Shota Ueda

activity

20202023

most citedDevelopment of a new wideband heterodyne receiver system for the Osaka 1.85-m mm-submm telescope -- Receiver development & the first light of simultaneous observation in 230GHz and 345GHz bands with an SIS-mixer with 4-21GHz IF output

11 citations · 36 across the 7 of their papers we have counts for

collaborators

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3 papers · 1 filter

astro-ph.GA2023

Predicting reliable H $_{2}$ column density maps from molecular line data using machine learning

Yoshito Shimajiri, Yasutomo Kawanishi, Shinji Fujita +13

The total mass estimate of molecular clouds suffers from the uncertainty in the H $_{2}$ -CO conversion factor, the so-called $X_{CO}$ factor, which is used to convert the $^{12}$ C…

astro-ph.GA2022★ 11 cited

Distance determination of molecular clouds in the 1st quadrant of the Galactic plane using deep learning : I. Method and Results

Shinji Fujita, A. M. Ito, Yusuke Miyamoto +15

Machine learning has been successfully applied in varied field but whether it is a viable tool for determining the distance to molecular clouds in the Galaxy is an open question. I…

astro-ph.GA2021★ 9 cited

Nobeyama 45 m Local Spur CO survey. I. Giant molecular filaments and cluster formation in the Vulpecula OB association

Mikito Kohno, Atsushi Nishimura, Shinji Fujita +13

We have performed new large-scale $^{12}$ CO, $^{13}$ CO, and C $^{18}$ O $J =$ 1-0 observations toward the Vulpecula OB association ( $l \sim 6 0^{\circ}$ ) as part of the Nobeyama 45 m Loca…

Predicting reliable H2​ column density maps from molecular line data using machine learning

Distance determination of molecular clouds in the 1st quadrant of the Galactic plane using deep learning : I. Method and Results

Nobeyama 45 m Local Spur CO survey. I. Giant molecular filaments and cluster formation in the Vulpecula OB association

Predicting reliable H $_{2}$ column density maps from molecular line data using machine learning