Abstract

The possibility of a vectorlike top partner decaying to a new colorless weak-singlet scalar or pseudoscalar has attracted some attention in the literature. We investigate the production of a weaksinglet charge-2=3 T quark that can decay to a spinless boson (Φ) and a top quark at the LHC. Earlier [A. Bhardwaj et al., Roadmap to explore vectorlike quarks decaying to a new scalar or pseudoscalar, arXiv:2203.13753.], we showed that in a large part of the parameter space, the T → tΦ and the loopinduced Φ → gg decays become the dominant decay modes for these particles. Here, we investigate the discovery prospects of the T quark in this region through the above decays. In particular, we focus on the pp → TT → ðtΦÞðtΦÞ → ðtðggÞÞðtðggÞÞ channel. Separating this signal from the huge Standard Model background is a challenging task, forcing us to employ a multivariate machine-learning technique. We find that the above channel can be a discovery channel of the top partner in the large part of the parameter space where the above decay chain dominates. Our analysis is largely model independent, and hence our results would be useful in a broad class of new physics models.

Abstract

In the left-right symmetric models, a heavy charged gauge boson W0 can decay to a lepton and a righthanded neutrino (RHN). If the neutrino masses are generated through the standard type-I seesaw mechanism, the Yukawa couplings controlling two-body decays of the RHN become very small. As a result, the RHN decays to another lepton and a pair of jets via an off-shell W0 . This is the basis of the Keung-Senjanović (KS) process, which was originally proposed as a probe of lepton number violation at the LHC. However, if a different mechanism like the inverse seesaw generates the neutrino masses, a TeV-scale RHN can have large Yukawa couplings and hence dominantly decay to a lepton and a W boson, leading to a kinematically different process from the KS one. We investigate the prospect of this unexplored process as a probe of the inverse seesaw mechanism in the left-right symmetric models at the High Luminosity LHC (HL-LHC). Our signal arises from the Drell-Yan production of a W0 and leads to two high-pT same-flavour-opposite-sign leptons and a boosted W-like fatjet in the final state. We find that a sequential W0 with mass up to ∼6 TeV along with a TeV-scale RHN can be discovered at the HL-LHC.

Abstract

At the LHC, the gluon-initiated processes are considered to be the primary source of di-Higgs production. However, in the presence of a new resonance, the light-quark initiated processes can also contribute significantly. In this letter, we look at the di-Higgs production mediated by a new singlet scalar. The singlet is produced in both quark-antiquark and gluon fusion processes through loops involving a scalar leptoquark and right-handed neutrinos. With benchmark parameters inspired from the recent resonant di-Higgs searches by the ATLAS collaboration, we examine the prospects of such a resonance in the TeVrange at the High-Luminosity LHC (HL-LHC) in the b¯ bτ +τ − mode with a multivariate analysis. We obtain the 5σ and 2σ contours and find that a significant part of the parameter space is within the reach of the HL-LHC.

Abstract

No Results Found

Abstract

In this work, we develop upper bounds on key rates for device-independent quantum key distribution (DI-QKD) protocols and devices. We study the reduced cc-squashed entanglement and show that it is a convex functional. As a result, we show that the convex hull of the currently known bounds is a tighter upper bound on the device-independent key rates of the standard Clauser-Horne-Shimony-Holt (CHSH)- based protocol. We further provide tighter bounds for DI-QKD key rates achievable by any protocol applied to the CHSH-based device. This bound is based on reduced relative entropy of entanglement optimized over decompositions into local and nonlocal parts. In the dynamical scenario of quantum channels, we obtain upper bounds for device-independent private capacity for the CHSH-based protocols. We show that the device-independent private capacity for the CHSH-based protocols on depolarizing and erasure channels is limited by the secret key capacity of dephasing channels.

Abstract

Continuous-time quantum walks provide a natural framework to tackle the fundamental problem of finding a node among a set of marked nodes in a graph, known as spatial search. Whether spatial search by continuous-time quantum walk provides a quadratic advantage over classical random walks has been an outstanding problem. Thus far, this advantage is obtained only for specific graphs or when a single node of the underlying graph is marked. In this article, we provide a new continuous-time quantum walk search algorithm that completely resolves this: our algorithm can find a marked node in any graph with any number of marked nodes, in a time that is quadratically faster than classical random walks. The overall algorithm is quite simple, requiring time evolution of the quantum walk Hamiltonian followed by a projective measurement. A key component of our algorithm is a purely analog procedure to perform operations on a state of the form $e^{-tH^2}|\psi\rangle$, for a given Hamiltonian H: it only requires evolving H for time scaling as $\sqrt{t}$. This allows us to quadratically fast-forward the dynamics of a continuous-time classical random walk. The applications of our work thus go beyond the realm of quantum walks and can lead to new analog quantum algorithms for preparing ground states of Hamiltonians or solving optimization problems.

Abstract

This article proposes Convolutional Neural Network based Auto Encoder (CNN-AE) to predict location dependent rate and coverage probability of a network from its topology. We train the CNN utilising BS location data of India, Brazil, Germany and the USA and compare its performance with stochastic geometry (SG) based analytical models. In comparison to the best-fitted SG-based model, CNN-AE improves the coverage and rate prediction errors by a margin of as large as 40 and 25 percents respectively. As an application, we propose a low complexity, provably convergent algorithm that, using trained CNN-AE, can compute locations of new BSs that need to be deployed in a network in order to satisfy pre-defined spatially heterogeneous performance goals.

Abstract

The article presents a combined theoretical and experimental study attempting to show how Pd nanoparticles (NPs) loading onto SnO2 substrate improves the acetone gas sensing performance. Pristine nanostructured SnO2 and Pd nanoparticles (Pd NPs) loaded SnO2 substrates have been prepared, characterized, and their acetone sensing performances have been measured. Experimental measurements have shown that Pd NP loading onto SnO2 suppresses the interfering effects of ethanol, water vapors, etc., and enhances the acetone sensor response, reversibility, response/recovery speeds, and signal-to-noise ratio. Various parameters like the adsorption energy, HOMO–LUMO energy gap, charge distribution, polarizability change, electrophilicity index, global hardness, etc., of several model systems, have been computed by using DFT. The computed parameters have been correlated with the conductivity, local reactivity, sensor response and selectivity, response/recovery times, etc., of the systems to understand the molecular-level effects of the Pd NP loading onto the SnO2 on the gas sensing process.

Abstract

Neural models command state-of-the-art per- formance across NLP tasks, including ones involving ‘‘reasoning’’. Models claiming to reason about the evidence presented to them should attend to the correct parts of the input while avoiding spurious patterns therein, be self-consistent in their predictions across in- puts, and be immune to biases derived from their pre-training in a nuanced, context- sensitive fashion. Do the prevalent *BERT- family of models do so? In this paper, we study this question using the problem of rea- soning on tabular data. Tabular inputs are especially well-suited for the study—they ad- mit systematic probes targeting the properties listed above. Our experiments demonstrate that a RoBERTa-based model, representative of the current state-of-the-art, fails at reason- ing on the following counts: it (a) ignores relevant parts of the evidence, (b) is over- sensitive to annotation artifacts, and (c) relies on the knowledge encoded in the pre-trained language model rather than the evidence pre- sented in its tabular inputs. Finally, through inoculation experiments, we show that fine- tuning the model on perturbed data does not help it overcome the above challenges.

Abstract

The paper presents a seismic safety assessment of unreinforced masonry (URM) building using two approaches. The first approach uses the ‘Pier Analysis’ method, based on the concept of equivalent lateral stiffness, where in-plane and out-of-plane actions are considered independently. The second approach is developed with the program SAP2000, where the linear response is evaluated using continuum ‘finite element modelling’ (FEM). Both methods are compared to evaluate the safety of wall piers and the differences in the outcomes under combined gravitational and lateral seismic forces. The analysis results showed that few wall elements are unsafe in in-plane and out-of-plane tension. It is also observed that the pier analysis method is conservative compared to FEM, but can be used as a simplified and quick tool in design offices for safety assessment, with reasonable accuracy. To safeguard the URM wall piers under lateral loads, a retrofitting technique is adopted by providing vertical and horizontal belts called splints and bandages, respectively, using welded wire mesh (WWM) reinforcement. The study using the ‘Pier Analysis’ shows that the lateral load capacity of unsafe URM piers can be enhanced up to 3.67 times and made safe using the applied retrofitting technique. Further, the retrofitting design methodology and recommendations for application procedures to on-site URM buildings are discussed in detail.