Continuous Monitoring of STAR\u27s Main Time Projection Chamber

STAR refers to the Solenoidal Tracking instrument At RHIC (the Relativistic Heavy Ion Collider). For momenta above 500 MeV/c charged kaons are not separated from pions within STAR\u27s Main TPC (Time Projection Chamber) by track density alone and they are poorly separated below 500 MeV/c, even when using information from other sources like the vertex tracker. Within the TPC large numbers of kaons and pions decay into muons (and undetected neutrinos). Earlier work has shown parent pions and kaons whose decays are detected within a TPC may be distinguished uniquely from each other in a two-dimensional plot of muon-emission angle versus momentum difference (between each parent meson and its decay muon). Since pions and kaons have zero spin, each muon decay-product emerges isotropically in its parent meson\u27s rest frame. Identification of particle type provides the parent meson\u27s rest mass and, thus, its total energy. This means the measurement of each decay event is kinematically complete. Thus, Lorentz Transformations may be used to transform each component of the decaying muon\u27s laboratory four-momentum into the rest frame of its parent meson, where the muon decay is isotropic. An aggregated plot of muon directions from many parent rest frames will be isotropic in each (selected) sub-volume of the TPC unless there is a problem within the TPC or in its tracking algorithms. Continuous monitoring of a TPC is possible using this subset of detected charged particles

Coplanarity Test for Selecting a Pair of Charged-Particle Tracks Resulting from a Single Neutral-Particle Decay

It is hard to determine directly the position of a neutral subatomic particle, but when such a particle decays into a pair of charged particles, it is easy to determine the positions of the charged decay particles and thereby infer the position of the parent particle at the time of its decay. A minimum of two coordinate points for each of the two decay particles is needed to reconstruct the position of the parent vertex. The mathematics of the reconstruction process is inherently interesting, and it can be used to demonstrate to students the utility of some of the most fundamental ideas of vector analysis

Curing a Summing Error That Occurs Automatically When Fitting a Function to Binomial or Poisson Distributed Data

Without special precautions a sum-rule error occurs automatically when a chi-squared procedure is used to fit a funtion to binomial or Poisson distributed histogram data if the function has at least one linear parameter. Since the square of the variance per channel is equal to the mean population, errors are usually approximated using (G2~=yi\u3e0)}; this choice for approximating the variance gives a per-channel error weighting of 1/yi that automatically results in a sum-rule error. This sum-rule error consistently and systematically underestimates the total sum of the data points by an amount equal to the value of %*, resulting in Zjyj-Zjfj= J& where %i = £j(vi - QVyi an^ f\u27i= f(Xj,{parameters}). In contrast, using {o\u27-f=(¦\u3e()} gives the error weighting per channel of 1/fj that automatically results in a less well known sum rule error. This sum-rule error which is only half as large but opposite insign consistently and systematically overestimates the total sum ofthe data X? points by an amount equal to half the value of %?, that is, itresults in - Zjf- =- L y ,where Xr = (vi \u27i) \u27/\u27i- The good news is a combination of error weightings may be constructed which completely eliminates the otherwise automatically cocuring sum-rule error by taking advantage of cancellations occuring between the two sum-rule errors implicit in the two above-mentioned approaches to error-weighting per channel. This fortunitous linear combination ofsum-rule error swill combine and cancel ifthe fitting funtion is a sufficiently viable choice so that Xr= Xy = v (number ofdegrees of freedom); 1 2 consequently a weighted linear combination of these two definitions may be used, X 2 = 3 Xy + !%?• This choice for X = is 1 1 2 equivalent to choosing an error weighting of „\u27_\u27 = 3yj +:\u27\u3e(; ,and it essentially eliminates summing errors so that Xjyi- Zjf-. An alternate method is presented and proven for {jLt; = f-}infitting a function using Maximum Likelihood

Separating K+/- from Pi+/- using In-Flight Decays to Mu+/- + Nu

A method is presented for completely distinguishing between charged kaons and charged pions by using their charged muon (plus neutrino) decays (with neutrinos undetected) for meson laboratory momenta up to 1000 MeV/c. When either a charged kaon or a charged pion decays into a muon and a neutrino, momentum-energy (four-momentum) conservation will be used to provide unique kinematic trajectories for distinguishing kaon decays from pion decays when the change in three-momentum of the muon from that of either parent kaon or pion is measured (or simulated). Ina magnetic field, observation of a tracked particle showing a kink and/or a change in helicity indicates the decay of the parent particle into a similarly charged muon product. Unique kinematic separation between each parent kaon and parent pion is possible for each parent particle\u27s momentum up to 1000 MeV/c. Curvature-radius of the helical path in a magnetic field is used to determine each charged particle\u27s momentum, whether it be a kaon, a pion or a muon. A weak field is adequate for making this determination since momentum (curvature radius) need only be measured to an accuracy of about 10%. Monte Carlo calculations of the kineatic trajectories have been carried out for primary meson momenta between 0 and 1000 MeV/c and for a range of emission angles (or kinks ) between 0° and 180°. Monte Carlo results from these in-flight decay kinematic calculations show a complete separation is possible for pion decays from kaon decays for laboratory momenta up to 1000 MeV/c because these two classes of meson decays cluster into completely separated 2-D regions of difference-momentum (x)muon-angle space. The most difficult region for separating primary particles occurs for small-kink decays within less than 5°. Decay halflife and time dilation require an efficient time projection chamber to be fairly large, because kaons are strongly favored over pions at the higher laboratory momenta and for the smaller time projection chamber geometries

An investigative study of a spectrum-matching imaging system Final report

Evaluation system for classification of remote objects and materials identified by solar and thermal radiation emissio

Bias adjustment of satellite-based precipitation estimation using gauge observations: A case study in Chile

Satellite-based precipitation estimates (SPEs) are promising alternative precipitation data for climatic and hydrological applications, especially for regions where ground-based observations are limited. However, existing satellite-based rainfall estimations are subject to systematic biases. This study aims to adjust the biases in the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS) rainfall data over Chile, using gauge observations as reference. A novel bias adjustment framework, termed QM-GW, is proposed based on the nonparametric quantile mapping approach and a Gaussian weighting interpolation scheme. The PERSIANN-CCS precipitation estimates (daily, 0.04°×0.04°) over Chile are adjusted for the period of 2009–2014. The historical data (satellite and gauge) for 2009–2013 are used to calibrate the methodology; nonparametric cumulative distribution functions of satellite and gauge observations are estimated at every 1°×1° box region. One year (2014) of gauge data was used for validation. The results show that the biases of the PERSIANN-CCS precipitation data are effectively reduced. The spatial patterns of adjusted satellite rainfall show high consistency to the gauge observations, with reduced root-mean-square errors and mean biases. The systematic biases of the PERSIANN-CCS precipitation time series, at both monthly and daily scales, are removed. The extended validation also verifies that the proposed approach can be applied to adjust SPEs into the future, without further need for ground-based measurements. This study serves as a valuable reference for the bias adjustment of existing SPEs using gauge observations worldwide

Application of semiconductor industry cleaning technologies for Genesis sample collectors

Genesis array collectors recovered after the sub-nominal landing have been exposed to particulate and molecular contamination. In this study, semiconductor industry based cleaning technologies are being evaluated for their efficacy in contaminant removal

Merging high-resolution satellite-based precipitation fields and point-scale rain gauge measurements-A case study in Chile

With high spatial-temporal resolution, Satellite-based Precipitation Estimates (SPE) are becoming valuable alternative rainfall data for hydrologic and climatic studies but are subject to considerable uncertainty. Effective merging of SPE and ground-based gauge measurements may help to improve precipitation estimation in both better resolution and accuracy. In this study, a framework for merging satellite and gauge precipitation data is developed based on three steps, including SPE bias adjustment, gauge observation gridding, and data merging, with the objective to produce high-quality precipitation estimates. An inverse-root-mean-square-error weighting approach is proposed to combine the satellite and gauge estimates that are in advance adjusted and gridded, respectively. The model is applied and tested with the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) estimates (daily, 0.04° × 0.04°) over Chile, for the 6 year period of 2009-2014. Daily observations from about 90% of collected gauges over the study area are used for model calibration; the rest of the gauged data are regarded as ground “truth” for validation. Evaluation results indicate high effectiveness of the model in producing high-resolution-precision precipitation data. Compared to reference data, the merged data (daily) show correlation coefficients, probabilities of detection, root-mean-square errors, and absolute mean biases that were consistently improved from the original PERSIANN-CCS estimates. The cross-validation evidences that the framework is effective in providing high-quality estimates even over nongauged satellite pixels. The same method can be applied globally and is expected to produce precipitation products in near real time by integrating gauge observations with satellite estimates