Francesco Micheli


Last Name

Micheli

First Name

Francesco

ORCID

0000-0002-2727-5237

Organisational unit

03751 - Lygeros, John / Lygeros, John

Filters

2020 - 20255
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Publications 1 - 5 of 5
  • Data-Driven Control and Decision-Making under Uncertainty
    Item type: Doctoral Thesis
    Micheli, Francesco (2025)
  • Search for lepton flavour violating decays of a neutral heavy Higgs boson to μτ and eτ in proton-proton collisions at √s= 13 TeV
    Item type: Journal Article
    CMS Collaboration; Sirunyan, Albert M.; Backhaus, Malte; et al. (2020)
    Journal of High Energy Physics
    A search for lepton flavour violating decays of a neutral non-standard-model Higgs boson in the μτ and eτ decay modes is presented. The search is based on proton-proton collisions at a center of mass energy √s= 13 TeV collected with the CMS detector in 2016, corresponding to an integrated luminosity of 35.9 fb−1. The τ leptons are reconstructed in the leptonic and hadronic decay modes. No signal is observed in the mass range 200–900 GeV. At 95% confidence level, the observed (expected) upper limits on the production cross section multiplied by the branching fraction vary from 51.9 (57.4) fb to 1.6 (2.1) fb for the μτ and from 94.1 (91.6) fb to 2.3 (2.3) fb for the eτ decay modes.
  • First measurement of the forward rapidity gap distribution in pPb collisions at √sNN = 8.16  TeV
    Item type: Journal Article
    CMS Collaboration; Tumasyan, Armen; Backhaus, Malte; et al. (2023)
    Physical Review D
    For the first time at LHC energies, the forward rapidity gap spectra from proton-lead collisions for both proton and lead dissociation processes are presented. The analysis is performed over 10.4 units of pseudorapidity at a center-of-mass energy per nucleon pair of √sNN = 8.16 TeV, almost 300 times higher than in previous measurements of diffractive production in proton-nucleus collisions. For lead dissociation processes, which correspond to the pomeron-lead event topology, the epos-lhc generator predictions are a factor of 2 below the data, but the model gives a reasonable description of the rapidity gap spectrum shape. For the pomeron-proton topology, the epos-lhc, qgsjet ii, and hijing predictions are all at least a factor of 5 lower than the data. The latter effect might be explained by a significant contribution of ultraperipheral photoproduction events mimicking the signature of diffractive processes. These data may be of significant help in understanding the high energy limit of quantum chromodynamics and for modeling cosmic ray air showers.
  • Two-particle azimuthal correlations in γp interactions using pPb collisions at √sNN = 8.16 TeV
    Item type: Journal Article
    The CMS Collaboration; Tumasyan, Armen; Backhaus, Malte; et al. (2023)
    Physics Letters B
    The first measurements of the Fourier coefficients (V_n∆) of the azimuthal distributions of charged hadrons emitted from photon-proton (γp) interactions are presented. The data are extracted from 68.8 nb⁻¹ of ultra-peripheral proton-lead (pPb) collisions at √sNN=8.16TeV using the CMS detector. The high energy lead ions produce a flux of photons that can interact with the oncoming proton. This γp system provides a set of unique initial conditions with multiplicity lower than in photon-lead collisions but comparable to recent electron-positron and electron-proton data. The V_N∆ coefficients are presented in ranges of event multiplicity and transverse momentum (p_T) and are compared to corresponding hadronic minimum bias pPb results. For a given multiplicity range, the mean p_T of charged particles is smaller in γp than in pPb collisions. For both the γp and pPb samples, V_1∆ is negative, V_2∆ is positive, and V_3∆ consistent with 0. For each multiplicity and p_T range, V_2∆ is larger for γp events. The γp data are consistent with model predictions that have no collective effects.
  • Stochastic MPC for energy hubs using data driven demand forecasting
    Item type: Conference Paper
    Micheli, Francesco; Behrunani, Varsha; Mehr, Jonas; et al. (2023)
    IFAC-PapersOnLine ~ 22nd IFAC World Congress
    Energy hubs convert and distribute energy resources by combining different energy inputs through multiple conversion and storage components. The optimal operation of the energy hub exploits its flexibility to increase the energy efficiency and reduce the operational costs. However, uncertainties in the demand present challenges to energy hub optimization. In this paper, we propose a stochastic MPC controller to minimize energy costs using chance constraints for the uncertain electricity and thermal demands. Historical data is used to build a demand prediction model based on Gaussian processes to generate a forecast of the future electricity and heat demands. The stochastic optimization problem is solved via the Scenario Approach by sampling multi-step demand trajectories from the derived prediction model. The performance of the proposed predictor and of the stochastic controller is verified on a simulated energy hub model and demand data from a real building.
Publications 1 - 5 of 5