Engineering topics
Publications and source records attributed to Carminati, Federico.
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One of the major objectives of the experimental programs at the LHC is the discovery of new physics. This requires the identification of rare signals in immense backgrounds. Using machine learning algorithms greatly enhances our ability to achieve this objective. With the progress of quantum technologies, quantum machine learning could become a powerful tool for data analysis in high energy physics. In this study, using IBM gate-model quantum computing systems, we employ the quantum variational classifier method and the quantum kernel estimator method in two recent LHC flagship physics analyses: $t\bar{t}H$ (Higgs boson production in association with a top quark pair) and $H\rightarrow\mu\mu$ (Higgs boson decays to two muons). We have obtained early results with 10 qubits on the IBM quantum simulator and the IBM quantum hardware. On the quantum simulator, the quantum machine learning methods perform similarly to classical algorithms such as SVM (support vector machine) and BDT (boosted decision tree), which are often employed in LHC physics analyses. On the quantum hardware, the quantum machine learning methods have shown promising discrimination power, comparable to that on the quantum simulator. This study demonstrates that quantum machine learning has the ability to differentiate between signal and background in realistic physics datasets.
Common and community software packages, such as ROOT, Geant4 and event generators have been a key part of the LHC's success so far and continued development and optimisation will be critical in the future. The challenges are driven by an ambitious physics programme, notably the LHC accelerator upgrade to high-luminosity, HL-LHC, and the corresponding detector upgrades of ATLAS and CMS. In this document we address the issues for software that is used in multiple experiments (usually even more widely than ATLAS and CMS) and maintained by teams of developers who are either not linked to a particular experiment or who contribute to common software within the context of their experiment activity. We also give space to general considerations for future software and projects that tackle upcoming challenges, no matter who writes it, which is an area where community convergence on best practice is extremely useful.
The NASA-funded project reported on at the first IWSSRR in Arona to develop a Monte-Carlo simulation program for use in simulating the space radiation environment based on the FLUKA and ROOT codes is well into its second year of development, and considerable progress has been made. The general tasks required to achieve the final goals include the addition of heavy-ion interactions into the FLUKA code and the provision of a ROOT-based interface to FLUKA. The most significant progress to date includes the incorporation of the DPMJET event generator code within FLUKA to handle heavy-ion interactions for incident projectile energies greater than 3GeV/A. The ongoing effort intends to extend the treatment of these interactions down to 10 MeV, and at present two alternative approaches are being explored. The ROOT interface is being pursued in conjunction with the CERN LHC ALICE software team through an adaptation of their existing AliROOT software. As a check on the validity of the code, a simulation of the recent data taken by the ATIC experiment is underway.