arXiv · 0709.0147
Longitudinal spin physics program at RHIC-PHENIX is introduced. Recent results of pi0 cross section and A_LL are presented and discussed.
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K. Aoki. 2007-09-03. Spin Physics Program at RHIC-PHENIX. https://arxiv.org/abs/0709.0147
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Using the data sample of $(2712.4\pm14.3)\times 10^6$ $ψ(2S)$ events collected with the BESIII detector at the center-of-mass energy $\sqrt{s}=3.686\ \rm{GeV}$, we search for the weak decays $ψ(2S)\to D_s^-π^+ + c.c.$ and $ψ(2S)\to D_s^-ρ^+ + c.c.$ for the first time. Since no signal excess above the background is observed, the upper limits on the branching fractions at the 90\% confidence level are set to be $1.4\times 10^{-6}$ and $7.0\times 10^{-6}$ for $ψ(2S)\to D_s^-π^+ + c.c.$ and $ψ(2S)\to D_s^-ρ^+ + c.c.$, respectively.
The forthcoming Hyper-Kamiokande experiment is expected to achieve unprecedented sensitivity to neutrino oscillations. Meeting these precision goals requires substantially larger Monte Carlo datasets than previous experiments to satisfy stringent systematic-uncertainty requirements. While traditional maximum-likelihood reconstruction provides high-quality event reconstruction, its per-event computational cost makes reconstruction of large, systematically varied Monte Carlo samples increasingly impractical. We demonstrate a neural-network-based reconstruction approach for the Hyper-Kamiokande far detector using simulated data with the experiment's detector geometry. Single-particle events are generated with kinetic energies from the Cherenkov threshold up to 2 GeV above threshold. The resulting PMT charge and timing information are mapped to $274\times144$ five-channel images that serve as inputs to ResNet models in the WatChMaL framework. These models (i) classify events into four particle hypotheses ($e$, $μ$, $γ$, $π^{0}$) and (ii) regress the interaction vertex, direction, and momentum of electrons and muons. The regression models achieve momentum resolutions of $1.27\%$ for muons and $2.21\%$ for electrons, angular resolutions of $0.91^\circ$ and $1.60^\circ$, and vertex resolutions of $13.6\,\mathrm{cm}$ and $17.0\,\mathrm{cm}$, better than those reported for traditional methods. The classifier improves $e$-$μ$, $e$-$γ$, and $e$-$π^{0}$ separation relative to the traditional baseline, with ROC AUC values of $0.9999995$, $0.642$, and $0.9659$. Our networks achieve inference times of 1-2 ms per event on a single GPU, yielding speed-ups of $3.2\times10^{4}$-$5.2\times10^{4}$ relative to likelihood-based reconstruction.
Searches for new physics in low-background experiments infer a non-negative signal strength from few events and often report an upper limit. Nominal frequentist coverage requires both a valid interval construction and an adequate data model. We study how controlled model departures affect lower- and upper-endpoint coverage for six interval procedures in an exact Poisson counting experiment, dark-matter recoil spectra, and a neutrinoless-double-beta-decay peak search. We introduce the Poisson--Fisher degeneracy index $\mathcal I_{\mathrm{PF}}(δν;\vartheta_0)=(β,γ)$, which maps a specified expected-count deformation, after projection onto the complete fitted tangent space, to $β$, the signed fitted signal shift in profiled standard-error units, and $γ$, the Poisson--Fisher norm of the unabsorbed residual. Locally, the sign of $β$ identifies the threatened endpoint, while larger $γ$ implies greater detectability by the saturated-Poisson goodness-of-fit test used here at a fixed $5\%$ type-I error rate. Across the studied deformations, positive signal-like bias degrades discovery-side coverage while making upper limits conservative; negative signal bias from overestimated signal efficiency can make the upper endpoint undercover. Calibration under the nominal simulator does not protect against misspecification of that simulator relative to the data-generating process. A plausible deformation with large $|β|$ and small $γ$ may therefore evade diagnosis and should be represented by a nuisance constrained with auxiliary information or included in a defensible envelope. In the exactly collinear constrained-nuisance benchmark, modelling the deformation restores coverage at the evaluated grid points, at a quantifiable cost in interval sensitivity.