Assessing Mediator Performance: The Usefulness of Participant Questionnaires

As part of their obligation to provide quality services, courts that offer mediation need to periodically assess the performance of mediators to whom they refer cases. One of several methods that have been proposed for monitoring mediator quality is participant assessments of mediator performance. The present article reports an empirical study that examined attorneys\u27 assessments of the skillfulness of mediators in a federal appellate civil medations program. The attorneys rated some of the mediators as being more skillful than others, and these differences generally remained whether or not favorable outcomes were achieved in mediation. In addition, the attorneys rated individual mediators as being more skillful on some dimensions than others. These finding suggest that participant assessments could provide an effective means for monitoring mediator performance. We conclude by discussing a number of factors that could affect the usefulness of participant assessments

Thoughts of a Chief Circuit Mediator on Federal Court-Annexed Mediation

Published in cooperation with the American Bar Association Section of Dispute Resolutio

Searches for Neutrinos from Gamma-Ray Bursts using the IceCube Neutrino Observatory

Gamma-ray bursts (GRBs) are considered as promising sources of ultra-high-energy cosmic rays (UHECRs) due to their large power output. Observing a neutrino flux from GRBs would offer evidence that GRBs are hadronic accelerators of UHECRs. Previous IceCube analyses, which primarily focused on neutrinos arriving in temporal coincidence with the prompt gamma rays, found no significant neutrino excess. The four analyses presented in this paper extend the region of interest to 14 days before and after the prompt phase, including generic extended time windows and targeted precursor searches. GRBs were selected between May 2011 and October 2018 to align with the data set of candidate muon-neutrino events observed by IceCube. No evidence of correlation between neutrino events and GRBs was found in these analyses. Limits are set to constrain the contribution of the cosmic GRB population to the diffuse astrophysical neutrino flux observed by IceCube. Prompt neutrino emission from GRBs is limited to $\lesssim$1% of the observed diffuse neutrino flux, and emission on timescales up to $10^4$ s is constrained to 24% of the total diffuse flux

All-flavor constraints on nonstandard neutrino interactions and generalized matter potential with three years of IceCube DeepCore data

We report constraints on nonstandard neutrino interactions (NSI) from the observation of atmospheric neutrinos with IceCube, limiting all individual coupling strengths from a single dataset. Furthermore, IceCube is the first experiment to constrain flavor-violating and nonuniversal couplings simultaneously. Hypothetical NSI are generically expected to arise due to the exchange of a new heavy mediator particle. Neutrinos propagating in matter scatter off fermions in the forward direction with negligible momentum transfer. Hence the study of the matter effect on neutrinos propagating in the Earth is sensitive to NSI independently of the energy scale of new physics. We present constraints on NSI obtained with an all-flavor event sample of atmospheric neutrinos based on three years of IceCube DeepCore data. The analysis uses neutrinos arriving from all directions, with reconstructed energies between 5.6 GeV and 100 GeV. We report constraints on the individual NSI coupling strengths considered singly, allowing for complex phases in the case of flavor-violating couplings. This demonstrates that IceCube is sensitive to the full NSI flavor structure at a level competitive with limits from the global analysis of all other experiments. In addition, we investigate a generalized matter potential, whose overall scale and flavor structure are also constrained

Strong Constraints on Neutrino Nonstandard Interactions from TeV-Scale νμ_{μ} Disappearance at IceCube

We report a search for nonstandard neutrino interactions (NSI) using eight years of TeV-scale atmospheric muon neutrino data from the IceCube Neutrino Observatory. By reconstructing incident energies and zenith angles for atmospheric neutrino events, this analysis presents unified confidence intervals for the NSI parameter εμτ. The best-fit value is consistent with no NSI at a p value of 25.2%. With a 90% confidence interval of −0.0041≤εμτ≤0.0031 along the real axis and similar strength in the complex plane, this result is the strongest constraint on any NSI parameter from any oscillation channel to date

Search for Astrophysical Neutrinos from 1FLE Blazars with IceCube

The majority of astrophysical neutrinos have undetermined origins. The IceCube Neutrino Observatory has observed astrophysical neutrinos but has not yet identified their sources. Blazars are promising source candidates, but previous searches for neutrino emission from populations of blazars detected in ≳GeV gamma rays have not observed any significant neutrino excess. Recent findings in multimessenger astronomy indicate that high-energy photons, coproduced with high-energy neutrinos, are likely to be absorbed and reemitted at lower energies. Thus, lower-energy photons may be better indicators of TeV–PeV neutrino production. This paper presents the first time-integrated stacking search for astrophysical neutrino emission from MeV-detected blazars in the first Fermi Large Area Telescope low energy (1FLE) catalog using ten years of IceCube muon–neutrino data. The results of this analysis are found to be consistent with a background-only hypothesis. Assuming an E$^{−2}$ neutrino spectrum and proportionality between the blazars MeV gamma-ray fluxes and TeV–PeV neutrino flux, the upper limit on the 1FLE blazar energy-scaled neutrino flux is determined to be 1.64 × 10$^{−12}$ TeV cm$^{−2}$ s$^{−1}$ at 90% confidence level. This upper limit is approximately 1% of IceCube’s diffuse muon–neutrino flux measurement

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 1-100 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 false positive rate (FPR), compared to current IceCube methods. Alternatively, the GNN offers a reduction of the FPR 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 1-30 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

Search for Astrophysical Neutrinos from 1FLE Blazars with IceCube

The origin of astrophysical neutrinos has yet to be determined. The IceCube Neutrino Observatory has observed astrophysical neutrinos but has not yet identified their sources. Blazars are promising source candidates, but previous searches for neutrino emission from populations of blazars detected in $\gtrsim$ GeV gamma-rays have not observed any significant neutrino excess. Recent findings in multi-messenger astronomy indicate that high-energy photons, co-produced with high-energy neutrinos, are likely to be absorbed and reemitted at lower energies. Thus, lower-energy photons may be better indicators of TeV-PeV neutrino production. This paper presents the first time-integrated stacking search for astrophysical neutrino emission from MeV-detected blazars in the first Fermi-LAT low energy catalog (1FLE) using ten years of IceCube muon-neutrino data. The results of this analysis are found to be consistent with a background-only hypothesis. Assuming an E${-2}$ neutrino spectrum and proportionality between the blazars' MeV gamma-ray fluxes and TeV-PeV neutrino flux, the upper limit on the 1FLE blazar energy-scaled neutrino flux is determined to be $1.64 \times 10{-12}$ TeV cm${-2}$ s${-1}$ at 90% confidence level. This upper limit is approximately 1% of IceCube's diffuse muon-neutrino flux measurement

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