publications | Matteo Barbetti

Up-to-date list of publications available on Google Scholar, ResearchGate or Inspire HEP.

preprints

2023

arXiv

Improved measurement of \(CP\) violation parameters in \(B^0_s\to J/\psi K^{+}K^{-}\) decays in the vicinity of the \(\phi(1020)\) resonance

LHCb Collaboration: R. Aaij, and others

arXiv preprint – Submitted on 2 Aug 2023

Abs arXiv CDS

TBA
arXiv

Observation of the decays \(B_{(s)}^{0}\to D_{s1}(2536)^{\mp}K^{\pm}\)

LHCb Collaboration: R. Aaij, and others

arXiv preprint – Submitted on 1 Aug 2023

Abs arXiv CDS

TBA
arXiv

Observation of new baryons in the \(\Xi_b^-\pi^+\pi^-\) and \(\Xi_b^0\pi^+\pi^-\) systems

LHCb Collaboration: R. Aaij, and others

arXiv preprint – Submitted on 25 Jul 2023

Abs arXiv CDS

TBA
arXiv

Observation and branching fraction measurement of the decay \(\Xi_b^-\to\Lambda_b^0\pi^-\)

LHCb Collaboration: R. Aaij, and others

arXiv preprint – Submitted on 18 Jul 2023

Abs arXiv CDS

TBA
arXiv

Search for \(CP\) violation in the phase space of \(D^0 \to \pi^-\pi^+\pi^0\) decays with the energy test

LHCb Collaboration: R. Aaij, and others

arXiv preprint – Submitted on 22 Jun 2023

Abs arXiv CDS

TBA
arXiv

Measurements of \(CP\) asymmetries and branching fractions of \(B^-\) decays to two charm mesons

LHCb Collaboration: R. Aaij, and others

arXiv preprint – Submitted on 16 Jun 2023

Abs arXiv CDS

TBA
arXiv

Study of the Bose-Einstein correlations of same-sign pions in proton-lead collisions

LHCb Collaboration: R. Aaij, and others

arXiv preprint – Submitted on 16 Jun 2023

Abs arXiv CDS

TBA
arXiv

Evidence for the decays \(B^{0}\rightarrow\bar{D}^{(*)0}\phi\) and updated measurements of the branching fractions of the \(B^{0}_{s}\rightarrow\bar{D}^{(*)0}\phi\) decays

LHCb Collaboration: R. Aaij, and others

arXiv preprint – Submitted on 5 Jun 2023

Abs arXiv CDS

TBA
arXiv

Measurement of the mass difference and relative production rate of the \(\Omega^-_b\) and \(\Xi^-_b\) baryons

LHCb Collaboration: R. Aaij, and others

arXiv preprint – Submitted on 24 May 2023

Abs arXiv CDS

TBA
arXiv

Associated production of prompt \(J/\psi\) and \(\Upsilon\) mesons in \(pp\) collisions at \(\sqrt{s}=13~\rm{TeV}\)

LHCb Collaboration: R. Aaij, and others

arXiv preprint – Submitted on 24 May 2023

Abs arXiv CDS

TBA
arXiv

The LHCb upgrade I

LHCb Collaboration: R. Aaij, and others

arXiv preprint – Submitted on 17 May 2023

Abs arXiv CDS

The LHCb upgrade represents a major change of the experiment. The detectors have been almost completely renewed to allow running at an instantaneous luminosity five times larger than that of the previous running periods. Readout of all detectors into an all-software trigger is central to the new design, facilitating the reconstruction of events at the maximum LHC interaction rate, and their selection in real time. The experiment's tracking system has been completely upgraded with a new pixel vertex detector, a silicon tracker upstream of the dipole magnet and three scintillating fibre tracking stations downstream of the magnet. The whole photon detection system of the RICH detectors has been renewed and the readout electronics of the calorimeter and muon systems have been fully overhauled. The first stage of the all-software trigger is implemented on a GPU farm. The output of the trigger provides a combination of totally reconstructed physics objects, such as tracks and vertices, ready for final analysis, and of entire events which need further offline reprocessing. This scheme required a complete revision of the computing model and rewriting of the experiment's software.
arXiv

Measurement of \(\Xi_{c}^{+}\) production in \(p\rm{Pb}\) collisions at \(\sqrt{s_{\rm{NN}}}=8.16~\rm{TeV}\) at LHCb

LHCb Collaboration: R. Aaij, and others

arXiv preprint – Submitted on 11 May 2023

Abs arXiv CDS

TBA
arXiv

Study of charmonium decays to \(K^0_S K \pi\) in the \(B \to (K^0_S K \pi) K\) channels

LHCb Collaboration: R. Aaij, and others

arXiv preprint – Submitted on 28 Apr 2023

Abs arXiv CDS

TBA
arXiv

Precision measurement of \(CP\) violation in the penguin-mediated decay \(B_s^{0}\rightarrow\phi\phi\)

LHCb Collaboration: R. Aaij, and others

arXiv preprint – Submitted on 17 Apr 2023

Abs arXiv CDS

TBA
arXiv
Poster

Lamarr: LHCb ultra-fast simulation based on machine learning models

L. Anderlini, M. Barbetti, G. Corti, and 7 more authors
in 21st International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2022)
arXiv preprint – Submitted on 20 Mar 2023

Abs arXiv CDS Poster

Abut 90% of the computing resources available to the LHCb experiment has been spent to produce simulated data samples for Run 2 of the Large Hadron Collider at CERN. The upgraded LHCb detector will be able to collect larger data samples, requiring many more simulated events to analyze the data to be collected in Run 3. Simulation is a key necessity of analysis to interpret signal vs background and measure efficiencies. The needed simulation will far exceed the pledged resources, requiring an evolution in technologies and techniques to produce these simulated data samples. In this contribution, we discuss Lamarr, a Gaudi-based framework to speed-up the simulation production parametrizing both the detector response and the reconstruction algorithms of the LHCb experiment. Deep Generative Models powered by several algorithms and strategies are employed to effectively parametrize the high-level response of the single components of the LHCb detector, encoding within neural networks the experimental errors and uncertainties introduced in the detection and reconstruction phases. Where possible, models are trained directly on real data, statistically subtracting any background components through weights application. Embedding Lamarr in the general LHCb Gauss Simulation framework allows to combine its execution with any of the available generators in a seamless way. The resulting software package enables a simulation process completely independent of the Detailed Simulation used to date.
arXiv
Poster

Hyperparameter Optimization as a Service on INFN Cloud

Matteo Barbetti, and Lucio Anderlini
in 21st International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2022)
arXiv preprint – Submitted on 13 Jan 2023

Abs arXiv Poster Website

The simplest and often most effective way of parallelizing the training of complex Machine Learning models is to execute several training instances on multiple machines, possibly scanning the hyperparameter space to optimize the underlying statistical model and the learning procedure. Often, such a meta learning procedure is limited by the ability of accessing securely a common database organizing the knowledge of the previous and ongoing trials. Exploiting opportunistic GPUs provided in different environments represents a further challenge when designing such optimization campaigns. In this contribution we discuss how a set of REST APIs can be used to access a dedicated service based on INFN Cloud to monitor and possibly coordinate multiple training instances, with gradientless optimization techniques, via simple HTTP requests. The service, named Hopaas (Hyperparameter OPtimization As A Service), is made of web interface and sets of APIs implemented with a FastAPI back-end running through Uvicorn and NGINX in a virtual instance of INFN Cloud. The optimization algorithms are currently based on Bayesian techniques as provided by Optuna. A Python front-end is also made available for quick prototyping. We present applications to hyperparameter optimization campaigns performed combining private, INFN Cloud and CINECA resources.