Abstract: The Xi(++)(cc) -> Xi('+)(c)pi(+) decay is observed using proton-proton collisions collected by the LHCb experiment at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 5.4 fb(-1). The Xi(++)(cc) -> Xi('+)(c)pi(+) decay is reconstructed partially, where the photon from the Xi('+)(c) -> Xi(+)(c)gamma decay is not reconstructed and the pK(-)pi(+) final state of the Sc+ baryon is employed. The Xi(++)(cc) -> Xi('+)(c)pi(+) branching fraction relative to that of the Xi(++)(cc) -> Xi('+)(c)pi(+) decay is measured to be 1.41 +/- 0.17 +/- 0.10, where the first uncertainty is statistical and the second systematic.

Abstract: The simple m(2)phi(2) potential as an inflationary model is coming under increasing tension with limits on the tensor-to-scalar ratio r and measurements of the scalar spectral index n(s). Cubic Galileon interactions in the context of the Horndeski action can potentially reconcile the observables. However, we show that this cannot be achieved with only a constant Galileon mass scale because the interactions turn off too slowly, leading also to gradient instabilities after inflation ends. Allowing for a more rapid transition can reconcile the observables but moderately breaks the slow-roll approximation leading to a relatively large and negative running of the tilt alpha(s) that can be of order n(s) – 1. We show that the observables on CMB and large scale structure scales can be predicted accurately using the optimized slow-roll approach instead of the traditional slow-roll expansion. Upper limits on vertical bar alpha(s)vertical bar place a lower bound of r greater than or similar to 0.005 and, conversely, a given r places a lower bound on vertical bar alpha(s)vertical bar, both of which are potentially observable with next generation CMB and large scale structure surveys.

Abstract: A search for CP and P violation using triple- product asymmetries is performed with Lambda(0)(b) -> pK(-) pi(+) pi(-), Lambda(0)(b) -> pK(-) K+ K- and Xi(0)(b) -> pK(-) K- pi(+) decays. The data sample corresponds to integrated luminosities of 1.0 fb(-1) and 2.0 fb(-1), recorded with the LHCb detector at centre- of- mass energies of 7TeV and 8TeV, respectively. The CP- and P-violating asymmetries are measured both integrating over all phase space and in speci fi c phase- space regions. No signi fi cant deviation from CP or P symmetry is found. The fi rst observation of Lambda(0)(b) -> pK(-) chi(c0) (1P)(->pi(+) pi(-), K+ K-) decay is also reported.

Abstract: The Large Hadron Collider (LHC) is about to enter its third run at unprecedented energies. The experiments at the LHC face computational challenges with enormous data volumes that need to be analysed by thousands of physics users. The ATLAS EventIndex project, currently running in production, builds a complete catalogue of particle collisions, or events, for the ATLAS experiment at the LHC. The distributed nature of the experiment data model is exploited by running jobs at over one hundred Grid data centers worldwide. Millions of files with petabytes of data are indexed, extracting a small quantity of metadata per event, that is conveyed with a data collection system in real time to a central Hadoop instance at CERN. After a successful first implementation based on a messaging system, some issues suggested performance bottlenecks for the challenging higher rates in next runs of the experiment. In this work we characterize the weaknesses of the previous messaging system, regarding complexity, scalability, performance and resource consumption. A new approach based on an object-based storage method was designed and implemented, taking into account the lessons learned and leveraging the ATLAS experience with this kind of systems. We present the experiment that we run during three months in the real production scenario worldwide, in order to evaluate the messaging and object store approaches. The results of the experiment show that the new object-based storage method can efficiently support large-scale data collection for big data environments like the next runs of the ATLAS experiment at the LHC.

Abstract: The dark matter sector remains completely unknown. It is therefore crucial to keep an open mind regarding its nature and possible interactions. Focusing on the case of Weakly Interacting Massive Particles, in this work we make this general philosophy more concrete by applying modern machine learning techniques to dark matter direct detection. We do this by encoding and decoding the graphical representation of background events in the XENONnT experiment with a convolutional variational autoencoder. We describe a methodology that utilizes the `anomaly score' derived from the reconstruction loss of the convolutional variational autoencoder as well as a pre-trained standard convolutional neural network, in a semi-supervised fashion. Indeed, we observe that optimum results are obtained only when both unsupervised and supervised anomaly scores are considered together. A data set that has a higher proportion of anomaly score is deemed anomalous and deserves further investigation. Contrary to classical analyses, in principle all information about the events is used, preventing unnecessary information loss. Lastly, we demonstrate the reach of learning-focused anomaly detection in this context by comparing results with classical inference, observing that, if tuned properly, these techniques have the potential to outperform likelihood-based methods.