Abstract: Simulating nature and in particular processes in particle physics require expensive computations and sometimes would take much longer than scientists can afford. Here, we explore ways to a solution for this problem by investigating recent advances in generative modeling and present a study for the generation of events from a physical process with deep generative models. The simulation of physical processes requires not only the production of physical events, but to also ensure that these events occur with the correct frequencies. We investigate the feasibility of learning the event generation and the frequency of occurrence with several generative machine learning models to produce events like Monte Carlo generators. We study three processes: a simple two-body decay, the processes e(+)e(-)-> Z -> l(+)l(-) and pp -> tt<mml:mo><overbar></mml:mover> including the decay of the top quarks and a simulation of the detector response. By buffering density information of encoded Monte Carlo events given the encoder of a Variational Autoencoder we are able to construct a prior for the sampling of new events from the decoder that yields distributions that are in very good agreement with real Monte Carlo events and are generated several orders of magnitude faster. Applications of this work include generic density estimation and sampling, targeted event generation via a principal component analysis of encoded ground truth data, anomaly detection and more efficient importance sampling, e.g., for the phase space integration of matrix elements in quantum field theories. Here, the authors report buffered-density variational autoencoders for the generation of physical events. This method is computationally less expensive over other traditional methods and beyond accelerating the data generation process, it can help to steer the generation and to detect anomalies.

Abstract: The "mu from nu'' supersymmetric standard model (mu nu SSM) cures the μproblem and concurrently reproduces measured neutrino data by using a set of usual right-handed neutrino superfields. Recently, the LHC has revealed the first scalar boson which naturally makes it tempting to test μnu SSM in the light of this new discovery. We show that this new scalar, while decaying to a pair of unstable long-lived neutralinos, can lead to a distinct signal with nonprompt multileptons. With concomitant collider analysis we show that this signal provides an intriguing signature of the model, pronounced with light neutralinos. Evidence of this signal is well envisaged with sophisticated displaced vertex analysis, which deserves experimental attention.

Abstract: MoEDAL is designed to identify new physics in the form of stable or pseudostable highly ionizing particles produced in high-energy Large Hadron Collider (LHC) collisions. Here we update our previous search for magnetic monopoles in Run 2 using the full trapping detector with almost four times more material and almost twice more integrated luminosity. For the first time at the LHC, the data were interpreted in terms of photon-fusion monopole direct production in addition to the Drell-Yan-like mechanism. The MoEDAL trapping detector, consisting of 794 kg of aluminum samples installed in the forward and lateral regions, was exposed to 4.0 fb(-1) of 13 TeV proton-proton collisions at the LHCb interaction point and analyzed by searching for induced persistent currents after passage through a superconducting magnetometer. Magnetic charges equal to or above the Dirac charge are excluded in all samples. Monopole spins 0, 1/2, and 1 are considered and both velocity-independent and-dependent couplings are assumed. This search provides the best current laboratory constraints for monopoles with magnetic charges ranging from two to five times the Dirac charge.

Abstract: Nonstandard on-shell decays of W-+/- and Z bosons are possible within the framework of extended supersymmetric models, i.e., with singlet states and/or new couplings compared to the minimal supersymmetric standard model. These modes are typically encountered in regions of the parameter space with light singlet-like scalars, pseudoscalars, and neutralinos. In this letter we emphasize how these states can lead to novel signals at colliders from Z- or W-+/--boson decays with prompt or displaced multileptons/tau jets/jets/photons in the final states. These new modes would give distinct evidence of new physics even when direct searches remain unsuccessful. We discuss the possibilities of probing these new signals using the existing LHC run-I data set. We also address the same in the context of the LHC run-II, as well as for the future colliders. We exemplify our observations with the “mu from v” supersymmetric standard model, where three generations of right-handed neutrino superfields are used to solve shortcomings of the minimal supersymmetric standard model. We also extend our discussion for other variants of supersymmetric models that can accommodate similar signatures.

Abstract: We present an efficient method of reconstructing the parameters of the constrained MSSM from assumed future LHC data, applied both on their own right and in combination with the cosmological determination of the relic dark matter abundance. Focusing on the ATLAS SU3 benchmark point, we demonstrate that our simple Gaussian approximation can recover the values of its parameters remarkably well. We examine two popular noninformative priors and obtain very similar results, although when we use an informative, naturalness-motivated prior, we find some sizeable differences. We show that a further strong improvement in reconstructing the SU3 parameters can by achieved by applying additional information about the relic abundance at the level of WMAP accuracy, although the expected data from Planck will have only a very limited additional impact. Further external data may be required to break some remaining degeneracies. We argue that the method presented here is applicable to a wide class of low-energy effective supersymmetric models, as it does not require one to deal with purely experimental issues, e.g., detector performance, and has the additional advantages of computational efficiency. Furthermore, our approach allows one to distinguish the effect of the model's internal structure and of the external data on the final parameters constraints.