The Photon Wave Function in Non-forward Diffractive Scattering with Non-vanishing Quark Masses

The light-cone Photon wave function in explicit helicity states, valid for massive quarks and in both momentum and configuration space, is presented by considering the leading order photon-proton hard scattering, i.e., the splitting quark pair scatters with the proton in the Regge limit. Further we apply it to the diffractive scattering at nonzero momentum transfer and reach a similar factorization as in the case of zero momentum transfer.Comment: 11 pages LaTeX, 2 figures, version to appear in Phys. Rev.

Soft and diffractive scattering with the cluster model in Herwig

We present a new model for soft interactions in the event-generator Herwig. The model consists of two components. One to model diffractive final states on the basis of the cluster hadronization model and a second component that addresses soft multiple interactions as multiple particle production in multiperipheral kinematics. We present much improved results for minimum-bias measurements at various LHC energies

The (gamma^*-q\bar q)-Reggeon Vertex in Next-to-Leading Order QCD

As a first step towards the computation of the NLO corrections to the photon impact factor in the $\gamma^*\gamma^* \to \gamma^* \gamma^*$ scattering process, we calculate the one loop corrections to the coupling of the reggeized gluon to the $\gamma^*\to q\bar{q}$ vertex. We list the results for the Feynman diagrams which contribute: all loop integrations are carried out, and the results are presented in the helicity basis of photon, quark, and antiquark.Comment: 26 pages LaTeX, 3 figures, typos fixe

A probabilistic approach to emission-line galaxy classification

We invoke a Gaussian mixture model (GMM) to jointly analyse two traditional emission-line classification schemes of galaxy ionization sources: the Baldwin-Phillips-Terlevich (BPT) and $\rm W_{H\alpha}$ vs. [NII]/H$\alpha$ (WHAN) diagrams, using spectroscopic data from the Sloan Digital Sky Survey Data Release 7 and SEAGal/STARLIGHT datasets. We apply a GMM to empirically define classes of galaxies in a three-dimensional space spanned by the $\log$ [OIII]/H$\beta$, $\log$ [NII]/H$\alpha$, and $\log$ EW(H${\alpha}$), optical parameters. The best-fit GMM based on several statistical criteria suggests a solution around four Gaussian components (GCs), which are capable to explain up to 97 per cent of the data variance. Using elements of information theory, we compare each GC to their respective astronomical counterpart. GC1 and GC4 are associated with star-forming galaxies, suggesting the need to define a new starburst subgroup. GC2 is associated with BPT's Active Galaxy Nuclei (AGN) class and WHAN's weak AGN class. GC3 is associated with BPT's composite class and WHAN's strong AGN class. Conversely, there is no statistical evidence -- based on four GCs -- for the existence of a Seyfert/LINER dichotomy in our sample. Notwithstanding, the inclusion of an additional GC5 unravels it. The GC5 appears associated to the LINER and Passive galaxies on the BPT and WHAN diagrams respectively. Subtleties aside, we demonstrate the potential of our methodology to recover/unravel different objects inside the wilderness of astronomical datasets, without lacking the ability to convey physically interpretable results. The probabilistic classifications from the GMM analysis are publicly available within the COINtoolbox (https://cointoolbox.github.io/GMM\_Catalogue/).Comment: Accepted for publication in MNRA

Exploring the spectroscopic diversity of type Ia supernovae with DRACULA: a machine learning approach

The existence of multiple subclasses of type Ia supernovae (SNeIa) has been the subject of great debate in the last decade. One major challenge inevitably met when trying to infer the existence of one or more subclasses is the time consuming, and subjective, process of subclass definition. In this work, we show how machine learning tools facilitate identification of subtypes of SNeIa through the establishment of a hierarchical group structure in the continuous space of spectral diversity formed by these objects. Using Deep Learning, we were capable of performing such identification in a 4 dimensional feature space (+1 for time evolution), while the standard Principal Component Analysis barely achieves similar results using 15 principal components. This is evidence that the progenitor system and the explosion mechanism can be described by a small number of initial physical parameters. As a proof of concept, we show that our results are in close agreement with a previously suggested classification scheme and that our proposed method can grasp the main spectral features behind the definition of such subtypes. This allows the confirmation of the velocity of lines as a first order effect in the determination of SNIa subtypes, followed by 91bg-like events. Given the expected data deluge in the forthcoming years, our proposed approach is essential to allow a quick and statistically coherent identification of SNeIa subtypes (and outliers). All tools used in this work were made publicly available in the Python package Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy (DRACULA) and can be found within COINtoolbox (https://github.com/COINtoolbox/DRACULA).Comment: 16 pages, 12 figures, accepted for publication in MNRA

Coherent Parton Showers with Local Recoils

We outline a new formalism for dipole-type parton showers which maintain exact energy-momentum conservation at each step of the evolution. Particular emphasis is put on the coherence properties, the level at which recoil effects do enter and the role of transverse momentum generation from initial state radiation. The formulated algorithm is shown to correctly incorporate coherence for soft gluon radiation. Furthermore, it is well suited for easing matching to next-to-leading order calculations.Comment: 24 pages, 3 figure

Probing the low transverse momentum domain of Z production with novel variables

The measurement of the low transverse momentum region of vector boson production in Drell-Yan processes has long been invaluable to testing our knowledge of QCD dynamics both beyond fixed-order in perturbation theory as well as in the non-perturbative region. Recently the D\O\ collaboration have introduced novel variables which lead to improved measurements compared to the case of the standard QT variable. To complement this improvement on the experimental side, we develop here a complete phenomenological study dedicated in particular to the new \phi* variable. We compare our study, which contains the state-of-the-art next-to-next-to-leading resummation of large logarithms and a smooth matching to the full next-to-leading order result, to the experimental data and find excellent agreement over essentially the entire range of \phi*, even without direct inclusion of non-perturbative effects. We comment on our findings and on the potential for future studies to constrain non-perturbative behaviour.Comment: 20 pages, 7 figures. Version accepted for publication in JHEP. A figure with comparison to RESBOS has been adde

Exploring the spectroscopic diversity of type Ia supernovae with DRACULA: a machine learning approach

The existence of multiple subclasses of type Ia supernovae (SNeIa) has been the subject of great debate in the last decade. One major challenge inevitably met when trying to infer the existence of one or more subclasses is the time consuming, and subjective, process of subclass definition. In this work, we show how machine learning tools facilitate the automatic discovery of sub-populations of SNIa; to that end we introduce the DRACULA Python package (Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy). Our approach is divided in three steps: (i) Transfer Learning, which takes advantage of all available spectra (even those without an epoch estimate) as an information source, (ii) dimensionality reduction through Deep Learning and (iii) unsupervised learning (clustering) using K-Means. Results match a previously suggested classification scheme, showing that the proposed method is able to grasp the main spectral features behind the definition of such subclasses. Moreover, our methodology is capable of automatically identifying a hierarchical structure of spectral features. This allows the confirmation of the velocity of lines as a first order effect in the determination of SNIa sub-classes, followed by 91bg-like events. In this context, SNIa spectra are described by a space of 4 dimensions + 1 for the time evolution of objects. We interpreted this as evidence that the progenitor system and the explosion mechanism should be described by a small number of initial physical parameters. Given the expected data deluge in the forthcoming years, our proposed approach is essential to allow a quick and statistically coherent identification of subclasses (and outliers). DRACULA is publicly available within COINtoolbox (https://github.com/COINtoolbox/DRACULA)

Jet vetoing and Herwig++

We investigate the simulation of events with gaps between jets with a veto on additional radiation in the gap in Herwig++. We discover that the currently-used random treatment of radiation in the parton shower is generating some unphysical behaviour for wide-angle gluon emission in QCD 2 to 2 scatterings. We explore this behaviour quantitatively by making the same assumptions as the parton shower in the analytical calculation. We then modify the parton shower algorithm in order to correct the simulation of QCD radiation.Comment: 18 pages, 11 figure