Suzaku Observations of M82 X-1 : Detection of a Curved Hard X-ray Spectrum

A report is presented on Suzaku observations of the ultra-luminous X-ray source X-1 in the starburst galaxy M82, made three time in 2005 October for an exposure of ~ 30 ks each. The XIS signals from a region of radius 3 around the nucleus defined a 2-10 keV flux of 2.1 x 10^-11 erg s-1 cm-2 attributable to point sources. The 3.2-10 keV spectrum was slightly more convex than a power-law with a photon index of 1.7. In all observations, the HXD also detected signals from M82 up to ~ 20 keV, at a 12-20 keV flux of 4.4 x 10^-12 erg s-1 cm-2 . The HXD spectrum was steeper than that of the XIS. The XIS and HXD spectra can be jointly reproduced by a cutoff power-law model, or similar curved models. Of the detected wide-band signals, 1/3 to 2/3 are attributable to X-1, while the remainder to other discrete sources in M82. Regardless of the modeling of these contaminants, the spectrum attributable to X-1 is more curved than a power-law, with a bolometric luminosity of (1.5 -3) x 10 ^40 erg s-1. These results are interpreted as Comptonized emission from a black hole of 100-200 solar masses, radiating roughly at the Eddington luminosity.Comment: 19 pages, 9 figures, accepted in Publications of the Astronomical Society of Japa

Regional Inequality Simulations Based on Asset Exchange Models with Exchange Range and Local Support Bias

To gain insights into the problem of regional inequality, we proposed new regional asset exchange models based on existing kinetic income-exchange models in economic physics. We did this by setting the spatial exchange range and adding bias to asset fraction probability in equivalent exchanges. Simulations of asset distribution and Gini coefficients showed that suppressing regional inequality requires, firstly an increase in the intra-regional economic circulation rate, and secondly the narrowing down of the exchange range (inter-regional economic zone). However, avoiding over-concentration of assets due to repeat exchanges requires adding a third measure; the local support bias (distribution norm). A comprehensive solution incorporating these three measures enabled shifting the asset distribution from over-concentration to exponential distribution and eventually approaching the normal distribution, reducing the Gini coefficient further. Going forward, we will expand these models by setting production capacity based on assets, path dependency on two-dimensional space, bias according to disparity, and verify measures to reduce regional inequality in actual communities.Comment: 14 pages, 8 figures. Published online at http://redfame.com/journal/index.php/aef/article/view/494

Unmanned aerial vehicles and deep learning for assessment of anthropogenic marine debris on beaches on an island in a semi-enclosed sea in Japan

The increasing prevalence of marine debris is a global problem, and urgent action for amelioration is needed. Identifying hotspots where marine debris accumulates will enable effective control; however, knowledge on the location of accumulation hotspots remains incomplete. In particular, marine debris accumulation on beaches is a concern. Surveys of beaches require intensive human effort, and survey methods are not standardized. If marine debris monitoring is conducted using a standardized method, data from different regions can be compared. With an unmanned aerial vehicle (UAV) and deep learning computational methods, monitoring a wide area at a low cost in a standardized way may be possible. In this study, we aimed to identify marine debris on beaches through deep learning using high-resolution UAV images by conducting a survey on Narugashima Island in the Seto Inland Sea of Japan. The flight altitude relative to the ground was set to 5 m, and images of a 0.81-ha area were obtained. Flight was conducted twice: before and after the beach cleaning. The combination of UAVs equipped with a zoom lens and operation at a low altitude allows for the acquisition of high resolution images of 1.1 mm/pixel. The training dataset (2970 images) was annotated by using VoTT, categorizing them into two classes: 'anthropogenic marine debris' and 'natural objects.' Using RetinaNet, marine debris was identified with an average sensitivity of 51% and a precision of 76%. In addition, the abundance and area of marine debris coverage were estimated. In this study, it was revealed that the combination of UAVs and deep learning enables the effective identification of marine debris. The effects of cleanup activities by citizens were able to be quantified. This method can widely be used to evaluate the effectiveness of citizen efforts toward beach cleaning and low-cost long-term monitoring