Experimental Requirements to Determine the Neutrino Mass Hierarchy Using Reactor Neutrinos

This paper presents experimental requirements to determine the neutrino mass hierarchy using reactor neutrinos. The detector shall be located at a baseline around 58 km from the reactor(s) to measure the energy spectrum of electron antineutrinos ($\bar{\nu}_e$) precisely. By applying Fourier cosine and sine transform to the L/E spectrum, features of the neutrino mass hierarchy can be extracted from the $|\Delta{m}^2_{31}|$ and $|\Delta{m}^2_{32}|$ oscillations. To determine the neutrino mass hierarchy above 90% probability, requirements to the baseline, the energy resolution, the energy scale uncertainty, the detector mass and the event statistics are studied at different values of $\sin^2(2\theta_{13})$Comment: Update Fig.

Structure-Preserving Graph Representation Learning

Though graph representation learning (GRL) has made significant progress, it is still a challenge to extract and embed the rich topological structure and feature information in an adequate way. Most existing methods focus on local structure and fail to fully incorporate the global topological structure. To this end, we propose a novel Structure-Preserving Graph Representation Learning (SPGRL) method, to fully capture the structure information of graphs. Specifically, to reduce the uncertainty and misinformation of the original graph, we construct a feature graph as a complementary view via k-Nearest Neighbor method. The feature graph can be used to contrast at node-level to capture the local relation. Besides, we retain the global topological structure information by maximizing the mutual information (MI) of the whole graph and feature embeddings, which is theoretically reduced to exchanging the feature embeddings of the feature and the original graphs to reconstruct themselves. Extensive experiments show that our method has quite superior performance on semi-supervised node classification task and excellent robustness under noise perturbation on graph structure or node features.Comment: Accepted by the IEEE International Conference on Data Mining (ICDM) 2022. arXiv admin note: text overlap with arXiv:2108.0482

A New Optical Model for Photomultiplier Tubes

It is critical to construct an accurate optical model of photomultiplier tubes (PMTs) in many applications to describe the angular and spectral responses of the photon detection efficiency (PDE) of the PMTs in their working media. In this study, we propose a new PMT optical model to describe both light interactions with the PMT window and optical processes inside PMTs with reasonable accuracy based on the optics theory and a GEANT4-based simulation toolkit. The proposed model builds a relationship between the PDE and the underlying processes that the PDE relies on. This model also provides a tool to transform the PDE measured in one working medium (like air) to the PDE in other media (like water, liquid scintillator, etc). Using two 20" MCP-PMTs and one 20" dynode PMT, we demonstrate a complete procedure to obtain the key parameters used in the model from experimental data, such as the optical properties of the antireflective coating and photocathode of the three PMTs. The proposed model can effectively reproduce the angular responses of the quantum efficiency of PMTs, even though an ideally uniform photocathode is assumed in the model. Interestingly, the proposed model predicts a similar level ($20\%\sim30\%$) of light yield excess observed in the experimental data of many liquid scintillator-based neutrino detectors, compared with that predicted at the stage of detector design. However, this excess has never been explained, and the proposed PMT model provides a good explanation for it, which highlights the imperfections of PMT models used in their detector simulations

Self-Supervision Can Be a Good Few-Shot Learner

Existing few-shot learning (FSL) methods rely on training with a large labeled dataset, which prevents them from leveraging abundant unlabeled data. From an information-theoretic perspective, we propose an effective unsupervised FSL method, learning representations with self-supervision. Following the InfoMax principle, our method learns comprehensive representations by capturing the intrinsic structure of the data. Specifically, we maximize the mutual information (MI) of instances and their representations with a low-bias MI estimator to perform self-supervised pre-training. Rather than supervised pre-training focusing on the discriminable features of the seen classes, our self-supervised model has less bias toward the seen classes, resulting in better generalization for unseen classes. We explain that supervised pre-training and self-supervised pre-training are actually maximizing different MI objectives. Extensive experiments are further conducted to analyze their FSL performance with various training settings. Surprisingly, the results show that self-supervised pre-training can outperform supervised pre-training under the appropriate conditions. Compared with state-of-the-art FSL methods, our approach achieves comparable performance on widely used FSL benchmarks without any labels of the base classes.Comment: ECCV 2022, code: https://github.com/bbbdylan/unisia