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Abstract The origin of switchbacks in the solar wind is discussed in two classes of theory that differ in the location of the source being either near the transition region near the Sun or in the solar wind itself. The two classes of theory differ in their predictions of the switchback rate (the number of switchbacks observed per hour) as a function of distance from the Sun. To distinguish between these theories, one-hour averages of Parker Solar Probe data were averaged over five orbits to find the following: (1) The hourly averaged switchback rate was independent of distance from the Sun. (2) The average switchback rate increased with solar wind speed. (3) The switchback size perpendicular to the flow increased as R , the distance from the Sun, while the radial size increased as R 2 , resulting in an increasing switchback aspect ratio with distance from the Sun. (4) The hourly averaged and maximum switchback rotation angles did not depend on the solar wind speed or distance from the Sun. These results are consistent with switchback formation in the transition region because their increase of tangential size with radius compensates for the radial falloff of their equatorial density to produce switchback rates that are independent of radial distance. This constant switchback rate is inconsistent with an in situ source. The switchback size and aspect ratio, but not their hourly average or maximum rotation angle, increased with radial distance to 100 solar radii. Additionally, quiet intervals between switchback patches occurred at the lowest solar wind speeds. 

Abstract Nonlinear ion-acoustic waves, ion holes, and electron holes have been observed on the Parker Solar Probe at a heliocentric distance of 35 solar radii. These time domain structures contain millisecond duration electric field spikes of several mV m⁻¹. They are observed inside or at boundaries of switchbacks in the background magnetic field. Their presence in switchbacks indicates that both electron- and ion-streaming electrostatic instabilities occur there to thermalize electron and ion beams. 

Many data sets can be viewed as a noisy sampling of an underlying space, and tools from topological data analysis can characterize this structure for the purpose of knowledge discovery. One such tool is persistent homology, which provides a multiscale description of the homological features within a data set. A useful representation of this homological information is a persistence diagram (PD). Efforts have been made to map PDs into spaces with additional structure valuable to machine learning tasks. We convert a PD to a finite dimensional vector representation which we call a persistence image (PI), and prove the stability of this transformation with respect to small perturbations in the inputs. The discriminatory power of PIs is compared against existing methods, showing significant performance gains. We explore the use of PIs with vector-based machine learning tools, such as linear sparse support vector machines, which identify features containing discriminating topological information. Finally, high accuracy inference of parameter values from the dynamic output of a discrete dynamical system (the linked twist map) and a partial differential equation (the anisotropic Kuramoto-Sivashinsky equation) provide a novel application of the discriminatory power of PIs. 

Abstract The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy. 

Abstract During LHC Run 2 (2015–2018) the ATLAS Level-1 topological trigger allowed efficient data-taking by the ATLAS experiment at luminosities up to 2.1 $$\times $$ × 10 $$^{34}$$ 34  cm $$^{-2}$$ - 2 s $$^{-1}$$ - 1 , which exceeds the design value by a factor of two. The system was installed in 2016 and operated in 2017 and 2018. It uses Field Programmable Gate Array processors to select interesting events by placing kinematic and angular requirements on electromagnetic clusters, jets, $$\tau $$ τ -leptons, muons and the missing transverse energy. It allowed to significantly improve the background event rejection and signal event acceptance, in particular for Higgs and B -physics processes. 

Abstract Several improvements to the ATLAS triggers used to identify jets containing b -hadrons ( b -jets) were implemented for data-taking during Run 2 of the Large Hadron Collider from 2016 to 2018. These changes include reconfiguring the b -jet trigger software to improve primary-vertex finding and allow more stable running in conditions with high pile-up, and the implementation of the functionality needed to run sophisticated taggers used by the offline reconstruction in an online environment. These improvements yielded an order of magnitude better light-flavour jet rejection for the same b -jet identification efficiency compared to the performance in Run 1 (2011–2012). The efficiency to identify b -jets in the trigger, and the conditional efficiency for b -jets that satisfy offline b -tagging requirements to pass the trigger are also measured. Correction factors are derived to calibrate the b -tagging efficiency in simulation to match that observed in data. The associated systematic uncertainties are substantially smaller than in previous measurements. In addition, b -jet triggers were operated for the first time during heavy-ion data-taking, using dedicated triggers that were developed to identify semileptonic b -hadron decays by selecting events with geometrically overlapping muons and jets. 

A bstract A search for dark-matter particles in events with large missing transverse momentum and a Higgs boson candidate decaying into two photons is reported. The search uses 139 fb − 1 of proton-proton collision data collected at $$ \sqrt{s} $$ s = 13 TeV with the ATLAS detector at the CERN LHC between 2015 and 2018. No significant excess of events over the Standard Model predictions is observed. The results are interpreted by extracting limits on three simplified models that include either vector or pseudoscalar mediators and predict a final state with a pair of dark-matter candidates and a Higgs boson decaying into two photons. 

Abstract This paper presents a search for dark matter in the context of a two-Higgs-doublet model together with an additional pseudoscalar mediator, a , which decays into the dark-matter particles. Processes where the pseudoscalar mediator is produced in association with a single top quark in the 2HDM+ a model are explored for the first time at the LHC. Several final states which include either one or two charged leptons (electrons or muons) and a significant amount of missing transverse momentum are considered. The analysis is based on proton–proton collision data collected with the ATLAS experiment at $$\sqrt{s} = 13$$ s = 13  TeV during LHC Run 2 (2015–2018), corresponding to an integrated luminosity of 139  $$\hbox {fb}^{-1}$$ fb - 1 . No significant excess above the Standard Model predictions is found. The results are expressed as 95% confidence-level limits on the parameters of the signal models considered.