Abstract

We develop randomized quantum algorithms to simulate quantum collision models, also known as repeated interaction schemes, which provide a rich framework to model various open-system dynamics. The underlying technique involves composing time evolutions of the total (system, bath, and interaction) Hamiltonian and intermittent tracing out the environment degrees of freedom. This results in a unified framework where any near-term Hamiltonian simulation algorithm can be incorporated to implement an arbitrary number of such collisions on early fault-tolerant quantum computers: we do not assume access to specialized oracles such as block encodings and minimize the number of ancilla qubits needed. In particular, using the correspondence between Lindbladian evolution and completely positive trace-preserving maps arising out of memoryless collisions, we provide an end-to-end quantum algorithm for simulating Lindbladian dynamics. For a system of 𝑛-qubits, we exhaustively compare the circuit depth needed to estimate the expectation value of an observable with respect to the reduced state of the system after time 𝑡 while employing different near-term Hamiltonian simulation techniques, requiring at most 𝑛+2 qubits in all. We compare the cnot gate counts of the various approaches for estimating the transverse field magnetization of a 10-qubit XX-Heisenberg spin chain under amplitude damping. Finally, we also develop a framework to efficiently simulate an arbitrary number of memory-retaining collisions, i.e., where environments interact, leading to non-Markovian dynamics. Overall, our methods can leverage quantum collision models for both Markovian and non-Markovian dynamics on early fault-tolerant quantum computers, shedding light on the advantages and limitations of simulating open systems dynamics using this framework.

Abstract

Following up on our earlier study [J. Bardhan et al., Phys. Rev. D 107, 115001 (2023)], we investigate the LHC prospects of pair-produced vectorlike 𝐵 quarks decaying exotically to a new gauge-singlet (pseudo)scalar field Φ and a 𝑏 quark. After the electroweak symmetry breaking, the Φ decays predominantly to 𝑔⁡𝑔⁡/𝑏⁢𝑏 final states, leading to a fully hadronic 2⁢𝑏 +4⁢𝑗 or 6⁢𝑏 signature. Because of the large Standard Model background and the lack of leptonic handles, it is a difficult channel to probe. To overcome the challenge, we employ a hybrid deep learning model containing a graph neural network followed by a deep neural network. We estimate that such a state-of-the-art deep learning analysis pipeline can lead to a performance comparable to that in the semileptonic mode, taking the discovery (exclusion) reach up to about 𝑀_𝐵=1.8⁢(2.4)  TeV at HL-LHC when 𝐵 decays fully exotically, i.e., BR⁡(𝐵→𝑏⁢Φ)=100%.

Abstract

We present a transformer architecture-based foundation model for tasks at high-energy particle colliders such as the Large Hadron Collider. We train the model to classify jets using a self-supervised strategy inspired by the Joint Embedding Predictive Architecture. We use the JetClass dataset containing 100M jets of various known particles to pre-train the model with a data-centric approach -- the model uses a fraction of the jet constituents as the context to predict the embeddings of the unseen target constituents. Our pre-trained model fares well with other datasets for standard classification benchmark tasks. We test our model on two additional downstream tasks: top tagging and differentiating light-quark jets from gluon jets. We also evaluate our model with task-specific metrics and baselines and compare it with state-of-the-art models in high-energy physics. Project site: this https URL

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

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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