Abstract: We study in detail the impact of the current uncertainty in nucleon matrix elements on the sensitivity of direct and indirect experimental techniques for dark matter detection. We perform two scans in the framework of the cMSSM: one using recent values of the pion-sigma term obtained from Lattice QCD, and the other using values derived from experimental measurements. The two choices correspond to extreme values quoted in the literature and reflect the current tension between different ways of obtaining information about the structure of the nucleon. All other inputs in the scans, astrophysical and from particle physics, are kept unchanged. We use two experiments, XENON100 and IceCube, as benchmark cases to illustrate our case. We find that the interpretation of dark matter search results from direct detection experiments is more sensitive to the choice of the central values of the hadronic inputs than the results of indirect search experiments. The allowed regions of cMSSM parameter space after including XENON100 constrains strongly differ depending on the assumptions on the hadronic matrix elements used. On the other hand, the constraining potential of IceCube is almost independent of the choice of these values.

Abstract: We carry out global fits to the nonuniversal Higgs Model (NUHM), applying all relevant present-day constraints. We present global probability maps for the NUHM parameters and observables (including collider signatures, direct, and indirect detection quantities), both in terms of posterior probabilities and in terms of profile likelihood maps. We identify regions of the parameter space where the neutralino dark matter in the model is either binolike, or else higgsinolike with mass close to 1 TeV and a spin-independent scattering cross section similar to 10(-9)-10(-8) pb. We trace the occurrence of the higgsinolike region to be a consequence of a mild focusing effect in the running of one of the Higgs masses, the existence of which in the NUHM we identify in our analysis. Although the usual binolike neutralino is more prominent, higgsinolike dark matter cannot be excluded, however its significance strongly depends on the prior and statistics used to assess it. We note that, despite experimental constraints often favoring different regions of parameter space to the constrained minimal supersymmetric standard model, most observational consequences appear fairly similar, which will make it challenging to distinguish the two models experimentally.

Abstract: Context. At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Aims. Here we present the first application of deep learning based algorithms to automatically detect and classify point sources from gamma-ray data. For concreteness we refer to this approach as AutoSourceID. Methods. To detect point sources, we utilized U-shaped convolutional networks for image segmentation and k-means for source clustering and localization. We also explored the Centroid-Net algorithm, which is designed to find and count objects. Using two algorithms allows for a cross check of the results, while a combination of their results can be used to improve performance. The training data are based on 9.5 years of exposure from The Fermi Large Area Telescope (Fermi-LAT) and we used source properties of active galactic nuclei (AGNs) and pulsars (PSRs) from the fourth Fermi-LAT source catalog in addition to several models of background interstellar emission. The results of the localization algorithm are fed into a classification neural network that is trained to separate the three general source classes (AGNs, PSRs, and FAKE sources). Results. We compared our localization algorithms qualitatively with traditional methods and find them to have similar detection thresholds. We also demonstrate the robustness of our source localization algorithms to modifications in the interstellar emission models, which presents a clear advantage over traditional methods. The classification network is able to discriminate between the three classes with typical accuracy of similar to 70%, as long as balanced data sets are used in classification training. We published online our training data sets and analysis scripts and invite the community to join the data challenge aimed to improve the localization and classification of gamma-ray point sources.

Abstract: Simulating nature and in particular processes in particle physics require expensive computations and sometimes would take much longer than scientists can afford. Here, we explore ways to a solution for this problem by investigating recent advances in generative modeling and present a study for the generation of events from a physical process with deep generative models. The simulation of physical processes requires not only the production of physical events, but to also ensure that these events occur with the correct frequencies. We investigate the feasibility of learning the event generation and the frequency of occurrence with several generative machine learning models to produce events like Monte Carlo generators. We study three processes: a simple two-body decay, the processes e(+)e(-)-> Z -> l(+)l(-) and pp -> tt<mml:mo><overbar></mml:mover> including the decay of the top quarks and a simulation of the detector response. By buffering density information of encoded Monte Carlo events given the encoder of a Variational Autoencoder we are able to construct a prior for the sampling of new events from the decoder that yields distributions that are in very good agreement with real Monte Carlo events and are generated several orders of magnitude faster. Applications of this work include generic density estimation and sampling, targeted event generation via a principal component analysis of encoded ground truth data, anomaly detection and more efficient importance sampling, e.g., for the phase space integration of matrix elements in quantum field theories. Here, the authors report buffered-density variational autoencoders for the generation of physical events. This method is computationally less expensive over other traditional methods and beyond accelerating the data generation process, it can help to steer the generation and to detect anomalies.

Abstract: We present global fits of an effective field theory description of real, and complex scalar dark matter candidates. We simultaneously take into account all possible dimension 6 operators consisting of dark matter bilinears and gauge invariant combinations of quark and gluon fields. We derive constraints on the free model parameters for both the real (five parameters) and complex (seven) scalar dark matter models obtained by combining Planck data on the cosmic microwave background, direct detection limits from LUX, and indirect detection limits from the Fermi Large Area Telescope. We find that for real scalars indirect dark matter searches disfavour a dark matter particle mass below 100 GeV. For the complex scalar dark matter particle current data have a limited impact due to the presence of operators that lead to p-wave annihilation, and also do not contribute to the spin-independent scattering cross-section. Although current data are not informative enough to strongly constrain the theory parameter space, we demonstrate the power of our formalism to reconstruct the theoretical parameters compatible with an actual dark matter detection, by assuming that the excess of gamma rays observed by the Fermi Large Area Telescope towards the Galactic centre is entirely due to dark matter annihilations. Please note that the excess can very well be due to astrophysical sources such as millisecond pulsars. We find that scalar dark matter interacting via effective field theory operators can in principle explain the Galactic centre excess, but that such interpretation is in strong tension with the non-detection of gamma rays from dwarf galaxies in the real scalar case. In the complex scalar case there is enough freedom to relieve the tension.