MACRIB High efficiency - high purity hadron identification for DELPHI

Analysis of the data shows that hadron tags of the two standard DELPHI particle identification packages RIBMEAN and HADSIGN are weakly correlated. This led to the idea of constructing a neural network for both kaon and proton identification using as input the existing tags from RIBMEAN and HADSIGN, as well as preproccessed TPC and RICH detector measurements together with additional dE/dx information from the DELPHI vertex detector. It will be shown in this note that the net output is much more efficient at the same purity than the HADSIGN or RIBMEAN tags alone. We present an easy-to-use routine performing the necessary calculations

A neural network z-vertex trigger for Belle II

We present the concept of a track trigger for the Belle II experiment, based on a neural network approach, that is able to reconstruct the z (longitudinal) position of the event vertex within the latency of the first level trigger. The trigger will thus be able to suppress a large fraction of the dominating background from events outside of the interaction region. The trigger uses the drift time information of the hits from the Central Drift Chamber (CDC) of Belle II within narrow cones in polar and azimuthal angle as well as in transverse momentum (sectors), and estimates the z-vertex without explicit track reconstruction. The preprocessing for the track trigger is based on the track information provided by the standard CDC trigger. It takes input from the 2D ($r - \varphi$) track finder, adds information from the stereo wires of the CDC, and finds the appropriate sectors in the CDC for each track in a given event. Within each sector, the z-vertex of the associated track is estimated by a specialized neural network, with a continuous output corresponding to the scaled z-vertex. The input values for the neural network are calculated from the wire hits of the CDC.Comment: Proceedings of the 16th International workshop on Advanced Computing and Analysis Techniques in physics research (ACAT), Preprint, reviewed version (only minor corrections

Scaling-aware rating of count forecasts

Forecast quality should be assessed in the context of what is possible in theory and what is reasonable to expect in practice. Often, one can identify an approximate upper bound to a probabilistic forecast's sharpness, which sets a lower, not necessarily achievable, limit to error metrics. In retail forecasting, a simple, but often unconquerable sharpness limit is given by the Poisson distribution. When evaluating forecasts using traditional metrics such as Mean Absolute Error, it is hard to judge whether a certain achieved value reflects unavoidable Poisson noise or truly indicates an overdispersed prediction model. Moreover, every evaluation metric suffers from precision scaling: Perhaps surprisingly, the metric's value is mostly defined by the selling rate and by the resulting rate-dependent Poisson noise, and only secondarily by the forecast quality. For any metric, comparing two groups of forecasted products often yields "the slow movers are performing worse than the fast movers" or vice versa, the na\"ive scaling trap. To distill the intrinsic quality of a forecast, we stratify predictions into buckets of approximately equal predicted value and evaluate metrics separately per bucket. By comparing the achieved value per bucket to benchmarks, we obtain an intuitive visualization of forecast quality, which can be summarized into a single rating that makes forecast quality comparable among different products or even industries. The thereby developed scaling-aware forecast rating is applied to forecasting models used on the M5 competition dataset as well as to real-life forecasts provided by Blue Yonder's Demand Edge for Retail solution for grocery products in Sainsbury's supermarkets in the United Kingdom. The results permit a clear interpretation and high-level understanding of model quality by non-experts.Comment: 39 pages, 11 figures; improved introduction and outloo

Cerebellar Degeneration as Presenting Symptom of Recurrent Endometrial Stromal Sarcoma with Sex-Cord Elements

We report a 66-year-old woman with slowly progressive ataxia due to cerebellar atrophy. Imaging studies revealed multiple lesions in both the lungs and dorsal subpleural space. A biopsy identified the lesions as metastases of a low-grade endometrial stromal sarcoma containing sex-cord elements. The histological appearance was identical to a uterine tumor the patient was treated for with hysterectomy 16 years before. The metastases were removed surgically, and after 3 months ataxia had regressed. We conclude that the presenting cerebellar degeneration in this patient resulted from the metastatic recurrence of the endometrial tumor

Two Photon Couplings of Hybrid Mesons

A new formalism is developed for the two photon production of hybrid mesons via intermediate hadronic decays. In an adiabatic and non-relativistic context with spin 1 pair creation we obtain the first absolute estimates of unmixed hybrid production strengths to be small (0.03 - 3 eV) in relation to experimental meson widths (0.1 - 5 keV). Within this context, two photon experiments at Babar, Cleo II, LEP2 and LHC therefore strongly discriminate between hybrid and conventional meson wave function components, filtering out conventional meson components. Decay widths of unmixed hybrids vanish. Conventional meson two photon widths are roughly in agreement with experiment.Comment: 15 pages, LaTeX, 2 postscript figures, uses epsf. Rewritten for clarificatio