Di-photon excess illuminates Dark Matter

We propose a simplified model of dark matter with a scalar mediator to accommodate the di-photon excess recently observed by the ATLAS and CMS collaborations. Decays of the resonance into dark matter can easily account for a relatively large width of the scalar resonance, while the magnitude of the total width combined with the constraint on dark matter relic density lead to sharp predictions on the parameters of the Dark Sector. Under the assumption of a rather large width, the model predicts a signal consistent with ~300 GeV dark matter particle in channels with large missing energy. This prediction is not yet severely bounded by LHC Run I searches and will be accessible at the LHC Run II in the jet plus missing energy channel with more luminosity. Our analysis also considers astrophysical constraints, pointing out that future direct detection experiments will be sensitive to this scenario.Comment: 23 pages, 8 figures. Added 2 figures and more discussion

MadDM v.1.0: Computation of Dark Matter Relic Abundance Using MadGraph5

We present MadDM v.1.0, a numerical tool to compute dark matter relic abundance in a generic model. The code is based on the existing MadGraph 5 architecture and as such is easily integrable into any MadGraph collider study. A simple Python interface offers a level of user-friendliness characteristic of MadGraph 5 without sacrificing functionality. MadDM is able to calculate the dark matter relic abundance in models which include a multi-component dark sector, resonance annihilation channels and co-annihilations. We validate the code in a wide range of dark matter models by comparing the relic density results from MadDM to the existing tools and literature.Comment: 35 pages, 6 figure

MadDM: New Dark Matter Tool in the LHC era

We present the updated version of MadDM, a new dark matter tool based on MadGraph5_aMC@NLO framework. New version includes direct detection capability in addition to relic abundance computation. In this article, we provide short description of the implementation of relevant effective operators and validations against existing results in literature.Comment: 4 pages. Submitted to the proceedings of PPC 201

Measuring Boosted Tops in Semi-leptonic ttˉt\bar t Events for the Standard Model and Beyond

We present a procedure for tagging boosted semi-leptonic $t\bar t$ events based on the Template Overlap Method. We introduce a new formulation of the template overlap function for leptonically decaying boosted tops and show that it can be used to compensate for the loss of background rejection due to reduction of b-tagging efficiency at high $p_T$. A study of asymmetric top pair production due to higher order effects shows that our approach improves the resolution of the truth level kinematic distributions. We show that the hadronic top overlap is weakly susceptible to pileup up to 50 interactions per bunch crossing, while leptonic overlap remains impervious to pileup to at least 70 interactions. A case study of Randall-Sundrum Kaluza-Klein gluon production suggests that the new formulation of semi-leptonic template overlap can extend the projected exclusion of the LHC $\sqrt{s}$ = 8 TeV run to Kaluza-Klein gluon masses of 2.7 TeV, using the leading order signal cross section.Comment: 34 pages, 18 figures; v2. Matches version accepted for publication in JHEP. We added new paragraphs in the introduction and in section 7 to explain more clearly the scope of our study and limitations. Results unchange

Direct Detection of Dark Matter with MadDM v.2.0

We present MadDM v.2.0, a numerical tool for dark matter physics in a generic model. This version is the next step towards the development of a fully automated framework for dark matter searches at the interface of collider physics, astro-physics and cosmology. It extends the capabilities of v.1.0 to perform calculations relevant to the direct detection of dark matter. These include calculations of spin-independent/spin-dependent nucleon scattering cross sections and nuclear recoil rates (as a function of both energy and angle), as well as a simplified functionality to compare the model points with existing constraints. The functionality of MadDM v.2.0 incorporates a large selection of dark matter detector materials and sizes, and simulates detector effects on the nuclear recoil signals. We validate the code in a wide range of dark matter models by comparing results from MadDM v.2.0 to the existing tools and literature.Comment: 38 pages, 8 figures, 5 tables; v2. Matches the version accepted for publication in Physics of the Dark Universe. We have improved table IV by validating the other sps points of the MSS

Playing Tag with ANN: Boosted Top Identification with Pattern Recognition

Many searches for physics beyond the Standard Model at the Large Hadron Collider (LHC) rely on top tagging algorithms, which discriminate between boosted hadronic top quarks and the much more common jets initiated by light quarks and gluons. We note that the hadronic calorimeter (HCAL) effectively takes a "digital image" of each jet, with pixel intensities given by energy deposits in individual HCAL cells. Viewed in this way, top tagging becomes a canonical pattern recognition problem. With this motivation, we present a novel top tagging algorithm based on an Artificial Neural Network (ANN), one of the most popular approaches to pattern recognition. The ANN is trained on a large sample of boosted tops and light quark/gluon jets, and is then applied to independent test samples. The ANN tagger demonstrated excellent performance in a Monte Carlo study: for example, for jets with p_T in the 1100-1200 GeV range, 60% top-tag efficiency can be achieved with a 4% mis-tag rate. We discuss the physical features of the jets identified by the ANN tagger as the most important for classification, as well as correlations between the ANN tagger and some of the familiar top-tagging observables and algorithms.Comment: 20 pages, 9 figure

MadDM v.3.0: a Comprehensive Tool for Dark Matter Studies

We present MadDM v.3.0, a numerical tool to compute particle dark matter observables in generic new physics models. The new version features a comprehensive and automated framework for dark matter searches at the interface of collider physics, astrophysics and cosmology and is deployed as a plugin of the MadGraph5_aMC@NLO platform, inheriting most of its features. With respect to the previous version, MadDM v.3.0 can now provide predictions for indirect dark matter signatures in astrophysical environments, such as the annihilation cross section at present time and the energy spectra of prompt photons, cosmic rays and neutrinos resulting from dark matter annihilation. MadDM indirect detection features support both $2\to2$ and $2 \to n$ dark matter annihilation processes. In addition, the ability to compare theoretical predictions with experimental constraints is extended by including the Fermi-LAT likelihood for gamma-ray constraints from dwarf spheroidal galaxies and by providing an interface with the nested sampling algorithm PyMultinNest to perform high dimensional parameter scans efficiently. We validate the code for a wide set of dark matter models by comparing the results from MadDM v.3.0 to existing tools and results in the literature.Comment: 42 pages + references, 13 figures, 2 tables. Revised version matches the published version, minor change