More Like this

1. Neutrinoless double β decay with light sterile neutrinos: the contact terms

   https://doi.org/10.1007/JHEP04(2025)181  

   Cirigliano, Vincenzo; Dekens, Wouter; Quiroga, Sebastián Urrutia (April 2025, Journal of High Energy Physics)

   A<sc>bstract</sc> We study neutrinoless double-beta decay in extensions of the Standard Model that includenright-handed neutrino singlets, with massesm_sbelow the GeV scale. Generalizing recently developed matching methods, we determine them_sdependence of the short-rangenn→ppcouplings that appear to leading order in the chiral effective field theory description of neutrinoless double beta decay. We focus on two scenarios, corresponding to the minimalνSM and left-right symmetric models. We illustrate the impact of our new results in the case of theνSM, showing a significant impact on the neutrinoless double-beta decay half-life whenm_sis in the 200–800 MeV range. 

   more » « less
2. Optimal sparse eigenspace and low-rank density matrix estimation for quantum systems

   https://doi.org/https://doi.org/10.1016/j.jspi.2020.11.002  

   Cai, Tony; Kim, Donggyu; Song, Xinyu; Wang, Yazhen (July 2021, Journal of statistical planning and inference)

   Dette, Holger; Lee, Stephen; Pensky, Marianna (Ed.)

   Quantum state tomography, which aims to estimate quantum states that are described by density matrices, plays an important role in quantum science and quantum technology. This paper examines the eigenspace estimation and the reconstruction of large low-rank density matrix based on Pauli measurements. Both ordinary principal component analysis (PCA) and iterative thresholding sparse PCA (ITSPCA) estimators of the eigenspace are studied, and their respective convergence rates are established. In particular, we show that the ITSPCA estimator is rate-optimal. We present the reconstruction of the large low-rank density matrix and obtain its optimal convergence rate by using the ITSPCA estimator. A numerical study is carried out to investigate the finite sample performance of the proposed estimators. 

   more » « less
3. Symmetry plane correlations in Pb–Pb collisions at $${\varvec{\sqrt{s_\text {NN}}}} =2.76$$ TeV

   https://doi.org/10.1140/epjc/s10052-023-11658-w  

   Acharya, S.; Adamová, D.; Adler, A.; Aglieri Rinella, G.; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahmad, S.; Ahn, S. U.; Ahuja, I.; et al (July 2023, The European Physical Journal C)

   Abstract A newly developed observable for correlations between symmetry planes, which characterize the direction of the anisotropic emission of produced particles, is measured in Pb–Pb collisions at $$\sqrt{s_\text {NN}}$$ s NN  = 2.76 TeV with ALICE. This so-called Gaussian Estimator allows for the first time the study of these quantities without the influence of correlations between different flow amplitudes. The centrality dependence of various correlations between two, three and four symmetry planes is presented. The ordering of magnitude between these symmetry plane correlations is discussed and the results of the Gaussian Estimator are compared with measurements of previously used estimators. The results utilizing the new estimator lead to significantly smaller correlations than reported by studies using the Scalar Product method. Furthermore, the obtained symmetry plane correlations are compared to state-of-the-art hydrodynamic model calculations for the evolution of heavy-ion collisions. While the model predictions provide a qualitative description of the data, quantitative agreement is not always observed, particularly for correlators with significant non-linear response of the medium to initial state anisotropies of the collision system. As these results provide unique and independent information, their usage in future Bayesian analysis can further constrain our knowledge on the properties of the QCD matter produced in ultrarelativistic heavy-ion collisions. 

   more » « less
4. Neural Network Models for Ionospheric Electron Density Prediction at a Fixed Altitude Using Neural Architecture Search

   https://doi.org/10.1029/2024SW003945  

   Pan, Yang; Jin, Mingwu; Zhang, Shun‐Rong; Wing, Simon; Deng, Yue (August 2024, Space Weather)

   Abstract Specification and forecast of ionospheric parameters, such as ionospheric electron density (Ne), have been an important topic in space weather and ionospheric research. Neural networks (NNs) emerge as a powerful modeling tool forNeprediction. However, heavy manual adjustments are time consuming to determine the optimal NN structures. In this work, we propose to use neural architecture search (NAS), an automatic machine learning method, to mitigate this problem. NAS aims to find the optimal network structure through the alternate optimization of the hyperparameters and the corresponding network parameters within a pre‐defined hyperparameter search space. A total of 16‐year data from Millstone Hill incoherent scatter radar (ISR) are used for the NN models. One single‐layer NN (SLNN) model and one deep NN (DNN) model are both trained with NAS, namely SLNN‐NAS and DNN‐NAS, forNeprediction and compared with their manually tuned counterparts (SLNN and DNN) based on previous studies. Our results show that SLNN‐NAS and DNN‐NAS outperformed SLNN and DNN, respectively. These NN predictions ofNedaily variation patterns reveal a 27‐day mid‐latitude topsideNevariation, which cannot be reasonably represented by traditional empirical models developed using monthly averages. DNN‐NAS yields the best prediction accuracy measured by quantitative metrics and rankings of daily pattern prediction, especially with an improvement in mean absolute error more than 10% compared to the SLNN model. The limited improvement of NAS is likely due to the network complexity and the limitation of fully connected NN without the time histories of input parameters. 

   more » « less
5. Final Moments. II. Observational Properties and Physical Modeling of Circumstellar-material-interacting Type II Supernovae

   https://doi.org/10.3847/1538-4357/ad4a2a  

   Jacobson-Galán, W V; Dessart, L; Davis, K W; Kilpatrick, C D; Margutti, R; Foley, R J; Chornock, R; Terreran, G; Hiramatsu, D; Newsome, M; et al (July 2024, The Astrophysical Journal)

   Abstract We present ultraviolet/optical/near-infrared observations and modeling of Type II supernovae (SNe II) whose early time (δt< 2 days) spectra show transient, narrow emission lines from shock ionization of confined (r< 10¹⁵cm) circumstellar material (CSM). The observed electron-scattering broadened line profiles (i.e., IIn-like) of Hi, Hei/ii, Civ, and Niii/iv/vfrom the CSM persist on a characteristic timescale (t_IIn) that marks a transition to a lower-density CSM and the emergence of Doppler-broadened features from the fast-moving SN ejecta. Our sample, the largest to date, consists of 39 SNe with early time IIn-like features in addition to 35 “comparison” SNe with no evidence of early time IIn-like features, all with ultraviolet observations. The total sample includes 50 unpublished objects with a total of 474 previously unpublished spectra and 50 multiband light curves, collected primarily through the Young Supernova Experiment and Global Supernova Project collaborations. For all sample objects, we find a significant correlation between peak ultraviolet brightness and botht_IInand the rise time, as well as evidence for enhanced peak luminosities in SNe II with IIn-like features. We quantify mass-loss rates and CSM density for the sample through the matching of peak multiband absolute magnitudes, rise times,t_IIn, and optical SN spectra with a grid of radiation hydrodynamics and non-local thermodynamic equilibrium radiative-transfer simulations. For our grid of models, all with the same underlying explosion, there is a trend between the duration of the electron-scattering broadened line profiles and inferred mass-loss rate: $t_{\mathrm{IIn}}\approx 3.8[\dot{M}/$(0.01M_⊙yr⁻¹)] days. 

   more » « less