Ll. Ametller

Vector-like fermion and standard Higgs production at hadron colliders

Vector-like fermions are characterized by large neutral current decay rates, in particular into Higgs bosons. If they exist, their clear signals at hadron colliders open a window to Higgs detection, especially to the intermediate Higgs mass region. We discuss in some detail rates and signatures for simple cases.

On the detectability of sleptons at large hadron colliders

Abstract We analyse the possibility of detecting sleptons at large hadron colliders. We compare the different slepton-pair-production mechanisms, qq, WW, gg → l ≈ l ∽ , finding that the small qq cross section is the largest one. Although Although electroweak and strong backgrounds tend to be large, we show that with appropriate cuts it may be possible to observe sleptons with a mass up to 250 (350) GeV at LHC (SSC), for a discovery limit of 10 events and an integrated luminosity of 10−4 pb−1. Sleptons can be also single produced in gaugino decays at large hadron colliders. Relatively light sleptons may be observable in these processes in specific models.

Zt and γt production via top flavour-changing neutral couplings at the Fermilab Tevatron

Abstract Associated single top production with a Z boson or a photon at large hadron colliders provides a precise determination of top flavour-changing neutral couplings. The best way to measure these couplings with the up quark at Tevatron is to search for events with three jets and missing energy or events with a photon, a charged lepton, a jet and missing energy. Other decay channels are also discussed.

Discriminating signal from background using neural networks: Application to top-quark search at the Fermilab Tevatron.

The application of neural networks in high energy physics to the separation of signal from background events is studied. A variety of problems usually encountered in this sort of analysis, from variable selection to systematic errors, are presented. The top-quark search is used as an example to illustrate the problems and proposed solutions. @S0556-2821~96!06013-4# PACS number~s!: 14.65.Ha, 02.50.Sk, 13.85.Qk It is well known that neural networks ~NN’s ! are useful tools for pattern recognition. In high energy physics, they have been used or proposed as good candidates for tasks of signal versus background classification. However, most of the existing studies are somewhat academic, in the sense that they essentially compare the NN performances with other classical techniques of classification using Monte Carlo ~MC! events for that purpose. In realistic applications, real events should be analyzed and compared with simulated events, introducing systematic effects which have to be taken into account and could significantly modify the efficiency of the analysis. We try to give some insight in this direction using the top quark search at the Fermilab Tevatron as illustration. The top quark has been observed by the Collider Detector at Fermilab ~CDF !@ 1 #and D0 @2# collaborations. Recently, NN’s have been applied to experimental top-quark searches by the D0 Collaboration @3#, for a fixed top-quark mass, concluding that NN’s are more efficient than traditional methods, in agreement with previous parton level studies @4#. In this paper we continue and complete the analysis of Ref. @4# for the top-quark search at the Tevatron. A more realistic study is performed by including parton hadronization and detector simulation with jet reconstruction. In addition, contrary to Ref. @4# where the top mass was fixed, the present study is valid for a large range of top mass values. Moreover, the number of kinematical variables considered is enlarged and different ways of selecting subsets of the most relevant ones to the process under consideration are discussed. Finally, the influence of systematic errors on the NN results is studied. The analysis is focused on the top-quark search at the ,

Lowest resonance in QCD from low-energy data

We worked out a generalization of su(2) chiral perturbation theory, including a perturbative singlet scalar field. The approach suggests that the prediction for sensible low-energy observables converge faster towards their physical value. The physical mass and width of the scalar particle are obtained through a simultaneous analysis of the pion vector form factor and the ???p 0 p 0 cross section. Both values are statistically consistent with the ones obtained by using Roy equations in p-p scattering. In addition we find indications that the photon-photon-singlet coupling is quite small.

Chiral-loop and vector-meson contributions to η → ππγγ decays

Abstract The process η → π 0 π 0 γγ is discussed in Chiral Perturbation Theory (ChPT). Special attention is deveted to one-loop corrections, η-η′ mixing effects and vector-meson dominance of ChPT counter-terms. The less interesting η → π + π − γγ transition is briefly discussed too.

NLO forward–backward charge asymmetries in pp( _ )→l−l+j production at large hadron colliders

Abstract We consider the next-to-leading order corrections, O ( α s ) , to forward–backward charge asymmetries for lepton-pair production in association with a large transverse momentum jet at large hadron colliders. We find that the leading order results are essentially confirmed. Although experimentally challenging and in practice with large backgrounds, these observables could provide a new determination of the weak mixing angle sin 2 θ eff lept ( M Z 2 ) with a statistical precision for each lepton flavour of ∼ 10 −3 ( 7 × 10 −3 ) at LHC (Tevatron), and if b jets are identified, of the b quark Z asymmetry A FB b with a statistical precision of ∼ 2 × 10 −3 ( 4 × 10 −2 ) at LHC (Tevatron).

Z' decays into four fermions

If a new Zis discovered with a mass � 1 TeV at LHC/SSC, its (rare) decays into two charged leptons plus missing transverse en- ergy will probe the Zcoupling to the lepton doublet �

Neural network analysis for γγ → π+π−π0 at Daphne☆

Abstract We consider the possibility of using neural networks in experimental data analysis in Daphne. We analyze the process γγ → π+π−π0 and its backgrounds using neural networks and we compare their performances with traditional methods of applying cuts on several kinematical variables. We find that the neural networks are more efficient and can be of great help for processes with small number of produced events.

Enhancing the top-quark signal at Fermilab Tevatron using neural nets.

We show, in agreement with previous studies, that neural nets can be useful for top-quark analysis at the Fermilab Tevatron. The main features of [ital t[bar t]] and background events in a mixed sample are projected on a single output, which controls the efficiency, purity, and statistical significance of the [ital t[bar t]] signal. We consider a feed-forward multilayer neural net for the CDF reported top-quark mass, using six kinematical variables as inputs. Our main results are based on the exhaustive comparison of the neural net performances with those obtainable from the standard experimental analysis, by imposing different sets of linear cuts over the same variables, showing how the neural net approach improves the standard analysis results.