Source Size Limitation from Variabilities of a Lensed Quasar

In the case of gravitationally-lensed quasars, it is well-known that there is a time delay between occurrence of the intrinsic variabilities in each split image. Generally, the source of variabilities has a finite size, and there are time delays even in one image. If the origin of variabilities is widely distributed, say over \gsim 100 pc as whole, variabilities between split images will not show a good correlation even though their origin is identical. Using this fact, we are able to limit the whole source size of variabilities in a quasar below the limit of direct resolution by today's observational instruments.Comment: 15 pages LaTeX, 3 figures, accepted to ApJ Letter. e-mail: [email protected]

Helical channel design and technology for cooling of muon beams

Novel magnetic helical channel designs for capture and cooling of bright muon beams are being developed using numerical simulations based on new inventions such as helical solenoid (HS) magnets and hydrogen-pressurized RF (HPRF) cavities. We are close to the factor of a million six-dimensional phase space (6D) reduction needed for muon colliders. Recent experimental and simulation results are presented.Comment: 6 pp. 14th Advanced Accelerator Concepts Workshop 13-19 Jun 2010: Annapolis, Marylan

Recurrence of the blue wing enhancements in the high ionization lines of SDSS 1004+4112 A

We present integral field spectroscopic observations of the quadruple-lensed QSO SDSS 1004+4112 taken with the fiber system INTEGRAL at the William Herschel Telescope on 2004 January 19. In May 2003 a blueward enhancement in the high ionization lines of SDSS 1004+4112A was detected and then faded. Our observations are the first to note a second event of similar characteristics less than one year after. Although initially attributed to microlensing, the resemblance among the spectra of both events and the absence of microlensing-induced changes in the continuum of component A are puzzling. The lack of a convincing explanation under the microlensing or intrinsic variability hypotheses makes the observed enhancements particularly relevant, calling for close monitoring of this object.Comment: 4 pages, 5 figure

Integration of Multi-Sensor Data to Estimate Plot-Level Stem Volume Using Machine Learning Algorithms–Case Study of Evergreen Conifer Planted Forests in Japan

The development of new methods for estimating precise forest structure parameters is essential for the quantitative evaluation of forest resources. Conventional use of satellite image data, increasing use of terrestrial laser scanning (TLS), and emerging trends in the use of unmanned aerial systems (UASs) highlight the importance of modern technologies in the realm of forest observation. Each technology has different advantages, and this work seeks to incorporate multiple satellite, TLS- and UAS-based remote sensing data sets to improve the ability to estimate forest structure parameters. In this paper, two regression analysis approaches are considered for the estimation: random forest regression (RFR) and support vector regression (SVR). To collect the dependent variable, in situ measurements of individual tree parameters (tree height and diameter at breast height (DBH)) were taken in a Japanese cypress forest using the nondestructive TLS method, which scans the forest to obtain dense and accurate point clouds under the tree canopy. Based on the TLS data, the stem volume was then computed and treated as ground truth information. Topographic and UAS information was then used to calculate various remotely sensed explanatory variables, such as canopy size, canopy cover, and tree height. Canopy cover and canopy shapes were computed via the orthoimages derived from the UAS and watershed segmentation method, respectively. Tree height was computed by combining the digital surface model (DSM) from the UAS and the digital terrain model (DTM) from the TLS data. Topographic variables were computed from the DTM. The backscattering intensity in the satellite imagery was obtained based on L-band (Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2)) and C-band (Sentinel-1) synthetic aperture radar (SAR). All satellite (10–25 m resolution), TLS (3.4 mm resolution) and UAS (2.3–4.6 cm resolution) data were then combined, and RFR and SVR were trained; the resulting predictive powers were then compared. The RFR method yielded fitting R2 up to 0.665 and RMSE up to 66.87 m3/ha (rRMSE = 11.95%) depending on the input variables (best result with canopy height, canopy size, canopy cover, and Sentinel-1 data), and the SVR method showed fitting R2 up to 0.519 and RMSE up to 80.12 m3/ha (rRMSE = 12.67%). The RFR outperformed the SVR method, which could delineate the relationship between the variables for better model accuracy. This work has demonstrated that incorporating various remote sensing data to satellite data, especially adding finer resolution data, can provide good estimates of forest parameters at a plot level (10 by 10 m), potentially allowing advancements in precision forestry