arXiv subjects

Shota Arai

Publications and source records attributed to Shota Arai.

6 recordsLinked to original sources

arXiv2026

Low-Noise SiPM Light Readout and ASIC-Based Charge Readout of a Liquid Argon Time Projection Chamber for MeV Gamma-Ray Measurements

We have developed a compact liquid argon time projection chamber (LArTPC), NanoGRAMS, as a technology demonstrator for the Gamma-Ray and AntiMatter Survey (GRAMS). LArTPCs have the potential to enable Compton cameras with unprecedented effective area in the MeV gamma-ray band. NanoGRAMS has an active volume of $5.12 \times 5.12 \times 10~\mathrm{cm^3}$ and is equipped with a low-noise scintillation and charge readout system. The scintillation light is detected by an array of 16 SiPMs ($6 \times 6~\mathrm{mm^2}$ each), whose signals are summed and amplified by a low-noise transimpedance amplifier operable at liquid argon temperature. Ionization electrons are read out with $3.2\,\mathrm{mm}$-pitch pixels and processed by VATA-SGD ASICs, with synchronization provided by an FPGA-based data acquisition system. We irradiated the detector with a $^{60}\mathrm{Co}$ source (1173 and $1332\,\mathrm{keV}$) and successfully detected both 1-hit and 2-hit events. The collected charge was converted to deposited energy using a phenomenological recombination model, and the detector response was evaluated with a Geant4-based Monte Carlo simulation. The reconstructed energy spectrum shows Compton edges at 963 and $1118\,keV$, consistent with the expected values. For 2-hit events, the sequence of interactions was identified, and the reconstructed back-projection image agrees with the source position. These results demonstrate the feasibility of NanoGRAMS as a Compton camera for MeV gamma-ray imaging spectroscopy.

astro-ph.IM↗

arXiv2025

How many patients could we save with LLM priors?

Imagine a world where clinical trials need far fewer patients to achieve the same statistical power, thanks to the knowledge encoded in large language models (LLMs). We present a novel framework for hierarchical Bayesian modeling of adverse events in multi-center clinical trials, leveraging LLM-informed prior distributions. Unlike data augmentation approaches that generate synthetic data points, our methodology directly obtains parametric priors from the model. Our approach systematically elicits informative priors for hyperparameters in hierarchical Bayesian models using a pre-trained LLM, enabling the incorporation of external clinical expertise directly into Bayesian safety modeling. Through comprehensive temperature sensitivity analysis and rigorous cross-validation on real-world clinical trial data, we demonstrate that LLM-derived priors consistently improve predictive performance compared to traditional meta-analytical approaches. This methodology paves the way for more efficient and expert-informed clinical trial design, enabling substantial reductions in the number of patients required to achieve robust safety assessment and with the potential to transform drug safety monitoring and regulatory decision making.

stat.ME↗

arXiv2025

PorousGen: An Efficient Algorithm for Generating Porous Structures with Accurate Porosity and Uniform Density Distribution

This work presents a novel algorithm for generating porous structures as an alternative to the PoreSpy program suite. Unlike PoreSpy, which often produces structures whose porosity deviates from the target value, our proposed algorithm generates structures whose porosity closely matches the specified input, within a defined error margin. Furthermore, parallel computation enables efficient generation of large-scale structures, while memory usage is reduced compared to PoreSpy. To evaluate performance, structures were generated using both PoreSpy and the proposed method with parameters corresponding to X-ray ptychography experiments. The porosity mismatch in PoreSpy led to a relative error exceeding 20% in the computed gas diffusion coefficients, whereas our method reproduced the experimental values within 5%. These results demonstrate that the proposed method provides an efficient, high-precision approach for generating porous structures and supports reliable prediction of material properties. The program called PorousGen is publicly available under the MIT License from https://github.com/YoshidomeGroup-Hydration/PorousGen.

cond-mat.soft↗

arXiv2025

Development of Solar Flare X-ray Polarimeter with Micro-Pixel CMOS Sensors

We are developing an X-ray polarimeter using micro-pixel CMOS sensors for solar flare X-ray polarimetry. The system consists of a 2.5-$μ$m pixel CMOS image sensor with a 12.8$\times$12.8 mm$^2$ imaging area and a readout system based on a Zynq System-on-Chip. While previous studies have validated this concept, no realistic feasibility studies have been conducted for the solar flare X-ray polarization observation. In this work, we performed polarization sensitivity measurements at synchrotron facilities. The results show that our polarimeter is sensitive to the X-ray polarization, exhibiting a modulation factor of 5-15% at an energy range of 6-22 keV. The measurements also determined the thickness of the sensitive layer to be approximately 5 $μ$m, and the thicknesses of the insensitive layers to be 0.8 $μ$m (Si), 2.1 $μ$m (SiO2), and 0.24 $μ$m (Cu). These measured thicknesses lead to a quantum efficiency of 3-4% at 10 keV. Based on these experimental evaluations, we estimated the sensitivity of the micro-pixel CMOS polarimeter system. We found that, when combined with a telescope with an effective area of $\sim$10 cm$^2$, this system can detect X-ray polarization with a polarization degree of a few percent for M-class flares.

astro-ph.IM↗

arXiv2025

Exploring the Alignment of Perceived and Measured Sleep Quality with Working Memory using Consumer Wearables

Wearable devices offer detailed sleep-tracking data. However, whether this information enhances our understanding of sleep or simply quantifies already-known patterns remains unclear. This work explores the relationship between subjective sleep self-assessments and sensor data from an Oura ring over 4--8 weeks in-the-wild. 29 participants rated their sleep quality daily compared to the previous night and completed a working memory task. Our findings reveal that differences in REM sleep, nocturnal heart rate, N-Back scores, and bedtimes highly predict sleep self-assessment in significance and effect size. For N-Back performance, REM sleep duration, prior night's REM sleep, and sleep self-assessment are the strongest predictors. We demonstrate that self-report sensitivity towards sleep markers differs among participants. We identify three groups, highlighting that sleep trackers provide more information gain for some users than others. Additionally, we make all experiment data publicly available.

cs.HC↗

arXiv2024

Development of the X-ray polarimeter using CMOS imager: polarization sensitivity of a $1.5~{\rm μm}$ pixel CMOS sensor

We are developing an imaging polarimeter by combining a fine-pixel CMOS image sensor with a coded aperture mask as part of the cipher project, aiming to achieve X-ray polarimetry in the energy range of $10$$\unicode{x2013}$$30~\mathrm{keV}$. A successful proof-of-concept experiment was conducted using a fine-pixel CMOS sensor with a $2.5~\mathrm{μm}$ pixel size. In this study, we conducted beam experiments to assess the modulation factor (MF) of the CMOS sensor with a $1.5~\mathrm{μm}$ pixel size manufactured by Canon and to determine if there was any improvement in the MF. The measured MF was $8.32\% \pm 0.34\%$ at $10~\mathrm{keV}$ and $16.10\% \pm 0.68\%$ at $22~\mathrm{keV}$, exceeding those of the $2.5~\mathrm{μm}$ sensor in the $6$$\unicode{x2013}$$22~\mathrm{keV}$ range. We also evaluated the quantum efficiency of the sensor, inferring a detection layer thickness of $2.67 \pm 0.48~{\rm μm}$. To develop a more sensitive polarimeter, a sensor with a thicker detection layer, smaller pixel size, and reduced thermal diffusion effect is desirable.

astro-ph.IM↗