arXiv subjects

Ying Yuan

Publications and source records attributed to Ying Yuan.

At least 91 records · Page 5Linked to original sources

arXiv2021

A search for solar axions and anomalous neutrino magnetic moment with the complete PandaX-II data

We report a search for new physics signals using the low energy electron recoil events in the complete data set from PandaX-II, in light of the recent event excess reported by XENON1T. The data correspond to a total exposure of 100.7 ton-day with liquid xenon. With robust estimates of the dominant background spectra, we perform sensitive searches on solar axions and neutrinos with enhanced magnetic moment. We find that the axion-electron coupling $g_{Ae}<4.6\times 10^{-12}$ for an axion mass less than $\rm 0.1~keV/c^2$ and the neutrino magnetic moment $μ_ν<4.9\times 10^{-11}μ_{B}$ at 90% confidence level. The observed excess from XENON1T is within our experimental constraints.

hep-ex↗

arXiv2021

Constraining self-interacting dark matter with the full dataset of PandaX-II

Self-interacting Dark Matter (SIDM) is a leading candidate proposed to solve discrepancies between predictions of the prevailing cold dark matter theory and observations of galaxies. Many SIDM models predict the existence of a light force carrier that mediate strong dark matter self-interactions. If the mediator couples to the standard model particles, it could produce characteristic signals in dark matter direct detection experiments. We report searches for SIDM models with a light mediator using the full dataset of the PandaX-II experiment, based on a total exposure of 132 tonne-days. No significant excess over background is found, and our likelihood analysis leads to a strong upper limit on the dark matter-nucleon coupling strength. We further combine the PandaX-II constraints and those from observations of the light element abundances in the early universe, and show that direct detection and cosmological probes can provide complementary constraints on dark matter models with a light mediator.

hep-ex↗

arXiv2021

Horizontal Position Reconstruction in PandaX-II

Dual-phase noble-gas time projection chambers (TPCs) have improved the sensitivities for dark matter direct search in past decades. The capability of TPCs to reconstruct 3-D vertexes of keV scale recoilings is one of the most advantageous features. In this work, we develop two horizontal position reconstruction algorithms for the PandaX-II dark matter search experiment using the dual-phase liquid xenon TPC. Both algorithms are optimized by the $^{83m}$Kr calibration events and use photon distribution of ionization signals among photomultiplier tubes to infer the positions. According to the events coming from the gate electrode, the uncertainties in the horizontal positions are 3.4 mm (3.9 mm) in the analytical (simulation-based) algorithm for an ionization signal with several thousand photon electrons in the center of the TPC

physics.ins-det↗

arXiv2021

Search for Light Dark Matter-Electron Scatterings in the PandaX-II Experiment

We report constraints on light dark matter through its interactions with shell electrons in the PandaX-II liquid xenon detector with a total 46.9 tonne$\cdot$day exposure. To effectively search for these very low energy electron recoils, ionization-only signals are selected from the data. 1821 candidates are identified within ionization signal range between 50 to 75 photoelectrons, corresponding to a mean electronic recoil energy from 0.08 to 0.15 keV. The 90% C.L. exclusion limit on the scattering cross section between the dark matter and electron is calculated based on Poisson statistics. Under the assumption of point interaction, we provide the world's most stringent limit within the dark matter mass range from 15 to 30 $\rm MeV/c^2$, with the corresponding cross section from $2.5\times10^{-37}$ to $3.1\times10^{-38}$ cm$^2$.

hep-ex↗

arXiv2021

Study of production of (anti)deuteron observed in Au+Au collisions at $\sqrt{S_{NN}}$=14.5, 62.4 and 200 GeV

Transverse momentum distributions of deuterons and anti-deuterons in Au+Au collisions at $\sqrt{S_{NN}}$=14.5, 62.4 and 200 GeV with different centrality are studied in the framework of the multisource thermal model. Transverse momentum spectra are conformably and approximately described by the Tsallis distribution. The dependence of parameters (average transverse momenta, effective temperature and entropy index) on event centrality are obtained. It is found that the parameters increase with increase of the average number of particles involved in collisions, which reveals the nuclear stopping degree increases with collision centrality.

hep-ph↗

arXiv2021

Determination of responses of liquid xenon to low energy electron and nuclear recoils using the PandaX-II detector

We report a systematic determination of the responses of PandaX-II, a dual phase xenon time projection chamber detector, to low energy recoils. The electron recoil (ER) and nuclear recoil (NR) responses are calibrated, respectively, with injected tritiated methane or $^{220}$Rn source, and with $^{241}$Am-Be neutron source, within an energy range from $1-25$ keV (ER) and $4-80$ keV (NR), under the two drift fields of 400 and 317 V/cm. An empirical model is used to fit the light yield and charge yield for both types of recoils. The best fit models can well describe the calibration data. The systematic uncertainties of the fitted models are obtained via statistical comparison against the data.

physics.ins-det↗

arXiv2021

Internal Calibration of the PandaX-II Detector with Radon Gaseous Sources

We have developed a low-energy electron recoil (ER) calibration method with $^{220}$Rn for the PandaX-II detector. $^{220}$Rn, emanated from natural thorium compounds, was fed into the detector through the xenon purification system. From 2017 to 2019, we performed three dedicated calibration campaigns with different radon sources. We studied the detector response to $α$, $β$, and $γ$ particles with focus on low energy ER events. During the runs in 2017 and 2018, the amount of radioactivity of $^{222}$Rn were on the order of 1\% of that of $^{220}$Rn and thorium particulate contamination was negligible, especially in 2018. We also measured the background contribution from $^{214}$Pb for the first time in PandaX-II with the help from a $^{222}$Rn injection. Calibration strategy with $^{220}$Rn and $^{222}$Rn will be implemented in the upcoming PandaX-4T experiment and can be useful for other xenon-based detectors as well.

physics.ins-det↗

arXiv2021

Results of Dark Matter Search using the Full PandaX-II Exposure

We report the dark matter search results obtained using the full 132 ton$\cdot$day exposure of the PandaX-II experiment, including all data from March 2016 to August 2018. No significant excess of events is identified above the expected background. Upper limits are set on the spin-independent dark matter-nucleon interactions. The lowest 90% confidence level exclusion on the spin-independent cross section is $2.2\times 10^{-46}$ cm$^2$ at a WIMP mass of 30 GeV/$c^2$.

astro-ph.CO↗

arXiv2020

Elastic Priors to Dynamically Borrow Information from Historical Data in Clinical Trials

Use of historical data and real-world evidence holds great potential to improve the efficiency of clinical trials. One major challenge is how to effectively borrow information from historical data while maintaining a reasonable type I error. We propose the elastic prior approach to address this challenge and achieve dynamic information borrowing. Unlike existing approaches, this method proactively controls the behavior of dynamic information borrowing and type I errors by incorporating a well-known concept of clinically meaningful difference through an elastic function, defined as a monotonic function of a congruence measure between historical data and trial data. The elastic function is constructed to satisfy a set of information-borrowing constraints prespecified by researchers or regulatory agencies, such that the prior will borrow information when historical and trial data are congruent, but refrain from information borrowing when historical and trial data are incongruent. In doing so, the elastic prior improves power and reduces the risk of data dredging and bias. The elastic prior is information borrowing consistent, i.e. asymptotically controls type I and II errors at the nominal values when historical data and trial data are not congruent, a unique characteristics of the elastic prior approach. Our simulation study that evaluates the finite sample characteristic confirms that, compared to existing methods, the elastic prior has better type I error control and yields competitive or higher power.

stat.ME↗

arXiv2020

Optimal Bayesian hierarchical model to accelerate the development of tissue-agnostic drugs and basket trials

Tissue-agnostic trials enroll patients based on their genetic biomarkers, not tumor type, in an attempt to determine if a new drug can successfully treat disease conditions based on biomarkers. The Bayesian hierarchical model (BHM) provides an attractive approach to design phase II tissue-agnostic trials by allowing information borrowing across multiple disease types. In this article, we elucidate two intrinsic and inevitable issues that may limit the use of BHM to tissue-agnostic trials: sensitivity to the prior specification of the shrinkage parameter and the competing "interest" among disease types in increasing power and controlling type I error. To address these issues, we propose the optimal BHM (OBHM) approach. With OBHM, we first specify a flexible utility function to quantify the tradeoff between type I error and power across disease type based on the study objectives, and then we select the prior of the shrinkage parameter to optimize the utility function of clinical and regulatory interest. OBMH effectively balances type I and II errors, addresses the sensitivity of the prior selection, and reduces the "unwarranted" subjectivity in the prior selection. Simulation study shows that the resulting OBHM and its extensions, clustered OBHM (COBHM) and adaptive OBHM (AOBHM), have desirable operating characteristics, outperforming some existing methods with better balanced power and type I error control. Our method provides a systematic, rigorous way to apply BHM and solve the common problem of blindingly using a non-informative inverse-gamma prior (with a large variance) or priors arbitrarily chosen that may lead to pathological statistical properties.

stat.ME↗

arXiv2020

Social Network Mediation Analysis: a Latent Space Approach

Social networks contain data on both actor attributes and social connections among them. Such connections reflect the dependence among social actors, which is important for individual's mental health and social development. To investigate the potential mediation role of a social network, we propose a mediation model with a social network as a mediator. In the model, dependence among actors is accounted by a few mutually orthogonal latent dimensions. The scores on these dimensions are directly involved in the intervention process between an independent variable and a dependent variable. Because all the latent dimensions are equivalent in terms of their relationship to social networks, it is hardly to name them. The intervening effect through an individual dimension is thus of little practical interest. Therefore, we would rather focus on the mediation effect of a network. Although the scores are not unique, we rigorously articulate that the proposed network mediation effect is still well-defined. To estimate the model, we adopt a Bayesian estimation method. This modeling framework and the Bayesian estimation method is evaluated through a simulation study under representative conditions. Its usefulness is demonstrated through an empirical application to a college friendship network.

stat.ME↗

arXiv2020

A novel sentence embedding based topic detection method for micro-blog

Topic detection is a challenging task, especially without knowing the exact number of topics. In this paper, we present a novel approach based on neural network to detect topics in the micro-blogging dataset. We use an unsupervised neural sentence embedding model to map the blogs to an embedding space. Our model is a weighted power mean word embedding model, and the weights are calculated by attention mechanism. Experimental result shows our embedding method performs better than baselines in sentence clustering. In addition, we propose an improved clustering algorithm referred as relationship-aware DBSCAN (RADBSCAN). It can discover topics from a micro-blogging dataset, and the topic number depends on dataset character itself. Moreover, in order to solve the problem of parameters sensitive, we take blog forwarding relationship as a bridge of two independent clusters. Finally, we validate our approach on a dataset from sina micro-blog. The result shows that we can detect all the topics successfully and extract keywords in each topic.

cs.IR↗

arXiv2020

Incorporating historical information to improve phase I clinical trial designs

Incorporating historical data or real-world evidence has a great potential to improve the efficiency of phase I clinical trials and to accelerate drug development. For model-based designs, such as the continuous reassessment method (CRM), this can be conveniently carried out by specifying a "skeleton," i.e., the prior estimate of dose limiting toxicity (DLT) probability at each dose. In contrast, little work has been done to incorporate historical data or real-world evidence into model-assisted designs, such as the Bayesian optimal interval (BOIN), keyboard, and modified toxicity probability interval (mTPI) designs. This has led to the misconception that model-assisted designs cannot incorporate prior information. In this paper, we propose a unified framework that allows for incorporating historical data or real-world evidence into model-assisted designs. The proposed approach uses the well-established "skeleton" approach, combined with the concept of prior effective sample size, thus it is easy to understand and use. More importantly, our approach maintains the hallmark of model-assisted designs: simplicity---the dose escalation/de-escalation rule can be tabulated prior to the trial conduct. Extensive simulation studies show that the proposed method can effectively incorporate prior information to improve the operating characteristics of model-assisted designs, similarly to model-based designs.

stat.ME↗

arXiv2020

Uncertainty Quantification of First Principles Computational Phase Diagram Predictions of Li-Si System Via Bayesian Sampling

In this work, an assessment of the CALPHAD method trained on only density functional theory (DFT) data is performed for the Li-Si binary system, as a case study. Using a parameter sampling approach based on the Bayesian Error Estimation Functional (BEEF-vdW) exchange-correlation potential. By using built-in ensemble of functionals from BEEF-vdW, the uncertainties of the Gibbs Free Energy fitting parameters are obtained and can be propagated to the resulting phase diagram. To find the best fitting form of the CALPHAD model, we implement a model selection step using the Bayesian information criterion (BIC). Applying the best selected CALPHAD model from the DFT calculation, to other sampled BEEF functionals, an ensemble of CALPHAD models is generated leading to an ensemble of phase diagram predictions. The resulting phase diagrams are then compiled into a single-phase diagram representing the most probable phase predicted as well as a quantitative metric of confidence for the prediction. This treatment of uncertainty resulting from DFT provides a rigorous way to ensure the correlated errors of DFT is accounted for in the estimation of uncertainty. From the phase diagram, we have determined intercalation voltages for lithiated silicon. In combination, we can generate a better understanding of the phase transitions and voltage profile to make a more analysis-informed prediction for experiments and the performance of Si-anodes within batteries.

cond-mat.mtrl-sci↗

arXiv2020

Determining the Superconducting Transition Temperatures of Liquids

We develop a non-perturbative approach for calculating the superconducting transition temperatures ($T_{c}$) of liquids. The electron-electron scattering amplitude induced by electron-phonon coupling (EPC), from which the effective pairing interaction can be inferred, is related to the fluctuation of the $T$-matrix of electron scattering induced by ions. By applying the relation, EPC parameters can be extracted from a path-integral molecular dynamics simulation. For determining $T_{c}$, the linearized Eliashberg equations are re-established in the non-perturbative context. We apply the approach to estimate $T_{c}$ of metallic hydrogen liquids. It indicates that metallic hydrogen liquids in the pressure regime from $0.5$ to $1.5\mathrm{\,TPa}$ have $T_{c}$ well above their melting temperatures, therefore are superconducting liquids.

cond-mat.supr-con↗

arXiv2019

Design and commissioning of a 600 L Time Projection Chamber with Microbulk Micromegas

We report the design, construction, and initial commissioning results of a large high pressure gaseous Time Projection Chamber (TPC) with Micromegas modules for charge readout. The detector vessel has an inner volume of about 600 L and an active volume of 270 L. At 10 bar operating pressure, the active volume contains about 20 kg of xenon gas and can image charged particle tracks. Drift electrons are collected by the charge readout plane, which accommodates a tessellation of seven Micromegas modules. Each of the Micromegas covers a square of 20 cm by 20 cm. A new type of Microbulk Micromegas is chosen for this application due to its good gain uniformity and low radioactive contamination. Initial commissioning results with 1 Micromegas module running with 1 bar argon and isobutane gas mixture and 5 bar xenon and trimethylamine (TMA) gas mixture are reported. We also recorded extended background tracks from cosmic ray events and highlighted the unique tracking feature of this gaseous TPC.

physics.ins-det↗

arXiv2018

TOP: Time-to-Event Bayesian Optimal Phase II Trial Design for Cancer Immunotherapy

Immunotherapies have revolutionized cancer treatment. Unlike chemotherapies, immune agents often take longer time to show benefit, and the complex and unique mechanism of action of these agents renders the use of multiple endpoints more appropriate in some trials. These new features of immunotherapy make conventional phase II trial designs, which assume a single binary endpoint that is quickly ascertainable, inefficient and dysfunctional. We propose a flexible and efficient time-to-event Bayesian optimal phase II (TOP) design. The TOP design is efficient in that it allows real-time "go/no-go" interim decision making in the presence of late-onset responses by using all available data, and maximizes the statistical power for detecting effective treatments. TOP is flexible in the number of interim looks and capable of handling simple and complicated endpoints under a unified framework. We conduct simulation studies to evaluate the operating characteristics of the TOP design.Compared to some existing designs, the TOP design shortens the trial duration and has higher power to detect effective treatment with well controlled type I errors. The TOP design allows for making real-time "go/no-go" interim decisions in the presence of late-onset responses, and is capable of handling various types of endpoints under a unified framework. It is transparent and easy to implement as its decision rules can be tabulated and included in the protocol prior to the conduct of the trial. The TOP design provides a flexible, efficient and easy-to-implement method to accelerate and improve the development of immunotherapies.

stat.ME↗

arXiv2018

Time-to-Event Model-Assisted Designs to Accelerate Phase I Clinical Trials

Two useful strategies to speed up drug development are to increase the patient accrual rate and use novel adaptive designs. Unfortunately, these two strategies often conflict when the evaluation of the outcome cannot keep pace with the patient accrual rate and thus the interim data cannot be observed in time to make adaptive decisions. A similar logistic difficulty arises when the outcome is of late onset. Based on a novel formulation and approximation of the likelihood of the observed data, we propose a general methodology for model-assisted designs to handle toxicity data that are pending due to fast accrual or late-onset toxicity, and facilitate seamless decision making in phase I dose-finding trials. The dose escalation/de-escalation rules of the proposed time-to-event model-assisted designs can be tabulated before the trial begins, which greatly simplifies trial conduct in practice compared to that under existing methods. We show that the proposed designs have desirable finite and large-sample properties and yield performance that is superior to that of more complicated model-based designs. We provide user-friendly software for implementing the designs.

stat.ME↗