Abstract: A measurement of event-shape variables in proton-proton collisions at large momentum transfer is presented using data collected at root s = 13 TeV with the ATLAS detector at the Large Hadron Collider. Six event-shape variables calculated using hadronic jets are studied in inclusive multijet events using data corresponding to an integrated luminosity of 139 fb(-1). Measurements are performed in bins of jet multiplicity and in different ranges of the scalar sum of the transverse momenta of the two leading jets, reaching scales beyond 2 TeV. These measurements are compared with predictions from Monte Carlo event generators containing leading-order or next-to-leading order matrix elements matched to parton showers simulated to leading-logarithm accuracy. At low jet multiplicities, shape discrepancies between the measurements and the Monte Carlo predictions are observed. At high jet multiplicities, the shapes are better described but discrepancies in the normalisation are observed.

Abstract: Monte Carlo simulations are a unique tool to check the response of a detector and to monitor its performance. For a deep-sea neutrino telescope, the variability of the environmental conditions that can affect the behaviour of the data acquisition system must be considered, in addition to a reliable description of the active parts of the detector and of the features of physics events, in order to produce a realistic set of simulated events. In this paper, the software tools used to produce neutrino and cosmic ray signatures in the telescope and the strategy developed to represent the time evolution of the natural environment and of the detector efficiency are described.

Abstract: The KM3NeT infrastructure consists of two deep-sea neutrino telescopes being deployed in the Mediterranean Sea. The telescopes will detect extraterrestrial and atmospheric neutrinos by means of the incident photons induced by the passage of relativistic charged particles through the seawater as a consequence of a neutrino interaction. The telescopes are configured in a three-dimensional grid of digital optical modules, each hosting 31 photomultipliers. The photomultiplier signals produced by the incident Cherenkov photons are converted into digital information consisting of the integrated pulse duration and the time at which it surpasses a chosen threshold. The digitization is done by means of time to digital converters (TDCs) embedded in the field programmable gate array of the central logic board. Subsequently, a state machine formats the acquired data for its transmission to shore. We present the architecture and performance of the front-end firmware consisting of the TDCs and the state machine.

Abstract: Microquasars with high-mass companion stars are promising very high energy (VHE; 0.1-100 TeV) gamma-ray emitters, but their behaviors above 10 TeV are poorly known. Using the High Altitude Water Cerenkov (HAWC) observatory, we search for excess gamma-ray emission coincident with the positions of known high-mass microquasars (HMMQs). No significant emission is observed for LS 5039, Cyg X-1, Cyg X-3, and SS 433 with 1523 days of HAWC data. We set the most stringent limit above 10 TeV obtained to date on each individual source. Under the assumption that HMMQs produce gamma rays via a common mechanism, we have performed source-stacking searches, considering two different scenarios: (I) gamma-ray luminosity is a fraction epsilon ( gamma ) of the microquasar jet luminosity, and (II) VHE gamma rays are produced by relativistic electrons upscattering the radiation field of the companion star in a magnetic field B. We obtain epsilon ( gamma ) < 5.4 x 10(-6) for scenario I, which tightly constrains models that suggest observable high-energy neutrino emission by HMMQs. In the case of scenario II, the nondetection of VHE gamma rays yields a strong magnetic field, which challenges synchrotron radiation as the dominant mechanism of the microquasar emission between 10 keV and 10 MeV.

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.