Title:
Search for D0 decays to invisible final states with Charm tagging method at Belle
Speaker:
Dr. Lai, Yun-Tsung (National Taiwan University)
Time: notice: regular time adjusted
03/18 (Sat.) 18:00 PDT, 19:00 MDT, 20:00 CDT, 21:00 EDT,
03/19 (Sun.) 09:00 Taiwan
Link:
Abstract:
The Belle experiment is one of the two B factory experiments in the world, and
it has collected data sample of ~710 fb-1 at the Υ(4S) resonances (total
amount of data sample is ~1 ab-1) with the Belle detector at the KEKB
asymmetric-energy e+e- collider. The research focus of Belle is the CP
violation involved in the B meson decay, rare decay of B meson, etc. Now the
members are working for the upgrade of Belle: the Belle II experiment with the
SuperKEKB collider. The first run is scheduled at the end of 2017. The
SuperKEKB is expected to have a 40 times larger instantaneous luminosity than
KEKB. With much larger amount of data, our target is to probe new physics in
the rare B decay.
In this talk, besides the introduction the KEKB collider and the Belle detectors,
I will also talk about the search for D0 decays to invisible final states
with Charm flavor tagging method at Belle. In an e+e- flavor factory experiment,
two heavy-flavor mesons (such as B or D) would be produced in flavor-conjugate
states. By fully reconstructing one of the B or D meson, the recoil
information can be utilized to search for decays with invisible final state
particles. The design of the tagging method and details of the D0
→ invisible measurement will be reported.
Monday, March 13, 2017
Sunday, March 5, 2017
170311 Highly-resolved air pollution modeling and its impact on estimating on-road PM 2.5 related premature mortality—an example in central North Carolina
Title:
Highly-resolved air pollution modeling and its impact on estimating on-road PM 2.5 related premature mortality—an example in central North Carolina
Speaker:
Shih Ying “Changsy” Chang, PhD
(Univ. of North Carolina at Chapel Hill/Sonoma Technology, INC.)
Time:
03/11 (Sat.) 15:00 PST, 16:00 MST, 17:00 CST, 18:00 EST,
03/12 (Sun.) 07:00 Taiwan
Link:
part-1
part-2
Prerequisite knowledge:
none
Abstract:
Emission from onroad vehicles is a major contributor of air pollution-related premature death. Previous studies have estimated that onroad emissions in the U.S. cause 29,000 to 53,000 ozone and PM 2.5 -related premature deaths. In these studies, grid-based air quality chemical transport models (CTM) were used to provide ambient concentration estimates. Because these models were usually run at a relatively coarse spatial resolution (i.e. 36 km × 36 km or 12 km × 12 km), they fail to fully characterize the concentration hotspots at the proximity of emission sources and thus fail to capture high-risk areas. Several studies have shown that people living close to major roads have higher risk to develop respiratory diseases than those living several hundred meters away. To capture this sharp gradient and to improve characterization of the exposure and risk from traffic-related air pollutants, spatially resolved concentration is required. However, estimating concentration at a high spatial resolution is challenging for large-scale application and is rarely used for estimating premature mortality. In this study, we compared the on-road PM 2.5 related premature mortality in central North Carolina with two different concentration estimation approaches – a) using the Community Multiscale Air Quality (CMAQ) model, to model concentration at a coarser resolution of a 36 km × 36 km grid resolution, and b) using a hybrid of a Gaussian dispersion model, CMAQ, and a space-time interpolation technique to provide annual average PM 2.5 concentrations at a Census block level (~105,000 Census blocks). The hybrid modeling approach estimated 24% more on-road PM 2.5 related premature mortality than CMAQ. The major difference is from the primary on-road PM 2.5 where the hybrid approach estimated 2.5 times more primary on-road PM 2.5 related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on-road PM 2.5 premature mortality occurs within 1,000 meters from roadways where 50% of the total population resides, highlighting the importance to characterize near-road primary PM 2.5 and suggesting that previous studies may have underestimated premature mortality due to PM 2.5 from traffic-related emissions.
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