Abstract: In the present work, we show that the Galactic Center Excess (GCE) emission, as recently updated by the Fermi-LAT Collaboration, could be explained by a mixture of Fermi bubbles-like emission plus dark matter (DM) annihilation, in the context of a scalar-singlet Higgs portal scenario (SHP). In fact, the standard SHP, where the DM particle, S, only has renormalizable interactions with the Higgs, is non-operational due to strong constraints, especially from DM direct detection limits. Thus we consider the most economical extension, called ESHP (for extended SHP), which consists solely in the addition of a second (more massive) scalar singlet in the dark sector. The second scalar can be integrated-out, leaving a standard SHP plus a dimension-6 operator. Mainly, this model has only two relevant parameters (the DM mass and the coupling of the dim-6 operator). DM annihilation occurs mainly into two Higgs bosons, SS -> hh. We demonstrate that, despite its economy, the ESHP model provides an excellent fit to the GCE (with p-value similar to 0.6-0.7) for very reasonable values of the parameters, in particular, ms similar or equal to 130 GeV. This agreement of the DM candidate to the GCE properties does not clash with other observables and keep the S – particle relic density at the accepted value for the DM content in the universe.

Abstract: A key research question at the Large Hadron Collider is the test of models of new physics. Testing if a particular parameter set of such a model is excluded by LHC data is a challenge: it requires time consuming generation of scattering events, simulation of the detector response, event reconstruction, cross section calculations and analysis code to test against several hundred signal regions defined by the ATLAS and CMS experiments. In the BSM-AI project we approach this challenge with a new idea. A machine learning tool is devised to predict within a fraction of a millisecond if a model is excluded or not directly from the model parameters. A first example is SUSY-AI, trained on the phenomenological supersymmetric standard model (pMSSM). About 300,000 pMSSM model sets – each tested against 200 signal regions by ATLAS – have been used to train and validate SUSY-AI. The code is currently able to reproduce theATLAS exclusion regions in 19 dimensions with an accuracy of at least 93%. It has been validated further within the constrained MSSM and the minimal natural supersymmetric model, again showing high accuracy. SUSY-AI and its future BSM derivatives will help to solve the problem of recasting LHC results for any model of new physics. SUSY-AI can be downloaded from http://susyai.hepforge.org/. An on-line interface to the program for quick testing purposes can be found at http://www.susy-ai.org/.

Abstract: The Galactic Center Excess (GCE) in GeV gamma rays has been debated for over a decade, with the possibility that it might be due to dark matter annihilation or undetected point sources such as millisecond pulsars (MSPs). This study investigates how the gamma-ray emission model (-yEM) used in Galactic center analyses affects the interpretation of the GCE's nature. To address this issue, we construct an ultra-fast and powerful inference pipeline based on convolutional Deep Ensemble Networks. We explore the two main competing hypotheses for the GCE using a set of-yEMs with increasing parametric freedom. We calculate the fractional contribution (fsrc) of a dim population of MSPs to the total luminosity of the GCE and analyze its dependence on the complexity of the ryEM. For the simplest ryEM, we obtain fsrc = 0.10 f 0.07, while the most complex model yields fsrc = 0.79 f 0.24. In conclusion, we find that the statement about the nature of the GCE (dark matter or not) strongly depends on the assumed ryEM. The quoted results for fsrc do not account for the additional uncertainty arising from the fact that the observed gamma-ray sky is out-of-distribution concerning the investigated ryEM iterations. We quantify the reality gap between our ryEMs using deep-learning-based One-Class Deep Support Vector Data Description networks, revealing that all employed ryEMs have gaps to reality. Our study casts doubt on the validity of previous conclusions regarding the GCE and dark matter, and underscores the urgent need to account for the reality gap and consider previously overlooked “out of domain” uncertainties in future interpretations.

Abstract: We study SO(10). SU(5) and flipped SU(5) GUT models with non-universal soft supersynrimetry-breaking scalar masses, exploring how they are constrained by LIIC super-synrimetry searches and cold dark matter experiments, and how they can be probed and distinguished in future experiments. We find characteristic differences between the-various GUT scenarios, particularly in the coannihilation region, which is very sensitive to changes of parameters. For example, the flipped SU(5) GUT predicts the possibility of (t) over tilde (1-chi) coannihilation, which is absent in the regions of the SO(10) and SU(5) GUT parameter spaces that we study. We use the relic density predictions in different models to determine upper bounds for the neutralino masses, and we find large differences between different GUT models in the sparticle spectra for the same LSP mass, leading to direct connections of distinctive possible experimental measurements with the structure of the GUT group. We find that future LHC searches for generic missing E-T, charginos and stops will be able to constrain the different GUT models in complementary ways, as will the Xenon 1 ton and Darwin dark matter scattering experiments and future FERMI or CIA gamma-ray searches.

Abstract: We perform a forecast of the MSSM with universal soft terms (CMSSM) for the LHC, based on an improved Bayesian analysis. We do not incorporate ad hoc measures of the fine-tuning to penalize unnatural possibilities: such penalization arises from the Bayesian analysis itself when the experimental value of M-Z is considered. This allows to scan the whole parameter space, allowing arbitrarily large soft terms. Still the low-energy region is statistically favoured (even before including dark matter or g-2 constraints). Contrary to other studies, the results are almost unaffected by changing the upper limits taken for the soft terms. The results are also remarkable stable when using flat or logarithmic priors, a fact that arises from the larger statistical weight of the low-energy region in both cases. Then we incorporate all the important experimental constrains to the analysis, obtaining a map of the probability density of the MSSM parameter space, i.e. the forecast of the MSSM. Since not all the experimental information is equally robust, we perform separate analyses depending on the group of observables used. When only the most robust ones are used, the favoured region of the parameter space contains a significant portion outside the LHC reach. This effect gets reinforced if the Higgs mass is not close to its present experimental limit and persits when dark matter constraints are included. Only when the g-2 constraint (based on e(+)e(-) data) is considered, the preferred region (for μ> 0) is well inside the LHC scope. We also perform a Bayesian comparison of the positive- and negative-mu possibilities.