arXiv subjects

Kazuma Nakazawa

Publications and source records attributed to Kazuma Nakazawa.

5 recordsLinked to original sources

arXiv2026

First results on the search for the Galactic Center Excess in the sub-GeV band with the emulsion telescope in GRAINE 2023

Please check the paper for full abstract. The Galactic Center Excess (GCE) is an unexplained excess of gamma-ray emission from the Galactic Center. The GRAINE experiment aims to reveal the origin of the GCE using an emulsion gamma-ray telescope with high angular resolutions of 1 deg at 100 MeV and 0.1 deg at 1 GeV. In this study, we search for the GCE in a small region near the Galactic Center using the GRAINE 2023 flight data. In particular, rather than focusing on the spectral peak of the GCE at 2 GeV, we focused on the energy range below 300 MeV, where the spectral differences between the dark matter annihilation and millisecond pulsar scenarios are more pronounced. We searched for the GCE within 1 deg of the Galactic Center in the 75--300 MeV energy range. Although no significant excess was observed, we obtained an upper limit on the GCE flux of 1.70*10^-7 GeV cm^-2 s^-1 at the 2 sigma confidence level for the 1deg-radius ROI centered on the Galactic Center, based on a direct observation of the narrow region around the Galactic Center. This observation requires high angular resolution and represents a unique result from GRAINE. The obtained upper limit is consistent with the GCE flux near the Galactic Center, which was estimated from existing Fermi-LAT observations using a wide ROI and assuming an NFW profile. Although the current upper limit constrains some models, the available statistics are still insufficient to distinguish between the dark matter annihilation and millisecond pulsar scenarios, and both remain consistent with the current results. We also estimated the projected sensitivity of future GRAINE experiments based on the present observation and demonstrated their potential to probe the origin of the GCE by comparing the projected sensitivity with the predicted GCE spectra.

astro-ph.HE↗

arXiv2026

First overnight balloon flight of the GRAINE 2023 emulsion gamma-ray telescope enabled by a large-scale pressure-vessel gondola

The Gamma-Ray Astro Imager with Nuclear Emulsion (GRAINE) project conducts precision observations of sub-GeV--GeV cosmic gamma rays using a balloon-borne nuclear-emulsion telescope with high angular resolution. In GRAINE 2023, a 2.5-m$^{2}$ telescope was flown in the project's first overnight balloon flight, including observation periods for the Vela pulsar and Galactic center region. To operate the telescope under the low-pressure and low-temperature stratospheric environment, the balloon-style pressure-vessel concept was scaled up to a lightweight gondola with an internal length of 4.9 m. A new aluminum-alloy ring structure and a lightweight membranous-shell material, SHL-300MDL, were developed. While the telescope aperture was increased by a factor of 6.6 over GRAINE 2018, the pressure-vessel gondola mass was limited to 179 kg. Ground tests of the completed flight assembly demonstrated a differential pressure above 100 hPa at room temperature and at a mean temperature of $-66.0^{\circ}$C. The payload was launched from Alice Springs, Australia, in April 2023 and achieved a total flight duration of approximately 27 h, including 24.3 h of level flight. Although the upper membranous shell reached approximately $-60^{\circ}$C at night, the vessel internal pressure remained above the required 100 hPa throughout level flight. These results demonstrate that the developed gondola can accommodate a 2.5-m$^{2}$ emulsion gamma-ray telescope and maintain the required pressure during overnight stratospheric flight. Scientific analyses of astrophysical and atmospheric gamma rays, including dedicated analysis of the Galactic center region, are ongoing using the recovered emulsion data. This development provides a technical basis for repeated observations with future large-area GRAINE telescopes.

astro-ph.IM↗

arXiv2026

Artificial intelligence pioneers the double-strangeness factory

Artificial intelligence (AI) is transforming not only our daily experiences but also the technological development landscape and scientific research. In this study, we pioneered the application of AI in double-strangeness hypernuclear studies. These studies which investigate quantum systems with strangeness via hyperon interactions provide insights into fundamental baryon-baryon interactions and contribute to our understanding of the nuclear force and composition of neutron star cores. Specifically, we report the observation of a double hypernucleus in nuclear emulsion achieved via innovative integration of machine learning techniques. The proposed methodology leverages generative AI and Monte Carlo simulations to produce training datasets combined with object detection AI for effective event identification. Based on the kinematic analysis and charge identification, the observed event was uniquely identified as the production and decay of resulting from Ξ- capture by 14N in the nuclear emulsion. Assuming capture in the atomic 3D state, the binding energy of the two Λ hyperons in 13BΛΛ, BΛΛ, was determined as 25.57 +- 1.18(stat.) +- 0.07(syst.) MeV. The ΛΛ interaction energy obtained was 2.83 +- 1.18(stat.) +- 0.14(syst.) MeV. This study marks a new era in double-strangeness research.

nucl-ex↗

arXiv2025

Binding energy of $^{3}_Λ\rm{H}$ and $^{4}_Λ\rm{H}$ via image analyses of nuclear emulsions using deep-learning

Subatomic systems are pivotal for understanding fundamental baryonic interactions, as they provide direct access to quark-level degrees of freedom. In particular, introducing a strange quark adds "strangeness" as a new dimension, offering a powerful tool for exploring nuclear forces. The hypertriton, the lightest three-body hypernuclear system, provides an ideal testing ground for investigating baryonic interactions and quark behavior involving up, down, and strange quarks. However, experimental measurements of its lifetime and binding energy, key indicators of baryonic interactions, show significant deviations in results obtained from energetic collisions of heavy-ion beams. Identifying alternative pathways for precisely measuring the hypertriton's binding energy and lifetime is thus crucial for advancing experimental and theoretical nuclear physics. Here, we present an experimental study on the binding energies of $^3_Λ\mathrm{H}$ (hypertriton) and $^4_Λ\mathrm{H}$, performed through the analysis of photographic nuclear emulsions using modern techniques. By incorporating deep-learning methods, we uncovered systematic uncertainties in conventional nuclear emulsion analyses and established a refined calibration protocol for determining binding energies accurately. Our results are independent of those obtained from heavy-ion collision experiments, offering a complementary measurement and opening new avenues for investigating few-body hypernuclei interactions.

nucl-ex↗

arXiv2024

A novel machine learning method to detect double-$Λ$ hypernuclear events in nuclear emulsions

A novel method was developed to detect double-$Λ$ hypernuclear events in nuclear emulsions using machine learning techniques. The object detection model, the Mask R-CNN, was trained using images generated by Monte Carlo simulations, image processing, and image-style transformation based on generative adversarial networks. Despite being exclusively trained on $\prescript{6\ }{ΛΛ}{\rm{He}}$ events, the model achieved a detection efficiency of 93.8$\%$ for $\prescript{6\ }{ΛΛ}{\rm{He}}$ and 82.0$\%$ for $\prescript{5\ }{ΛΛ}{\rm{H}}$ events in the produced images. In addition, the model demonstrated its ability to detect the $\prescript{6\ }{ΛΛ}{\rm{He}}$ event named the Nagara event, which is the only uniquely identified double-$Λ$ hypernuclear event reported to date. It also exhibited a proper segmentation of the event topology. Furthermore, after analyzing 0.2$\%$ of the entire emulsion data from the J-PARC E07 experiment utilizing the developed approach, six new candidates for double-$Λ$ hypernuclear events were detected, suggesting that more than 2000 double-strangeness hypernuclear events were recorded in the entire dataset. This method is sufficiently effective for mining more latent double-$Λ$ hypernuclear events recorded in nuclear emulsion sheets by reducing the time required for manual visual inspection by a factor of five hundred.

hep-ex↗