arXiv subjects

Siqi Yang

Publications and source records attributed to Siqi Yang.

63 records · Page 4Linked to original sources

arXiv2022

Measurement of the proton structure parameters in the forward-backward charge asymmetry

The forward-backward asymmetry ($A_{FB}$) in the Drell-Yan process $pp/p\bar p \to Z/γ^* \to \ell^+\ell^-$ is sensitive to the proton structure information. Such information has been factorized into well-defined proton structure parameters which can be regarded as experimental observables. In this paper, we extract the structure parameters from the $A_{FB}$ distributions reported by the CMS collaboration in $pp$ collisions at $\sqrt{s} = 8$ TeV, and by the D0 collaboration in $p\bar p$ collisions at $\sqrt{s} = 1.96$ TeV. It is the first time that the unique parton information in the $A_{FB}$ spectrum can be decoupled from the electroweak calculation and measured as standalone observables, which can be used as new data constraints in the global quantum chromodynamics analysis of the parton distribution functions (PDFs). Although the parton information in the $pp$ and $p\bar p$ collisions are different, and the precisions of the measured structure parameters are statistically limited, the results from both the hadron colliders indicate that the down quark contribution might be higher than the theoretical predictions with the current PDFs at the relevant momentum fraction range.

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arXiv2022

Boost Asymmetry of the diboson productions in pp collisions

We propose the boost asymmetry of the diboson productions in pp collisions as a new experimental observable, which can provide unique information on the proton structure. The boost asymmetry rises as the difference in the kinematics of the two bosons, that are coupled to the two different quark and antiquark initial states, respectively, and thus reflects different features of the quark and antiquark parton densities. By comparing the kinematics of the two bosons, such as the boson energy or rapidity, the diboson events with antiquark having higher energy than quark can be distinguished from those with quark having higher energy than antiquark. This would provide unique information in some special parton momentum fraction regions, which cannot be directly proved by current W and Z measurements at the Large Hadron Collider or other deep inelastic scattering experients.

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arXiv2022

Factorization of the forward-backward charge asymmetry and measurements of the weak mixing angle and proton structure at hadron colliders

The forward-backward charge asymmetry (AFB) at hadron colliders is sensitive to both the electroweak (EW) symmetry breaking represented by the effective weak mixing angle, and the proton structure information in the initial state modeled by the parton distribution functions (PDFs). Due to their strong correlation, the precisions of the determination on the weak mixing angle and PDFs using the measured AFB spectrum are limited. In this paper, we define a set of structure parameters which factorize the unique proton information of the relative difference between quarks and antiquarks in the AFB observation. Other than the conventional way of extracting the weak mixing angle fro the convolution of PDF and EW calculations, we propose a new method to simultaneously determine the value of the weak mixing angle and the proton structure terms by fitting to the observed AFB distribution, and point out the necessity of specifying additional observations to further reduce the uncertainties on the proton structure terms respectively, so that the model-independent high precision measurements can be achieved at the future LHC experiments.

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arXiv2021

Reduction of the electroweak correlation in the PDF updating by using the forward-backward asymmetry of Drell-Yan process

We propose a new observable for the measurement of the forward-backward asymmetry $(A_{FB})$ in Drell-Yan lepton production. At hadron colliders, the $A_{FB}$ distribution is sensitive to both the electroweak (EW) fundamental parameter $\sin^2 θ_{W}$, the weak mixing angle, and the parton distribution functions (PDFs). Hence, the determination of $\sin^2 θ_{W}$ and the updating of PDFs by directly using the same $A_{FB}$ spectrum are strongly correlated. This correlation would introduce large bias or uncertainty into both precise measurements of EW and PDF sectors. In this article, we show that the sensitivity of $A_{FB}$ on $\sin^2 θ_{W}$ is dominated by its average value around the $Z$ pole region, while the shape (or gradient) of the $A_{FB}$ spectrum is insensitive to $\sin^2 θ_{W}$ and contains important information on the PDF modeling. Accordingly, a new observable related to the gradient of the spectrum is introduced, and demonstrated to be able to significantly reduce the potential bias on the determination of $\sin^2 θ_{W}$ when updating the PDFs using the same $A_{FB}$ data.

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arXiv2020

Reduction of PDF uncertainty in the measurement of the weak mixing angle at the ATLAS experiment

We investigate the parton distribution function (PDF) uncertainty in the measurement of the effective weak mixing angle $\sin^2θ_{\text{eff}}^{\ell}$ at the CERN Large Hadron Collider (LHC). The PDF-induced uncertainty is large in the proton-proton collisions at the LHC due to the dilution effect. The measurement of the Drell-Yan forward-backward asymmetry ($A_{FB}$) at the LHC can be used to reduce the PDF uncertainty in the $\sin^2θ_{\text{eff}}^{\ell}$ measurement. However, when including the full mass range of lepton pairs in the $A_{FB}$ data analysis, the correlation between the PDF updating procedure and the $\sin^2θ_{\text{eff}}^{\ell}$ extraction leads to a sizable bias in the obtained $\sin^2θ_{\text{eff}}^{\ell}$ value. From our studies, we find that the bias can be significantly reduced by removing Drell-Yan events with invariant mass around the $Z$ pole region, while most of the sensitivity in reducing the PDF uncertainty remains. Furthermore, the lepton charge asymmetry in the $W$ boson events as a function of the rapidity of the charged leptons, $A_\pm(η_\ell)$, is known to be another observable which can be used to reduce the PDF uncertainty in the $\sin^2θ_{\text{eff}}^{\ell}$ measurement. The constraint from $A_\pm(η_\ell)$ is complementary to that from the $A_{FB}$, thus no bias affects the $\sin^2θ_{\text{eff}}^{\ell}$ extraction. The studies are performed using the Error PDF Updating Method Package ({\sc ePump}), which is based on the Hessian updating methods. In this article, the CT14HERA2 PDF set is used as an example.

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arXiv2020

Unsupervised Domain Adaptive Object Detection using Forward-Backward Cyclic Adaptation

We present a novel approach to perform the unsupervised domain adaptation for object detection through forward-backward cyclic (FBC) training. Recent adversarial training based domain adaptation methods have shown their effectiveness on minimizing domain discrepancy via marginal feature distributions alignment. However, aligning the marginal feature distributions does not guarantee the alignment of class conditional distributions. This limitation is more evident when adapting object detectors as the domain discrepancy is larger compared to the image classification task, e.g. various number of objects exist in one image and the majority of content in an image is the background. This motivates us to learn domain invariance for category level semantics via gradient alignment. Intuitively, if the gradients of two domains point in similar directions, then the learning of one domain can improve that of another domain. To achieve gradient alignment, we propose Forward-Backward Cyclic Adaptation, which iteratively computes adaptation from source to target via backward hopping and from target to source via forward passing. In addition, we align low-level features for adapting holistic color/texture via adversarial training. However, the detector performs well on both domains is not ideal for target domain. As such, in each cycle, domain diversity is enforced by maximum entropy regularization on the source domain to penalize confident source-specific learning and minimum entropy regularization on target domain to intrigue target-specific learning. Theoretical analysis of the training process is provided, and extensive experiments on challenging cross-domain object detection datasets have shown the superiority of our approach over the state-of-the-art.

cs.CV↗

arXiv2018

Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks

This work shows that it is possible to fool/attack recent state-of-the-art face detectors which are based on the single-stage networks. Successfully attacking face detectors could be a serious malware vulnerability when deploying a smart surveillance system utilizing face detectors. We show that existing adversarial perturbation methods are not effective to perform such an attack, especially when there are multiple faces in the input image. This is because the adversarial perturbation specifically generated for one face may disrupt the adversarial perturbation for another face. In this paper, we call this problem the Instance Perturbation Interference (IPI) problem. This IPI problem is addressed by studying the relationship between the deep neural network receptive field and the adversarial perturbation. As such, we propose the Localized Instance Perturbation (LIP) that uses adversarial perturbation constrained to the Effective Receptive Field (ERF) of a target to perform the attack. Experiment results show the LIP method massively outperforms existing adversarial perturbation generation methods -- often by a factor of 2 to 10.

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arXiv2018

A novel method for lepton energy calibration at Hadron Collider Experiments

This report is to provide a novel method for the lepton energy calibration at Hadron Collider Experiments. The method improves the classic lepton energy calibration procedure widely used at hadron collider experiments. The classic method parameterizes the potential bias in the lepton en- ergy calibration, and determines the value of the parameter by the invariant mass of $Z/γ^*\rightarrow \ell^+\ell^-$ events. The precision of the calibration is dominated by the number of parameters or terms consid- ered in the parameterization, for example, a polynomial extension. With one physics constraint of the reconstructed Z boson mass, the classic procedure can use and determine one parameter. The novel method improves the precision of lepton calibration by introducing more terms in the parameterization. To precisely determine the values of multiple parameters, the method first ac- quires more constraints by separating the $Z/γ^*\rightarrow \ell^+\ell^-$ samples according to the decay kinematics, and then reduces the correlation between multiple parameters. Since the new method is still using the reconstructed Z boson masses as the only constraints, it is much faster and easier than detailed study of detector simulations.

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arXiv2017

TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition

This work tackles the face recognition task on images captured using thermal camera sensors which can operate in the non-light environment. While it can greatly increase the scope and benefits of the current security surveillance systems, performing such a task using thermal images is a challenging problem compared to face recognition task in the Visible Light Domain (VLD). This is partly due to the much smaller amount number of thermal imagery data collected compared to the VLD data. Unfortunately, direct application of the existing very strong face recognition models trained using VLD data into the thermal imagery data will not produce a satisfactory performance. This is due to the existence of the domain gap between the thermal and VLD images. To this end, we propose a Thermal-to-Visible Generative Adversarial Network (TV-GAN) that is able to transform thermal face images into their corresponding VLD images whilst maintaining identity information which is sufficient enough for the existing VLD face recognition models to perform recognition. Some examples are presented in Figure 1. Unlike the previous methods, our proposed TV-GAN uses an explicit closed-set face recognition loss to regularize the discriminator network training. This information will then be conveyed into the generator network in the forms of gradient loss. In the experiment, we show that by using this additional explicit regularization for the discriminator network, the TV-GAN is able to preserve more identity information when translating a thermal image of a person which is not seen before by the TV-GAN.

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