Predicting reliable H2_2 column density maps from molecular line data using machine learning

The total mass estimate of molecular clouds suffers from the uncertainty in the H$_2$-CO conversion factor, the so-called $X_{\rm CO}$ factor, which is used to convert the $^{12}$CO (1--0) integrated intensity to the H$_2$ column density. We demonstrate the machine learning's ability to predict the H$_2$ column density from the $^{12}$CO, $^{13}$CO, and C$^{18}$O (1--0) data set of four star-forming molecular clouds; Orion A, Orion B, Aquila, and M17. When the training is performed on a subset of each cloud, the overall distribution of the predicted column density is consistent with that of the Herschel column density. The total column density predicted and observed is consistent within 10\%, suggesting that the machine learning prediction provides a reasonable total mass estimate of each cloud. However, the distribution of the column density for values $> \sim 2 \times 10^{22}$ cm$^{-2}$, which corresponds to the dense gas, could not be predicted well. This indicates that molecular line observations tracing the dense gas are required for the training. We also found a significant difference between the predicted and observed column density when we created the model after training the data on different clouds. This highlights the presence of different $X_{\rm CO}$ factors between the clouds, and further training in various clouds is required to correct for these variations. We also demonstrated that this method could predict the column density toward the area not observed by Herschel if the molecular line and column density maps are available for the small portion, and the molecular line data are available for the larger areas.Comment: Accepted for publication in MNRA

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

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. In the Galaxy, the kinematic distance is commonly employed as the distance to a molecular cloud. However, there is a problem in that for the inner Galaxy, two different solutions, the ``Near'' solution, and the ``Far'' solution, can be derived simultaneously. We attempted to construct a two-class (``Near'' or ``Far'') inference model using a Convolutional Neural Network (CNN), a form of deep learning that can capture spatial features generally. In this study, we used the CO dataset toward the 1st quadrant of the Galactic plane obtained with the Nobeyama 45-m radio telescope (l = 62-10 degree, |b| < 1 degree). In the model, we applied the three-dimensional distribution (position-position-velocity) of the 12CO (J=1-0) emissions as the main input. The dataset with ``Near'' or ``Far'' annotation was made from the HII region catalog of the infrared astronomy satellite WISE to train the model. As a result, we could construct a CNN model with a 76% accuracy rate on the training dataset. By using the model, we determined the distance to molecular clouds identified by the CLUMPFIND algorithm. We found that the mass of the molecular clouds with a distance of < 8.15 kpc identified in the 12CO data follows a power-law distribution with an index of about -2.3 in the mass range of M >10^3 Msun. Also, the detailed molecular gas distribution of the Galaxy as seen from the Galactic North pole was determined.Comment: 29 pages, 12 figure

Structure of 136Sn and the Z = 50 magicity

The first 2+ excited state in the neutron-rich tin isotope 136Sn has been identified at 682(13) keV by measuring γ -rays in coincidence with the one proton removal channel from 137Sb. This value is higher than those known for heavier even-even N = 86 isotones, indicating the Z = 50 shell closure. It compares well to the first 2+ excited state of the lighter tin isotope 134Sn, which may suggest that the seniority scheme also holds for 136Sn. Our result confirms the trend of lower 2+ excitation energies of even-even tin isotopes beyond N = 82 compared to the known values in between the two doubly magic nuclei 100Sn and 132Sn. © The Author(s) 2014.published_or_final_versio

Investigating nuclear shell structure in the vicinity of 78Ni: Low-lying excited states in the neutron-rich isotopes 80,82Zn

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Spectroscopy of ¹⁷C via one-neutron knockout reaction

21st International Conference on Few-Body Problems in Physics, Chicago, IL, USA, May 18-22, 2015.A spectroscopic study of ¹⁷C was performed via the one-neutron knockout reaction of ¹⁸C on a carbon target at RIKEN-RIBF. Three unbound states at excitation energies of 2.66(2), 3.16(5), and 3.97(3) MeV (preliminary) were observed. The energies are compared with shell-model calculations and existing measurements to deduce their spin-parities. From the comparison, the states at 2.66(2) and 3.97(3) MeV are suggested to be 1/2⁻ and 3/2⁻, respectively. From its decay property, the state at 3.16(5) MeV is indicated to be 9/2⁺

Study of ¹⁹C by One-Neutron Knockout

21st International Conference on Few-Body Problems in Physics, Chicago, IL, USA, May 18-22, 2015.The spectroscopic structure of ¹⁹C, a prominent one-neutron halo nucleus, has been studied with a ²⁰C secondary beam at 290 MeV/nucleon and a carbon target. Neutron-unbound states populated by the one-neutron knockout reaction were investigated by means of the invariant mass method. The preliminary relative energy spectrum and parallel momentum distribution of the knockout residue, ¹⁹C∗, were reconstructed from the measured four momenta of the¹⁸C fragment, neutron, and beam. Three resonances were observed in the spectrum, which correspond to the states at Ex = 0.62(9), 1.42(10), and 2.89(10) MeV. The parallel momentum distributions for the 0.62-MeV and 2.89-MeV states suggest spin-parity assignments of 5/2⁺ and 1/2⁻, respectively. The 1.42-MeV state is in line with the reported 5/22⁺ state

Nuclear structure study for the neutron-rich nuclei beyond

The neutron-rich nuclei 136Sn and 132Cd have been studied in the purpose of nuclear structure for the nuclei beyond the doubly-magic nucleus 132Sn. The 2+1 → 0+ gs transitions were identified for these two nuclei using in-beam γ-ray spectroscopy in coincidence with one- and two-proton removal reactions, respectively, at the RIKEN Radioactive Isotope Beam Factory. The 2+ 1 state in 136Sn is found to be similar to that for 134Sn indicating the seniority scheme may also hold for the heavy tin isotopes beyond N = 82. For 132Cd, the 2+ 1 state provides the first spectroscopic information in the even-even nuclei locating in the region “southeast” of 132Sn and the result is discussed in terms of proton-neutron configuration mixing. In both these two nuclei, it was found that the valence neutrons play an essential role in their low-lying excitations

Nuclear structure study for the neutron-rich nuclei beyond 132Sn: In-beam gamma-ray spectroscopy of 136Sn and 132Cd

The neutron-rich nuclei 136Sn and 132Cd have been studied in the purpose of nuclear structure for the nuclei beyond the doubly-magic nucleus 132Sn. The 2+1 → 0+ gs transitions were identified for these two nuclei using in-beam γ-ray spectroscopy in coincidence with one- and two-proton removal reactions, respectively, at the RIKEN Radioactive Isotope Beam Factory. The 2+ 1 state in 136Sn is found to be similar to that for 134Sn indicating the seniority scheme may also hold for the heavy tin isotopes beyond N = 82. For 132Cd, the 2+ 1 state provides the first spectroscopic information in the even-even nuclei locating in the region “southeast” of 132Sn and the result is discussed in terms of proton-neutron configuration mixing. In both these two nuclei, it was found that the valence neutrons play an essential role in their low-lying excitations