24 research outputs found
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 Events for the Standard Model and Beyond
We present a procedure for tagging boosted semi-leptonic 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 . 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 = 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 and 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