Performance of the ALICE muon trigger system in pp and Pb/en-dashPb collisions at the LHC

The ALICE muon spectrometer studies the production of quarkonia and open heavy- flavour particles. It is equipped with a Trigger System composed of Resistive Plate Chambers which, by applying a transverse-momentum-based muon selection, minimises the background from light-hadron decays. The system has been continuously taking data throughout the LHC Run I; it has undergone maintenance and consolidation operations during the LHC shutdown period 1. In the first year of the LHC Run II, the system, fully recommissioned, has participated in data taking in pp and Pb/en-dashPb collisions. The performance of the system throughout the last data-taking period is presented.Comment: RPC 2016 Conference proceedin

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Study of quarkonium production in ultra-relativistic nuclear collisions with ALICE at the LHC

ALICE is devoted to the study of a deconfined state of nuclear matter called Quark Gluon Plasma (QGP). The bottomonium (bound states of beauty-anti beauty quark) production is affected by the presence of the QGP, since bottomonium states are produced sooner than the QGP and witness the whole evolution of the plasma. In this analysis the data coming from Pb-Pb collisions at √sNN = 5 TeV have been analyzed in order to detect possible modifications of the production rates, with respect to the rates observed in proton proton collisions. Furthermore, the performances of the detectors involved in the muon identification during the LHC RUN1 and RUN2 has been tested using a new analysis framework implemented as part of this thesis. Finally, in order to optimize the results of future analyses, a new muon identification algorithm has been developed and tested. This algorithm will become necessary in the LHC RUN3 running conditions, when the much higher luminosity will require a quasi-online reconstruction of data

Big Data and Big Science - Tossing a trick coin

National audienc

Υ\Upsilon production in p-Pb and Pb-Pb collisions with ALICE at the LHC

International audienceALICE (A Large Ion Collider Experiment) is devoted to the study of heavy-ion collisions at LHC energies. In such collisions a deconfined state of nuclear matter, the Quark-Gluon Plasma (QGP), is formed. Due to their early production, quarkonium states are good probes to study the QGP evolution. Such states are affected by suppression mechanisms which lead to reduced yields with respect to pp and p-Pb collisions, while regeneration phenomena might lead to an enhancement of their production. The latter effects are expected to be negligible at LHC for bottomonium states. The recent ALICE results on ϒ production in Pb-Pb collisions at sNN=5.02 TeV will be presented and compared with previous measurements at sNN=2.76 TeV . A comparison with theoretical calculations will be performed as well. Results obtained in p-Pb collisions at sNN=5.02 TeV will also be discussed

Study of quarkonium production in p-A and AA collisions with ALICE at the LHC

International audienc

Delivering a machine learning course on HPC resources

In recent years, proficiency in data science and machine learning (ML) became one of the most requested skills for jobs in both industry and academy. Machine learning algorithms typically require large sets of data to train the models and extensive usage of computing resources, both for training and inference. Especially for deep learning algorithms, training performances can be dramatically improved by exploiting Graphical Processing Units (GPUs). The needed skill set for a data scientist is therefore extremely broad, and ranges from knowledge of ML models to distributed programming on heterogeneous resources. While most of the available training resources focus on ML algorithms and tools such as TensorFlow, we designed a course for doctoral students where model training is tightly coupled with underlying technologies that can be used to dynamically provision resources. Throughout the course, students have access to a dedicated cluster of computing nodes on local premises. A set of libraries and helper functions is provided to execute a parallelized ML task by automatically deploying a Spark driver and several Spark execution nodes as Docker containers. Task scheduling is managed by an orchestration layer (Kubernetes). This solution automates the delivery of the software stack required by a typical ML workflow and enables scalability by allowing the execution of ML tasks, including training, over commodity (i.e. CPUs) or high-performance (i.e. GPUs) resources distributed over different hosts across a network. The adaptation of the same model on OCCAM, the HPC facility at the University of Turin, is currently under development