arXiv subjects

Miaochen Jin

Publications and source records attributed to Miaochen Jin.

7 recordsLinked to original sources

arXiv2026

When CPT Violation Hides in Plain Sight: How CP Measurements Are Compromised and How to Fix Them

The extraction of the leptonic charge-parity (CP)-violating phase $\delta_{\rm CP}$ from long-baseline neutrino oscillation experiments rests on the assumption of charge-parity-time (CPT) conservation. We show that CPT violation, parametrized as an asymmetry $\delta\Delta m^2_{31} \equiv \Delta\bar{m}^2_{31} - \Delta m^2_{31}$ between neutrino and antineutrino mass splittings, induces an effective, energy-dependent phase shift $\phi_{\rm eff}(E)$ that is functionally degenerate with $\delta_{\rm CP}$ in the appearance asymmetry $\langle\Delta P\rangle$. This has a profound implication for long-baseline experiments, where the tension between T2K and NO$\nu$A CPT-conserving best-fit $\delta_{\rm CP}$ values can be significantly alleviated by a CPT-violating truth; and a CPT-conserving fit can miss the true CP phase entirely for $|\delta\Delta m^2_{31}|\gtrsim 0.3\times10^{-3}~\text{eV}^2$ for DUNE. We then demonstrate that atmospheric neutrino telescopes provide the natural tool to resolve this degeneracy: using existing data from IceCube-DeepCore (7.74 yr) and KM3NeT/ORCA-6 (433 kt-yr), we derive a world-leading constraint on CPT-violation at $| \delta\Delta m^2_{31}|\leq 0.57\times10^{-3}~\text{eV}^2$ at 90% CL. With the IceCube Upgrade and full ORCA detector, we can reach a $1\sigma$ constraint at $10^{-4}~\text{eV}^2$ within a decade, providing the independent CPT constraint needed to ensure that DUNE's $\delta_{\rm CP}$ measurement is unambiguous.

hep-ph↗

arXiv2026

Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)

The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning (AI/ML), silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, novel codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing towards a successful transition to the new data frontier of fundamental science.

physics.ins-det↗

arXiv2025

Searching for sub-eV Sterile Neutrinos in Neutrino Telescopes

With the forthcoming deployment of IceCube-Upgrade, unprecedented statistics of atmospheric neutrinos in the energy range (1-100) GeV will become available, providing a valuable opportunity to probe physics beyond the Standard Model in the neutrino sector. In this study, we calculate the sensitivity of the IceCube-Upgrade to sterile neutrinos with mass-squared splittings $\lesssim 1~{\rm eV}^2$. We demonstrate that, particularly due to the (1-10) GeV energy window, $\nu_\mu-\nu_s$ mixing angles as small as $\sim5^\circ$ can be probed by IceCube-Upgrade for all mass-squared splittings below $1~{\rm eV}^2$. Furthermore, we investigate the potential impact of a sterile neutrino state on the precision determination of standard atmospheric neutrino mixing parameters in the IceCube-Upgrade.

hep-ph↗

arXiv2024

Comparison of Geometrical Layouts for Next-Generation Large-volume Cherenkov Neutrino Telescopes

Water-(Ice-) Cherenkov neutrino telescopes have played a pivotal role in the search and discovery of high-energy astrophysical neutrinos. Experimental collaborations are developing and constructing next-generation neutrino telescopes with improved optical modules (OMs) and larger geometrical volumes to increase their efficiency in the multi-TeV energy range and extend their reach to EeV energies. Although most existing telescopes share similar OM layouts, more layout options should be explored for next-generation detectors to maximize discovery capability. In this work, we study a set of layouts at different geometrical volumes and evaluate the signal event selection efficiency and reconstruction fidelity under both an only trigger-level linear regression algorithm and an offline Graph Neural Network (GNN) reconstruction. Our methodology and findings serve as first steps toward an optimized, global network of neutrino telescopes.

physics.ins-det↗

arXiv2024

Boosting Neutrino Mass Ordering Sensitivity with Inelasticity for Atmospheric Neutrino Oscillation Measurement

In this letter, we study the potential of boosting the atmospheric neutrino experiments sensitivity to the neutrino mass ordering (NMO) sensitivity by incorporating inelasticity measurements. We show how this observable improves the sensitivity to the NMO and the precision of other neutrino oscillation parameters relevant to atmospheric neutrinos, specifically in the IceCube-Upgrade and KM3NeT-ORCA detectors. Our results indicate that an oscillation analysis of atmospheric neutrinos including inelasticity information has the potential to enhance the ordering discrimination by several units of $χ^2$ in the assumed scenario of 5 and 3 years of running of IceCube-Upgrade and KM3NeT-ORCA detectors, respectively.

hep-ph↗

arXiv2023

Two Watts is All You Need: Enabling In-Detector Real-Time Machine Learning for Neutrino Telescopes Via Edge Computing

The use of machine learning techniques has significantly increased the physics discovery potential of neutrino telescopes. In the upcoming years, we are expecting upgrade of currently existing detectors and new telescopes with novel experimental hardware, yielding more statistics as well as more complicated data signals. This calls out for an upgrade on the software side needed to handle this more complex data in a more efficient way. Specifically, we seek low power and fast software methods to achieve real-time signal processing, where current machine learning methods are too expensive to be deployed in the resource-constrained regions where these experiments are located. We present the first attempt at and a proof-of-concept for enabling machine learning methods to be deployed in-detector for water/ice neutrino telescopes via quantization and deployment on Google Edge Tensor Processing Units (TPUs). We design a recursive neural network with a residual convolutional embedding, and adapt a quantization process to deploy the algorithm on a Google Edge TPU. This algorithm can achieve similar reconstruction accuracy compared with traditional GPU-based machine learning solutions while requiring the same amount of power compared with CPU-based regression solutions, combining the high accuracy and low power advantages and enabling real-time in-detector machine learning in even the most power-restricted environments.

hep-ex↗

arXiv2021

Influence of Water Vapor on the Interaction Between Dodecane Thiol Ligated Au Nanoparticles

It is well-known that the interaction between passivated nanoparticles can be tuned by their complete immersion in a chosen solvent, such as water. What remains unclear on a molecular level is how nanoparticle interactions may be altered in the presence of solvent vapor where complete immersion is not achieved. In this paper, we report an all-atom molecular dynamics simulation study of the change in pair potential of mean force between dodecane thiol ligated gold nanoparticles (AuNPs) when exposed to water vapor. With the equilibrium vapor pressure of water at 25 \degree C, there is very rapid condensation of water molecules onto the surface of the AuNPs in the form of mobile clusters of 100-2000 molecules that eventually coalesce into a few large clusters. When the distance between two AuNPs decreases, a water cluster bridging them provides an adhesive force that increases the depth and alters the shape of the pair-potential of mean force. That change of shape includes a decreased curvature near the minimum, consistent with experimental data showing that cyclic exposure to water vapor and its removal reversibly decreases and increases the Young's modulus of a freely suspended self-assembled monolayer of these AuNPs.

cond-mat.soft↗