Abstract

Machine learning methods have seen a meteoric rise in their applications in the scientific community. However, little effort has been put into understanding these “black box” models. We show how one can apply integrated gradients (IGs) to understand these models by designing different baselines, by taking an example case study in particle physics. We find that the zero-vector baseline does not provide good feature attributions and that an averaged baseline sampled from the background events provides consistently more reasonable attributions.

Abstract

Leptoquarks (LQs) can contribute to the magnetic and electric dipole moments of charged leptons, which the current experiments have measured with good accuracy. We revisit the parameter spaces of TeV-scale vector LQs that contribute to these observables and study how these models fare against the LHC bounds. We show that significant portions of the parameter space are excluded when the current LHC data is utilized effectively. We find that only U1and V2 can explain the observed positive shift in (aμexp-aμSM) through a lepton chirality-flipping contribution with O(1) LQ-quark-lepton coupling. We also see how these two LQs can fit the electron dipole moment and atomic parity violation measurements. We find that the current electric dipole moment measurements of the muon cannot restrain the LQ-quark-lepton couplings within perturbative regions.

Abstract

We review the main applications of machine learning models that are not fully supervised in particle physics, i.e., clustering, anomaly detection, detector simulation, and unfolding. Unsupervised methods are ideal for anomaly detection tasks—machine learning models can be trained on background data to identify deviations if we model the background data precisely. The learning can also be partially unsupervised when we can provide some information about the anomalies at the data level. Generative models are useful in speeding up detector simulations—they can mimic the computationally intensive task without large resources. They can also efficiently map detector-level data to parton-level data (i.e., data unfolding). In this review, we focus on interesting ideas and connections and briefly overview the underlying techniques wherever necessary.

Abstract

We review the main applications of machine learning models that are not fully supervised in particle physics, i.e., clustering, anomaly detection, detector simulation, and unfolding. Unsupervised methods are ideal for anomaly detection tasks—machine learning models can be trained on background data to identify deviations if we model the background data precisely. The learning can also be partially unsupervised when we can provide some information about the anomalies at the data level. Generative models are useful in speeding up detector simulations—they can mimic the computationally intensive task without large resources. They can also efficiently map detector-level data to parton-level data (i.e., data unfolding). In this review, we focus on interesting ideas and connections and briefly overview the underlying techniques wherever necessary.

Abstract

The scalar-leptoquark (sLQ) parameter space is well explored experimentally. The direct pair production searches at the LHC have excluded light sLQs almost model agnostically, and the high-pT dilepton tail data have put strong bounds on the leptoquark-quark-lepton Yukawa couplings for a wide range of sLQ masses. However, these do not show the complete picture. Previously, Mandal et al. [Single productions of colored particles at the LHC: An example with scalar leptoquarks, J. High Energy Phys. 07 (2015) 028] showed how the dilepton-dijet data from the pair production searches could give strong limits on these couplings. This was possible by including the single-production contribution to the dilepton-dijet signal. In this paper, we take a fresh look at the LHC limits on all sLQs by following the same principle and combine all significant contributions—from pair and single productions, t-channel sLQ exchange and its interference with the Standard Model background—to the μμjj final state and recast the limits. We notice that the sLQ exchange and its interference with the background processes play significant roles in the limits. The μμjj- recast limits are comparable to or, in some cases, significantly better than the currently known limits (from high-pT dilepton data and direct searches), i.e., the LHC data rules out more parameter space than what is considered in the current literature. For the first time, we also show how including the QED processes can noticeably improve the sLQ mass exclusion limits from the QCD-only limits.

Abstract

No Results Found

Abstract

A leptophobic $Z^prime$ that does not couple with the Standard Model leptons can evade the stringent bounds from the dilepton-resonance searches. In our earlier paper [T. Arun et al., Search for the $Z'$ boson decaying to a right-handed neutrino pair in leptophobic $U(1)$ models,Phys. Rev. D, 106 (2022) 095035; arXiv:2204.02949], we presented two gauge anomaly-free $U(1)$ models---one based on the Green-Schwarz (GS) anomaly cancellation mechanism, and the other on a grand unified theory (GUT) framework with gauge kinetic mixing---where a heavy leptophobic $Z'$ is present along with right-handed neutrinos ($N_R$). We pointed out the interesting possibility of a correlated search for $Z'$ and $N_R$ at the LHC through the $ppto Z'to N_R N_R$ channel. This channel can probe a part of the $Z'$ parameter space beyond the reach of the standard dijet resonance searches. In this follow-up paper, we analyse the challenging monolepton final state arising from the decays of the $N_R$ pair with Deep Learning. We present the high-luminosity LHC discovery reaches for six different GUT embeddings and a benchmark point in the GS setup. We also update our previous estimates in the dilepton channel with Deep Learning. We identify parameter regions that can be probed with the proposed channel but will remain inaccessible to dijet searches at the HL-LHC

Abstract

We investigate the dynamical aspects of the quantum switch and find a particular form of quantum memory emerging out of the switch action. We first analyse the loss of information in a general quantum evolution subjected to a quantum switch and propose a measure to quantify the switch-induced memory. We then derive an uncertainty relation between information loss and switch-induced memory. We explicitly consider the example of depolarising dynamics and show how it is affected by the action of a quantum switch. For a more detailed analysis, we consider both the control qubit and the final measurement on the control qubit as noisy and investigate the said uncertainty relation. Further, while deriving the Lindblad-type dynamics for the reduced operation of the switch action, we identify that the switch-induced memory actually leads to the emergence of non-Markovianity. Interestingly, we demonstrate that the emergent non-Markovianity can be explicitly attributed to the switch operation by comparing it with other standard measures of non-Markovianity. Our investigation thus paves the way forward to understanding the quantum switch as an emerging non-Markovian quantum memory.

Abstract

The presence of a new decay mode relaxes the current mass exclusion limits on vectorlike quarks considerably. We consider the case of a weak-singlet vectorlike B quark that can decay to a singlet scalar or pseudoscalar Φ. In an earlier paper [A. Bhardwaj et al., Roadmap to explore vectorlike quarks decaying to a new scalar or pseudoscalar, Phys. Rev. D, 106 (2022) 095014; arXiv:2203.13753], we mapped the possibilities to explore such setups at the LHC. We showed that it is possible for a B quark to decay into Φ and the Φ to dominantly decay to a pair of gluons or b quark(s) without fine-tuning the parameters. In this paper, we present a collider search strategy to look for the pair production of singlet B quarks. If both B’s decay into bΦ pairs, the final state is fully hadronic: BB → (bΦ)(bΦ) → (bgg)(bgg)/(bbb)(bbb), which is very challenging to probe. Therefore, we consider a simpler mixed decay specific to the singlet B case, BB → (bΦ)(tW) with a lepton in the final state, to achieve the best sensitivity at the highluminosity LHC. We use a deep neural network with a weighted cross-entropy loss to separate the signal from the huge SM background. Our analysis shows that large areas of the MB − MΦ parameter space are discoverable through this signature. We show how the discovery and exclusion regions scale with the branching ratio in the new decay mode. We also estimate the reach in the inclusive monolepton channel with the same network model.

Abstract

The leading order production of an SM singlet-like scalar has primarily been realized through the gluon fusion process by mixing with the SU(2)_L scalar doublet of the model. The dominant part of the physical state, i.e., the singlet component, does not have any role in its direct production. Focusing on such a state with a mass smaller than the SM-like Higgs scalar, we calculate the dominant next-to-leading (NLO) order corrections to its production cross-section. With these improved cross-sections, the present and future LHC limits may become somewhat more stringent