Precise measurement on the binding energy of hypertriton from the nuclear emulsion data using analysis with machine learning

6 pags., 3 figs.A machine learning model has been developed to search for events of production and decay of a hypertriton in nuclear emulsion data, which is used for measuring the binding energy of the hypertriton at the best precision. The developed model employs an established technique for object detection and is trained with surrogate images generated by Monte Carlo simulations and image transfer techniques. The first hypertriton event has already been detected with the developed method only with 10−4 of the total emulsion data. It implies that a sufficient number of hypertriton events will soon be detected for the precise measurement of the hypertriton binding energy

Unique approach for precise determination of binding energies of hypernuclei with nuclear emulsion and machine learning

4 pags., 1 tab. -- HYP2022 - 14th International Conference on Hypernuclear and Strange Particle PhysicsHypertriton is the lightest hypernucleus and a benchmark in hypernuclear physics. However, it has recently been suggested that its lifetime and binding energy values may differ from the established values. To solve this puzzle, it is necessary to measure both values with a higher precision. For the precise measurement of the binding energy, we are aiming at developing a novel technique to measure the hypertriton binding energy with unprecedented accuracy by combining nuclear emulsion data and machine learning techniques. The analysis will be based on the J-PARC E07 nuclear emulsion data. Furthermore, a machine-learning model is being developed to identify other single and double-strangeness hypernucle

Hypernuclear event detection in the nuclear emulsion with Monte Carlo simulation and machine learning

This study developed a novel method for detecting hypernuclear events recorded in nuclear emulsion sheets using machine learning techniques. The artificial neural network-based object detection model was trained on surrogate images created through Monte Carlo simulations and image-style transformations using generative adversarial networks. The performance of the proposed model was evaluated using $\alpha$-decay events obtained from the J-PARC E07 emulsion data. The model achieved approximately twice the detection efficiency of conventional image processing and reduced the time spent on manual visual inspection by approximately 1/17. The established method was successfully applied to the detection of hypernuclear events. This approach is a state-of-the-art tool for discovering rare events recorded in nuclear emulsion sheets without any real data for training.Comment: 32 pages, 13 figure

WASA-FRS experiments in FAIR Phase-0 at GSI

We have developed a new and unique experimental setup integrating the central part of the Wide Angle Shower Apparatus (WASA) into the Fragment Separator (FRS) at GSI. This combination opens up possibilities of new experiments with high-resolution spectroscopy at forward and measurements of light decay particles with nearly full solid-angle acceptance in coincidence. The first series of the WASA-FRS experiments have been successfully carried out in 2022. The developed experimental setup and two physics experiments performed in 2022 including the status of the preliminary data analysis are introduced

Arsenic incorporation into authigenic pyrite, Bengal Basin sediment, Bangladesh

Sediment from two deep boreholes (~400 m) approximately 90 km apart in southern Bangladesh was analyzed by X-ray absorption spectroscopy (XAS), total chemical analyses, chemical extractions, and electron probe microanalysis to establish the importance of authigenic pyrite as a sink for arsenic in the Bengal Basin. Authigenic framboidal and massive pyrite (median values 1500 and 3200 ppm As, respectively), is the principal arsenic residence in sediment from both boreholes. Although pyrite is dominant, ferric oxyhydroxides and secondary iron phases contain a large fraction of the sediment-bound arsenic between approximately 20 and 100 m, which is the depth range of wells containing the greatest amount of dissolved arsenic. The lack of pyrite in this interval is attributed to rapid sediment deposition and a low sulfur flux from riverine and atmospheric sources. The ability of deeper aquifers (\u3e150 m) to produce ground water with low dissolved arsenic in southern Bangladesh reflects adequate sulfur supplies and sufficient time to redistribute the arsenic into pyrite during diagenesis

Hypernuclear event detection in the nuclear emulsion with Monte Carlo simulation and machine learning

This study developed a novel method for detecting hypernuclear events recorded in nuclear emulsion sheets using machine learning techniques. The artificial neural network-based object detection model was trained on surrogate images created through Monte Carlo simulations and image-style transformations using generative adversarial networks. The performance of the proposed model was evaluated using α-decay events obtained from the J-PARC E07 emulsion data. The model achieved approximately twice the detection efficiency of conventional image processing and reduced the time spent on manual visual inspection by approximately 1/17. The established method was successfully applied to the detection of hypernuclear events. This approach is a state-of-the-art tool for discovering rare events recorded in nuclear emulsion sheets without any real data for training.</p

Hypernuclear event detection in the nuclear emulsion with Monte Carlo simulation and machine learning

This study developed a novel method for detecting hypernuclear events recorded in nuclear emulsion sheets using machine learning techniques. The artificial neural network-based object detection model was trained on surrogate images created through Monte Carlo simulations and image-style transformations using generative adversarial networks. The performance of the proposed model was evaluated using α-decay events obtained from the J-PARC E07 emulsion data. The model achieved approximately twice the detection efficiency of conventional image processing and reduced the time spent on manual visual inspection by approximately 1/17. The established method was successfully applied to the detection of hypernuclear events. This approach is a state-of-the-art tool for discovering rare events recorded in nuclear emulsion sheets without any real data for training.</p

Development of machine learning analyses with graph neural network for the WASA-FRS experiment

13 pags., 9 figs., 7 tabs.The WASA-FRS experiment aims to reveal the nature of light Λ hypernuclei with heavy-ion beams. The lifetimes of hypernuclei are measured precisely from their decay lengths and kinematics. To reconstruct a π- track emitted from hypernuclear decay, track finding is an important issue. In this study, a machine learning analysis method with a graph neural network (GNN), which is a powerful tool for deducing the connection between data nodes, was developed to obtain track associations from numerous combinations of hit information provided in detectors based on a Monte Carlo simulation. An efficiency of 98% was achieved for tracking π- mesons using the developed GNN model. The GNN model can also estimate the charge and momentum of the particles of interest. More than 99.9% of the negative charged particles were correctly identified with a momentum accuracy of 6.3%.This work was supported by the Special Postdoctoral Researcher Program at RIKEN. The authors thank Yukiko Kurakata of the High Energy Nuclear Physics Laboratory at RIKEN to provide administrative support for the entire project. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.Peer reviewe