Electron beam test of key elements of the laser-based calibration system for the muon g - 2 experiment

We report the test of many of the key elements of the laser-based calibration system for muon g - 2 experiment E989 at Fermilab. The test was performed at the Laboratori Nazionali di Frascati's Beam Test Facility using a 450 MeV electron beam impinging on a small subset of the final g - 2 lead-fluoride crystal calorimeter system. The calibration system was configured as planned for the E989 experiment and uses the same type of laser and most of the final optical elements. We show results regarding the calorimeter's response calibration, the maximum equivalent electron energy which can be provided by the laser and the stability of the calibration system components

Graph Neural Networks for low-energy event classification & reconstruction in IceCube

IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a central role in the analysis of data from IceCube. Reconstructing and classifying events is a challenge due to the irregular detector geometry, inhomogeneous scattering and absorption of light in the ice and, below 100 GeV, the relatively low number of signal photons produced per event. To address this challenge, it is possible to represent IceCube events as point cloud graphs and use a Graph Neural Network (GNN) as the classification and reconstruction method. The GNN is capable of distinguishing neutrino events from cosmic-ray backgrounds, classifying different neutrino event types, and reconstructing the deposited energy, direction and interaction vertex. Based on simulation, we provide a comparison in the 1 GeV–100 GeV energy range to the current state-of-the-art maximum likelihood techniques used in current IceCube analyses, including the effects of known systematic uncertainties. For neutrino event classification, the GNN increases the signal efficiency by 18% at a fixed background rate, compared to current IceCube methods. Alternatively, the GNN offers a reduction of the background (i.e. false positive) rate by over a factor 8 (to below half a percent) at a fixed signal efficiency. For the reconstruction of energy, direction, and interaction vertex, the resolution improves by an average of 13%–20% compared to current maximum likelihood techniques in the energy range of 1 GeV–30 GeV. The GNN, when run on a GPU, is capable of processing IceCube events at a rate nearly double of the median IceCube trigger rate of 2.7 kHz, which opens the possibility of using low energy neutrinos in online searches for transient events.Peer Reviewe

A muon-track reconstruction exploiting stochastic losses for large-scale Cherenkov detectors

IceCube is a cubic-kilometer Cherenkov telescope operating at the South Pole. The main goal of IceCube is the detection of astrophysical neutrinos and the identification of their sources. High-energy muon neutrinos are observed via the secondary muons produced in charge current interactions with nuclei in the ice. Currently, the best performing muon track directional reconstruction is based on a maximum likelihood method using the arrival time distribution of Cherenkov photons registered by the experiment\u27s photomultipliers. A known systematic shortcoming of the prevailing method is to assume a continuous energy loss along the muon track. However at energies >1 TeV the light yield from muons is dominated by stochastic showers. This paper discusses a generalized ansatz where the expected arrival time distribution is parametrized by a stochastic muon energy loss pattern. This more realistic parametrization of the loss profile leads to an improvement of the muon angular resolution of up to 20% for through-going tracks and up to a factor 2 for starting tracks over existing algorithms. Additionally, the procedure to estimate the directional reconstruction uncertainty has been improved to be more robust against numerical errors

Measurement of the Positive Muon Anomalous Magnetic Moment to 0.46 ppm

We present the first results of the Fermilab Muon g-2 Experiment for the positive muon magnetic anomaly $a_\mu \equiv (g_\mu-2)/2$. The anomaly is determined from the precision measurements of two angular frequencies. Intensity variation of high-energy positrons from muon decays directly encodes the difference frequency $\omega_a$ between the spin-precession and cyclotron frequencies for polarized muons in a magnetic storage ring. The storage ring magnetic field is measured using nuclear magnetic resonance probes calibrated in terms of the equivalent proton spin precession frequency ${\tilde{\omega}'^{}_p}$ in a spherical water sample at 34.7$^{\circ}$C. The ratio $\omega_a / {\tilde{\omega}'^{}_p}$, together with known fundamental constants, determines $a_\mu({\rm FNAL}) = 116\,592\,040(54)\times 10^{-11}$ (0.46\,ppm). The result is 3.3 standard deviations greater than the standard model prediction and is in excellent agreement with the previous Brookhaven National Laboratory (BNL) E821 measurement. After combination with previous measurements of both $\mu^+$ and $\mu^-$, the new experimental average of $a_\mu({\rm Exp}) = 116\,592\,061(41)\times 10^{-11}$ (0.35\,ppm) increases the tension between experiment and theory to 4.2 standard deviationsComment: 10 pages; 4 figure

The calibration system of the new g-2 experiment at Fermilab

The muon anomaly ( g 12 2 ) \u3bc / 2 has been measured to 0.54 parts per million by E821 experiment at Brookhaven National Laboratory, and at present there is a 3\u20134 standard-deviation difference between the Standard Model prediction and the experimental value. A new muon g 122 experiment, E989, is being prepared at Fermilab that will improve the experimental error by a factor of four to clarify this difference. A central component to reach this fourfold improvement in accuracy is the high-precision laser calibration system which should monitor the gain fluctuations of the calorimeter photodetectors at 0.04% accuracy