Measurement of Neutrino Oscillation Parameters from Muon Neutrino Disappearance with an Off-Axis Beam

The T2K collaboration reports a precision measurement of muon neutrino disappearance with an off-axis neutrino beam with a peak energy of 0.6 GeV. Near detector measurements are used to constrain the neutrino flux and cross section parameters. The Super-Kamiokande far detector, which is 295 km downstream of the neutrino production target, collected data corresponding to 3.01×1020 protons on target. In the absence of neutrino oscillations, 205±17 (syst.) events are expected to be detected and only 58 muon neutrino event candidates are observed. A fit to the neutrino rate and energy spectrum assuming three neutrino flavors, normal mass hierarchy and θ23≤π/4 yields a best-fit mixing angle sin2(2θ23)=1.000 and mass splitting |Δm232|=2.44×10−3 eV2/c4. If θ23≥π/4 is assumed, the best-fit mixing angle changes to sin2(2θ23)=0.999 and the mass splitting remains unchanged.

Observation of Electron Neutrino Appearance in a Muon Neutrino Beam

The T2K experiment has observed electron neutrino appearance in a muon neutrino beam produced 295 km from the Super-Kamiokande detector with a peak energy of 0.6 GeV. A total of 28 electron neutrino events were detected with an energy distribution consistent with an appearance signal, corresponding to a significance of 7.3σ when compared to 4.92 ± 0.55 expected background events. In the PMNS mixing model, the electron neutrino appearance signal depends on several parameters including three mixing angles θ12, θ23, θ13, a mass difference Δm232 and a CP violating phase δCP. In this neutrino oscillation scenario, assuming |Δm232|=2.4×10−3 eV2, sin2θ23=0.5, δCP=0, and Δm232>0 (Δm232<0), a best-fit value of sin22θ13 = 0.140+0.038−0.032 (0.170+0.045−0.037) is obtained.

Towards understanding thermal jet quenching via lattice simulations

After reviewing how simulations employing classical lattice gauge theory permit to test a conjectured Euclideanization property of a light-cone Wilson loop in a thermal non-Abelian plasma, we show how Euclidean data can in turn be used to estimate the transverse collision kernel, C(k⊥), characterizing the broadening of a high-energy jet. First results, based on data produced recently by Panero et al, suggest that C(k⊥) is enhanced over the known NLO result in a soft regime k⊥ < a few T. The shape of k3⊥ C(k⊥) is consistent with a Gaussian at small k⊥.

Strengths, weaknesses, opportunities and threats: A SWOT analysis of the ecosystem services framework

The ecosystem services concept (ES) is becoming a cornerstone of contemporary sustainability thought. Challenges with this concept and its applications are well documented, but have not yet been systematically assessed alongside strengths and external factors that influence uptake. Such an assessment could form the basis for improving ES thinking, further embedding it into environmental decisions and management. The Young Ecosystem Services Specialists (YESS) completed a Strengths–Weaknesses–Opportunities–Threats (SWOT) analysis of ES through YESS member surveys. Strengths include the approach being interdisciplinary, and a useful communication tool. Weaknesses include an incomplete scientific basis, frameworks being inconsistently applied, and accounting for nature's intrinsic value. Opportunities include alignment with existing policies and established methodologies, and increasing environmental awareness. Threats include resistance to change, and difficulty with interdisciplinary collaboration. Consideration of SWOT themes suggested five strategic areas for developing and implementing ES. The ES concept could improve decision-making related to natural resource use, and interpretation of the complexities of human-nature interactions. It is contradictory – valued as a simple means of communicating the importance of conservation, whilst also considered an oversimplification characterised by ambiguous language. Nonetheless, given sufficient funding and political will, the ES framework could facilitate interdisciplinary research, ensuring decision-making that supports sustainable development.

Characterization and source apportionment of organic aerosol using offline aerosol mass spectrometry

Field deployments of the Aerodyne Aerosol Mass Spectrometer (AMS) have significantly advanced real-time measurements and source apportionment of non-refractory particulate matter. However, the cost and complex maintenance requirements of the AMS make its deployment at sufficient sites to determine regional characteristics impractical. Furthermore, the negligible transmission efficiency of the AMS inlet for supermicron particles significantly limits the characterization of their chemical nature and contributing sources. In this study, we utilize the AMS to characterize the water-soluble organic fingerprint of ambient particles collected onto conventional quartz filters, which are routinely sampled at many air quality sites. The method was applied to 256 particulate matter (PM) filter samples (PM1, PM2:5, and PM10, i.e., PM with aerodynamic diameters smaller than 1, 2.5, and 10 μm, respectively), collected at 16 urban and rural sites during summer and winter. We show that the results obtained by the present technique compare well with those from co-located online measurements, e.g., AMS or Aerosol Chemical Speciation Monitor (ACSM). The bulk recoveries of organic aerosol (60–91 %) achieved using this technique, together with low detection limits (0.8 μg of organic aerosol on the analyzed filter fraction) allow its application to environmental samples. We will discuss the recovery variability of individual hydrocarbon ions, ions containing oxygen, and other ions. The performance of such data in source apportionment is assessed in comparison to ACSM data. Recoveries of organic components related to different sources as traffic, wood burning, and secondary organic aerosol are presented. This technique, while subjected to the limitations inherent to filter-based measurements (e.g., filter artifacts and limited time resolution) may be used to enhance the AMS capabilities in measuring size-fractionated, spatially resolved longterm data sets.