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Search for the lepton flavour violating decays B0→e±μ∓ and B0s→e±μ∓ with LHCb Run 2 data
No full textA large variety of new physics models suggest that the rates for lepton flavour violating -hadron decays may be much higher than predicted in the Standard Model, which leads to a high interest in the search for such decays.
This thesis presents the search for the lepton-flavour violating decays and with the data sample collected by the LHCb experiment between 2016 and 2018, which corresponds to an integrated luminosity of 5.4 fb of proton-proton collisions.
The analysis is performed while blinding the signal mass region, to avoid any bias introduced by the experimenter. The and branching fractions are measured with respect to the high statistics decay, with a fit to the invariant mass distribution in six independent data samples characterized by different background levels and signal mass resolutions due to electron bremsstrahlung. A dedicated selection is used to reduce background from random combinations and specific physics decays with a multivariate analysis and particle identification requirements, respectively. Efficiencies are determined from simulation, corrected for mismodeling effects with data-driven methods, and validated by measuring . The main physics backgrounds are identified to arise from decays to the , , and final states, and taken into account in the mass fit; the yields of the two components peaking in the signal mass window, (12.2 events) and (7.7 events), are estimated from simulation and validated with two independent data-driven methods. Pseudo-experiments are performed to test the fit stability, study potential biases, and estimate the sensitivity of the analysis.
In absence of signals and without any systematic uncertainty considered, the average expected upper limits at 95\%(90\%) confidence level are estimated to be , and or assuming the decay amplitude is completely dominated by the light (short-lived) or heavy (long-lived) mass eigenstate of the system, respectively.
This is an improvement in sensitivity by a factor 1.6 for and 2.8 for (when dominated by the heavy eigenstate), with respect to the previous best expected limits published by LHCb with the 2011 and 2012 data.
In addition, this thesis presents the quality assurance process of silicon photomultiplier detectors used for the Scintillating Fibre (SciFi) tracker of the recent LHCb detector upgrade.LPHE-O
A propos d'un immeuble d'angle aux Acacias et d'un immeuble traversant sur cour à Plainpalais à Genève
No full textLAM
Fabrication of Fresnel zone plates for hard X-rays
No full textA method to fabricate gold structures with high aspect ratio is presented. Fresnel zone plates with an outermost zone width of 100nm and structures of 1μm height are fabricated. Preliminary focusing results at an X-ray energy of 8keV are presented and ways to improve the zone plate parameters are discussed.LSX
Online optimisation and detection of process upset in semi-batch reactors using a kinetic modelling approach
No full textProcess Analytical Chemistry/Technology has tremendously evolved in the last decades due to the development of multivariate online sensors that are able to monitor the properties of industrial processes in real time [1, 2]. Online monitoring of product quality and the detection of process upsets are important for the pharmaceutical and fine chemical industry in order to maintain product specifications and meet their commitments regarding safety, health and environment. Many methods exist to extract useful information from the vast amount of data produced by online sensors. Chemometric methods, such as Principal Component Regression (PCR) and Partial Least Squares (PLS) or Black Box modelling (e.g. Neural Networks) are commonly used during the monitoring of batch processes [3, 4]. However, for these data-driven methods, calibration conditions need to be maintained during the actual process and the calibration generally behaves poorly when extrapolated to different operating conditions. On the other hand, kinetic modelling techniques [5], based on first principal models, describing the kinetics of main and side products, do not encounter such drawbacks and can be adapted for the monitoring of highly fluctuating processes, e.g. under semi-batch conditions. During batch and semi-batch processes, deviations from standard operating conditions can have various origins. Most frequent sources of deviations are due to slightly imprecise initial conditions (e.g. initial concentrations) or impurities in the initial reactants causing unexpected side reactions [6]. In this contribution, we propose a method for the online monitoring of semi-batch processes based on a kinetic modelling approach in order to optimise operating conditions and reduce “batch to batch” deviations. To our knowledge, this option has not yet been considered in literature. The proposed method requires the kinetic model and the associated rate constants to be known, i.e. determined in an early phase of R&D. In the following, the different steps of the algorithm, currently implemented into Matlab, are outlined. The algorithm assumes a first small amount of reagent to be dosed into the reaction mixture inside the reactor. Corrected initial concentrations are then determined by fitting the kinetic model to measurements, such as UV-vis, IR or heat power, using the Newton-Gauss-Levenberg/Marquardt (NGL/M) optimiser. If the optimiser fails the operator has the option to dose more reagent. Possible failure can be due to an early process upset, or to the fact that too little reagent was dosed in order to follow the kinetics reliably. The corrected initial concentrations are then fed back into the kinetic model and the algorithm optimises the flow rate for the dosed reagent or the operating temperature in order to maximise under constraints user-defined properties of the process, such as yield, selectivity or conversion. For this constrained optimisation, nonlinear programming (NLP) is employed (Matlab’s fmincon function). As soon as optimum operating conditions are obtained by the algorithm, the reactor will automatically run at these improved settings. As an option, flow rate and temperature can continuously be re-optimised to adapt to possible fluctuations in operating conditions. During the whole procedure, the algorithm also tests for possible process upset. If such an incident is detected the operator is asked to take appropriate action, for example a reactor shut down. The algorithm will be demonstrated using simulated data from mid-IR and UV-vis spectroscopy as well as from calorimetry. [1] P. Gemperline, G. Puxty, M. Maeder, D. Walker, F. Tarczynski, M. Bosserman, Analytical Chemistry 76 (2004) 2575-2582. [2] J. Workman, M. Koch, D. Veltkamp, Analytical Chemistry 77 (2005) 3789-3806. [3] M. Spear, Chemical Processing 70 (2007) 20-26. [4] T.J. Thurston, R.G. Brereton, D.J. Foord, R.E.A. Escott, Journal of Chemometrics 17 (2003) 313-322. [5] M. Maeder, Y.M. Neuhold, Practical Data Analysis in Chemistry, Elsevier, Amsterdam NL, 2007. [6] E.N.M. van Sprang, H.J. Ramaker, H.F.M. Boelens, J.A. Westerhuis, D. Whiteman, D. Baines, I. Weaver, Analyst 128 (2003) 98-102.IGMPresented as an Oral contributio
Structural and Magnetic Dynamics of a Laser Induced Phase Transition in FeRh
No full textWe use time-resolved x-ray diffraction and magneto-optical Kerr effect to study the laser-induced antiferromagnetic to ferromagnetic phase transition in FeRh. The structural response is given by the nucleation of independent ferromagnetic domains (tau(1) similar to 30 ps). This is significantly faster than the magnetic response (tau(2) similar to 60 ps) given by the subsequent domain realignment. X-ray diffraction shows that the two phases coexist on short time scales and that the phase transition is limited by the speed of sound. A nucleation model describing both the structural and magnetic dynamics is presented.LS
Comparative Dissociation of Peptide Polyanions by Electron Impact and Photo-Induced Electron Detachment
No full textWe compare product-ion mass spectra produced by electron detachment dissociation (EDD) and electron photodetachment dissociation (EPD) of multi-deprotonated peptides on a Fourier transform and a linear ion trap mass spectrometer, respectively. Both methods, EDD and EPD, involve the electron emission-induced formation of a radical oxidized species from a multi-deprotonated precursor peptide. Product-ion mass spectra display mainly fragment ions resulting from backbone cleavages of C-alpha-C bond ruptures yielding a and x ions. Fragment ions originating from N-C-alpha backbone bond cleavages are also observed, in particular by EPD. Although EDD and EPD methods involve the generation of a charge-reduced radical anion intermediate by electron emission, the product ion abundance distributions are drastically different. Both processes seem to be triggered by the location and the recombination of radicals (both neutral and cation radicals). Therefore, EPD product ions are predominantly formed near tryptophan and histidine residues, whereas in EDD the negative charge solvation sites on the backbone seem to be the most favorable for the nearby bond dissociation. (J Am Soc Mass Spectrom 2010, 21, 670-680) (C) 2010 Published by Elsevier Inc. on behalf of American Society for Mass SpectrometryLSM
Dihedral Angle Measurement of Pb in Cu: Advantages of TEM Sample Preparation by FIB
No full textSAMLA
The spectrum of heavy tailed random matrices
No full textLet X-N be an N x N random symmetric matrix with independent equidistributed entries. If the law P of the entries has a finite second moment, it was shown by Wigner [14] that the empirical distribution of the eigenvalues of X-N , once renormalized by root N, converges almost surely and in expectation to the so-called semicircular distribution as N goes to infinity. In this paper we study the same question when P is in the domain of attraction of an alpha-stable law. We prove that if we renormalize the eigenvalues by a constant a(N) of order N-1/alpha, the corresponding spectral distribution converges in expectation towards a law mu(alpha) and study some of its properties; it is a heavy-tailed probability measure which is absolutely continuous with respect to Lebesgue measure except possibly on a compact set of capacity zero.CI
Infrequent Rebalancing, Return Autocorrelation, and Seasonality
No full textA model of infrequent rebalancing can explain specific predictability patterns in the time series and cross-section of stock returns. First, infrequent rebalancing produces return autocorrelations that are consistent with empirical evidence from intraday returns and new evidence from daily returns. Autocorrelations can switch sign and become positive at the rebalancing horizon. Second, the cross-sectional variance in expected returns is larger when more traders rebalance. This effect generates seasonality in the cross-section of stock returns, which can help explain available empirical evidence.SFI-G