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数理科学・先端技術研究開発センター(MAT)

セミナーのお知らせ

[MATセミナー開催のお知らせ]

開催日時:
2022年5月17日(火) 13:00〜15:00
使用言語:
英語
講演者:
廣瀬重信 (対面&Zoom)
タイトル:
Development of Paleo-detectors
概要:
Old minerals are natural particle detectors, here called “paleo-detectors”. This is because in the mineral an atom recoiled by a particle such as a dark matter particle or a neutrino is stopped via nuclear collisions, leaving a crystal defect (i.e. event signal) that cannot be erased unless thermally annealed. While normal particle detectors have large exposure owing to their huge target mass, the paleo-detectors, in spite of their tiny mass, can have comparable exposure thanks to their very long exposure time over the geological time scale.

A big issue with paleo-detectors is how to read out the “signals” left inside the minerals. The crystal defects made by the nuclear stopping are expected to be weak and short (O(10-100) nm). This is contrasted with the fission tracks, which are made when daughter atoms are decelerated by the electronic stopping and thus are much clearer and longer (O(10) micron). Therefore, it is crucial for the paleo-detectors to develop methods for reading out efficiently the weak and short defects in the mineral crystals.

Paleo-detectors were firstly applied a few decades ago to search magnetic monopoles (Price and Salamon 1986) and weakly-interacting massive particles (WIMPs) (Snowden-ifft et al. 1995). Recently paleo-detectors have been focused again by possible improvements of read-out methods (Baum et al. 2020) and their new applications to search dark matter much heavier than WIMPs. Following these new works, we have started working on the paleo-detectors. In this paper, we will introduce our initial attempts to read-out the crystal defects inside mineral samples irradiated by low-energy (~keV/amu) heavy ions or fast neutrons (~O(1) MeV) to mimic the natural nuclear recoil events.

[MATセミナー開催のお知らせ]

開催日時:
2022年4月27日(水) 13:00〜15:00
使用言語:
英語
講演者:
荒川 創太
タイトル:
On the collisional growth and fragmentation of dust aggregates
概要:
Understanding the collisional behavior of dust aggregates consisting of submicron-sized grains is essential to unveiling how planetesimals formed in protoplanetary disks.
It is known that the collisional behavior of individual dust particles strongly depends on the strength of viscous dissipation force; however, impacts of viscous dissipation on the collisional behavior of dust aggregates have not been studied in detail, especially for the cases of oblique collisions.
Here we investigated the impacts of viscous dissipation on the collisional behavior of dust aggregates.
We performed three-dimensional numerical simulations of collisions between two equal-mass dust aggregates with various collision velocities and impact parameters.
We also changed the strength of viscous dissipation force systematically.
We found that the threshold collision velocity for the fragmentation of dust aggregates barely depends on the strength of viscous dissipation force when we consider oblique collisions.
In contrast, the size distribution of fragments changes significantly when the viscous dissipation force is considered.
We obtained the empirical fitting formulae for the size distribution of fragments for the case of strong dissipation, which would be useful to study the evolution of size and spatial distributions of dust aggregates in protoplanetary disks.

[MATセミナー開催のお知らせ]

開催日時:
2022年4月20日(水) 13:00〜15:00
使用言語:
英語
講演者:
Daniel Shigueo Morikawa
タイトル:
Landslide simulations with the SPH method using GPU programming
概要:
Landslides are a very challenging phenomena to numerically simulate. Summarizing a few of its challenging points: (a) its geometry is usually complex, especially because it may change its topology if comparing the initial configuration with the final one; (b) it may be triggered by various external conditions such as increased internal pore water pressure (b1), earthquakes (b2), and others; (c) the material behavior of a landslide is very complex, given that soil is, by definition, composed by several materials such as soil grains, water and air.
In this presentation I intend to show my previous research achievements using the SPH method, which is a type of numerical method that discretizes the space into particles. In summary, using topographical data and a computer code to translate into a particle mode to solve (a) and two SPH numerical models to solve (b1) and (b2). All methods were developed using GPU programming, which is a highly effective way to increase computational efficiency through parallel computation.
Lastly, I will present my current and future plans to improve landslide simulations with SPH. As for the current goal, I intend to increase the scale of simulations by developing a code that can utilize an arbitrary number of GPU cards in parallel. For the future, the objective is to tackle the problem of complex material behavior of the soil (c) using the SPH method to simulate the soil in microscale, then, using Neural Networks to bridge the gap between micro and macro scales in an efficient way.

[MATセミナー開催のお知らせ]

開催日時:
2022年4月13日(水) 13:00〜14:00
使用言語:
英語
講演者:
鄭 美嘉
タイトル:
Agritech imaging of underground plant root growth using a distributed fiber optic sensor
概要:
Recent studies have shown that root system architecture determines crop resilience and productivity. However, roots grow invisibly underground and are notoriously difficult to track. Root visualization requires digging, which is time-consuming and destructive. The lack of real-time non-invasive underground imaging methods has made it challenging to study this vital organ. Here, we report a method for imaging underground root system using the distributed fiber optic sensor. device named “Fiber-RADGET”. By formulating an optical fiber into spiral polytetrafluoroethylene film, the sensor device named Fiber-RADGET detects and monitors geophysical strain generated by root development. Agricultural technology is increasingly becoming automated with seamless feedback through Internet-of-Things remote sensors. The device highlighted here represents a significant addition to the repertoire of tools that next-generation agriculturalists can use for data-driven automation.