arXiv subjects

Zhongyi Zhang

Publications and source records attributed to Zhongyi Zhang.

48 records · Page 3Linked to original sources

arXiv2022

Topological classification of intrinsic 3D superconductors using anomalous surface construction

Intrinsic topological superconductors have protected gapless Majorana modes, bound and/or propagating, at the natural boundaries of the sample, without requiring field, defect, or heterostructure. We establish the complete classification/construction of intrinsic topological superconductors jointly protected by point-group and time-reversal symmetries in three dimensions. This is obtained from enumerating distinct ways for stacking $n$th-order irreducible building blocks, minimal anomalous surface states of $n$th-order topological superconductors. Particularly, our method provides a unified description of possible surface anomalies away from high-symmetry points/lines in terms of the homotopy group of the surface mass field.

cond-mat.mes-hall↗

arXiv2022

Mixture-of-theories Training: Can We Find New Physics and Anomalies Better by Mixing Physical Theories?

Model-independent search strategies have been increasingly proposed in recent years because on the one hand there has been no clear signal for new physics and on the other hand there is a lack of a highly probable and parameter-free extension of the standard model. For these reasons, there is no simple search target so far. In this work, we try to take a new direction and ask the question: bearing in mind that we have a large number of new physics theories that go beyond the Standard Model and may contain a grain of truth, can we improve our search strategy for unknown signals by using them "in combination"? In particular, we show that a signal hypothesis based on a large, intermingled set of many different theoretical signal models can be a superior approach to find an unknown BSM signal. Applied to a recent data challenge, we show that "mixture-of-theories training" outperforms strategies that optimize signal regions with a single BSM model as well as most unsupervised strategies. Applications of this work include anomaly detection and the definition of signal regions in the search for signals of new physics.

hep-ph↗

arXiv2022

Machine Learning Optimized Search for the $Z'$ from $U(1)_{L_μ-L_τ}$ at the LHC

Extending the Standard Model (SM) by a $U(1)_{L_μ-L_τ}$ group gives potentially significant new contributions to $g_μ-2$, allows the construction of realistic neutrino mass matrices, incorporates lepton universality violation, and offers an anomaly-free mediator for a Dark Matter (DM) sector. In a recent analysis we showed that published LHC searches are not very sensitive to this model. Here we apply several Machine Learning (ML) algorithms in order to distinguish this model from the SM using simulated LHC data. In particular, we optimize the $3μ$-signal, which has a considerably larger cross section than the $4μ$-signal. Furthermore, since the $2$-muon plus missing $E_T$ final state gets contributions from diagrams involving DM particles, we optimize it as well. We find greatly improved sensitivity, which already for $36$ fb$^{-1}$ of data exceeds the combination of published LHC and non-LHC results. We also emphasize the usefulness of Boosted Decision Trees which, unlike Neural Networks, easily allow to extract additional information from the data which directly connect to the theoretical model through feature importance. The same scheme could be used to analyze other models.

hep-ph↗

arXiv2022

Deep Learning-based Assessment of Hepatic Steatosis on chest CT

Purpose: Automatic methods are required for the early detection of hepatic steatosis to avoid progression to cirrhosis and cancer. Here, we developed a fully automated deep learning pipeline to quantify hepatic steatosis on non-contrast enhanced chest computed tomography (CT) scans. Materials and Methods: We developed and evaluated our pipeline on chest CT images of 1,431 randomly selected National Lung Screening Trial (NLST) participants. A dataset of 451 CT scans with volumetric liver segmentations of expert readers was used for training a deep learning model. For testing, in an independent dataset of 980 CT scans hepatic attenuation was manually measured by an expert reader on three cross-sectional images at different hepatic levels by selecting three circular regions of interest. Additionally, 100 randomly selected cases of the test set were volumetrically segmented by expert readers. Hepatic steatosis on the test set was defined as mean hepatic attenuation of < 40 Hounsfield unit. Spearman correlation was conducted to analyze liver fat quantification accuracy and the Cohen's Kappa coefficient was calculated for hepatic steatosis prediction reliability. Results: Our pipeline demonstrated strong performance and achieved a mean dice score of 0.970 for the volumetric liver segmentation. The spearman correlation of the liver fat quantification was 0.954 (P <0.0001) between the automated and expert reader measurements. The cohen's kappa coefficient was 0.875 for automatic assessment of hepatic steatosis. Conclusion: We developed a fully automatic deep learning-based pipeline for the assessment of hepatic steatosis in chest CT images. With the fast and cheap screening of hepatic steatosis, our pipeline has the potential to help initiate preventive measures to avoid progression to cirrhosis and cancer.

q-bio.QM↗

arXiv2021

Deep learning-based detection of intravenous contrast in computed tomography scans

Purpose: Identifying intravenous (IV) contrast use within CT scans is a key component of data curation for model development and testing. Currently, IV contrast is poorly documented in imaging metadata and necessitates manual correction and annotation by clinician experts, presenting a major barrier to imaging analyses and algorithm deployment. We sought to develop and validate a convolutional neural network (CNN)-based deep learning (DL) platform to identify IV contrast within CT scans. Methods: For model development and evaluation, we used independent datasets of CT scans of head, neck (HN) and lung cancer patients, totaling 133,480 axial 2D scan slices from 1,979 CT scans manually annotated for contrast presence by clinical experts. Five different DL models were adopted and trained in HN training datasets for slice-level contrast detection. Model performances were evaluated on a hold-out set and on an independent validation set from another institution. DL models was then fine-tuned on chest CT data and externally validated on a separate chest CT dataset. Results: Initial DICOM metadata tags for IV contrast were missing or erroneous in 1,496 scans (75.6%). The EfficientNetB4-based model showed the best overall detection performance. For HN scans, AUC was 0.996 in the internal validation set (n = 216) and 1.0 in the external validation set (n = 595). The fine-tuned model on chest CTs yielded an AUC: 1.0 for the internal validation set (n = 53), and AUC: 0.980 for the external validation set (n = 402). Conclusion: The DL model could accurately detect IV contrast in both HN and chest CT scans with near-perfect performance.

eess.IV↗

arXiv2020

Diabolical touching point in the magnetic energy levels of topological nodal-line metals

For three-dimensional metals, Landau levels disperse as a function of the magnetic field and the momentum wavenumber parallel to the field. In this two-dimensional parameter space, it is shown that two conically-dispersing Landau levels can touch at a diabolical point -- a Landau-Dirac point. The conditions giving rise to Landau-Dirac points are shown to be magnetic breakdown (field-induced quantum tunneling) and certain crystallographic spacetime symmetry. Both conditions are realizable in topological nodal-line metals, as we exemplify with CaP$_3$. A Landau-Dirac point reveals itself in anomalous batman-like peaks in the magnetoresistance, as well as in the onset of optical absorption linearly evolving to zero frequency as a function of the field magnitude/orientation.

cond-mat.mes-hall↗

arXiv2020

On the wrapped Fukaya category and the loop space of Lagrangians in a Liouville manifold

We introduce an $A_\infty$ map from the cubical chain complex of the based loop space of Lagrangian submanifolds with Legendrian boundary in a Liouville Manifold $C_{*}(Ω_{L} \mathcal{L}\mathit{ag})$ to wrapped Floer cohomology of Lagrangian submanifold $\mathcal{CW}^{-*}(L,L)$. In the case of a cotangent bundle and a Lagrangian co-fiber, the composition of our map with a previously constructed map from $\mathcal{CW}^*(L,L) \to C_{*}(Ω_q Q) $ shows that this map is split surjective.

math.SG↗

arXiv2019

Constraints on Mediator Coupled to Heavy Quarks from LHC Data

We apply LHC data to constrain a simplified extension of the Standard Model containing a new spin-1 mediator $R$, which does not couple to first generation quarks, and a spinor dark matter particle $χ$. We recast ATLAS and CMS searches for final states containing one or more jet(s) + missing $E_T$, with or without $b$ tags, as well as searches for di-jet resonances with $b$ or $t$ tagging. We find that LHC constraints on the axial vector couplings of the mediator are always stronger than the unitarity bound, which scales like $m_R/m_t$. If $R$ has a sizable invisible branching ratio, the strongest LHC bound on both vector couplings and axial vector coupling comes from a di-jet + missing $E_T$ search with or without double $b$ tag. These bounds are quite strong for $m_R < 1$ TeV, even though we have switched off all couplings to valence quarks. Searches for a di-jet resonance with double $b$ tag lead to comparable bounds with the previous results even if $R \rightarrow χ\bar χ$ decays are allowed; these are the only sensitive LHC searches if the invisible branching ratio of $R$ is very small or zero.

hep-ph↗

arXiv2019

Constraints on $U(1)_{L_μ-L_τ}$ from LHC Data

In this study, we apply LHC data to constrain the extension of the Standard Model by an anomaly-free $U(1)_{L_μ-L_τ}$ gauge group; this model contains a new gauge boson ($Z^\prime$) and a scalar dark matter particle ($ϕ_{\rm DM}$). We recast a large number of LHC analyses from ATLAS and CMS of multi-lepton final states. We find that for $10$ GeV $< m_{Z^\prime} < 60$ GeV the strongest constraint comes from a dedicated $Z^\prime$ search in the $4μ$ final state by the CMS collaboration; for larger $Z^\prime$ masses, searches for final states with three leptons plus missing $E_T$ are more sensitive. Searches for final states with two leptons and missing $E_T$, which are sensitive to $Z^\prime$ decays into dark matter particles, can only probe regions of parameter space that are excluded by searches in the $3$ and $4$ lepton channels. The combination of LHC data excludes values of $Z^\prime$ mass and coupling constant that can explain the deficit in $g_μ-2$ for $4$ GeV $\leq m_{Z^\prime} \leq 500$ GeV. However, for much of this range the LHC bound is weaker than the bound that can be derived from searches for trident events in neutrino-nucleus scattering.

hep-ph↗

arXiv2018

Constraints on a Light Leptophobic Mediator from LEP Data

We apply data taken at the $e^+e^-$ collider LEP in the 1990's at center-of-mass energy up to 209 GeV to constrain Dark Matter models with a light leptophobic spin-1 mediator $R$. We assume that the dark sector particle (DSP) is a spin-1/2 fermion $χ$. This scenario is well studied in the context of LHC searches for mediator mass from 100 GeV to several TeV. Emission of the mediator off a quark or antiquark at LEP gives rise to di-jet plus missing energy and 4-jet signatures, which we use to limit the relevant couplings. We focus on scenarios with $2 m_χ> m_R$, which are poorly constrained by LHC data. We recast published searches by the ALEPH collaboration. For $m_χ< 20$ GeV the best bounds result from an analysis at $\sqrt{s} \simeq M_Z$ of di-jet plus missing energy events. For heavier DSP but $m_R < 70$ GeV meaningful bounds can be derived from a four jet analysis at $\sqrt{s} = 183$ GeV. Unfortunately published searches using four jet final states at $\sqrt{s} \simeq M_Z$ use only a small fraction of the total data sample. Moreover, all published searches for di-jet plus missing energy final states at $\sqrt{s} \geq 130$ GeV have poor efficiency for our model; we therefore design new cuts that combine good background rejection with higher efficiency. Re-analyzing the higher energy data using our new cuts, and an analysis of the complete four jet data sample taken at $\sqrt{s}\simeq M_Z$, can explore new regions of parameter space.

hep-ph↗

arXiv2017

Two-weight codes and second order recurrences

Cyclic codes of dimension $2$ over a finite field are shown to have at most two nonzero weights. This extends a construction of Rao et al (2010) and disproves a conjecture of Schmidt-White (2002). We compute their weight distribution, and give a condition on the roots of their check polynomials for them to be MDS.

cs.IT↗

arXiv2017

Pisano period codes

The cyclic codes with parity check polynomial the reciprocal of the characteristic polynomial of the Fibonacci recurrence over a prime finite field are shown to have either one weight or two weights. When these codes are irreducible cyclic we obtain many counterexamples to the conjectural classification of two-weight irreducible cyclic codes of Schmidt and White (2002). When they are reducible and projective their duals are uniformly packed.

cs.IT↗