGET II Time Projection Chamber (TPC), this paper presents a comprehensive approach for leveraging Convolutional Neural Networks (CNNs) and various data processing methods. To address the inherent complexities of 3D TPC track reconstructions, the data is expressed in 2D projections and 1D quantities. This approach capitalizes on the diverse data modalities of the TPC, allowing for the efficient representation of the distinct features of the 3D events, with no loss in topology uniqueness. Additionally, it leverages the computational efficiency of 2D CNNs and benefits from the extensive availability of pre-trained models. Given the scarcity of real training data for the rare events of interest, simulated events are used to train the models to detect real events. To account for potential distribution shifts when predominantly depending on simulations, significant perturbations are embedded within the simulations. This produces a broad parameter space that works to account for potential physics parameter and detector response variations and uncertainties. These parameter-varied simulations are used to train sensitive 2D CNN object detectors. When combined with 1D histogram peak detection algorithms, this multi-modal detection framework is highly adept at identifying rare, two-particle events in data taken during experiment 21072 at the Facility for Rare Isotope Beams (FRIB), demonstrating a 100% recall for events of interest. We present the methods and outcomes of our investigation and discuss the potential future applications of these techniques