Abstract: We present new global fits of the constrained Minimal Supersymmetric Standard Model (cMSSM), including LHC 1/fb integrated luminosity SUSY exclusion limits, recent LHC 5/fb constraints on the mass of the Higgs boson and XENON100 direct detection data. Our analysis fully takes into account astrophysical and hadronic uncertainties that enter the analysis when translating direct detection limits into constraints on the cMSSM parameter space. We provide results for both a Bayesian and a Frequentist statistical analysis. We find that LHC 2011 constraints in combination with XENON100 data can rule out a significant portion of the cMSSM parameter space. Our results further emphasise the complementarity of collider experiments and direct detection searches in constraining extensions of Standard Model physics. The LHC 2011 exclusion limit strongly impacts on low-mass regions of cMSSM parameter space, such as the stau co-annihilation region, while direct detection data can rule out regions of high SUSY masses, such as the Focus-Point region, which is unreachable for the LHC in the near future. We show that, in addition to XENON100 data, the experimental constraint on the anomalous magnetic moment of the muon plays a dominant role in disfavouring large scalar and gaugino masses. We find that, should the LHC 2011 excess hinting towards a Higgs boson at 126 GeV be confirmed, currently favoured regions of the cMSSM parameter space will be robustly ruled out from both a Bayesian and a profile likelihood statistical perspective.

Abstract: Aims. In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to design a network that can accurately estimate sources' features and their uncertainties from single-band image cutouts, given the approximated locations of the sources provided by the previously developed code AutoSourceID-Light (ASID-L) or other external catalogues. This work serves as a proof of concept, showing the potential of machine learning in estimating astronomical features when trained on meticulously crafted synthetic images and subsequently applied to real astronomical data.Methods. The algorithm presented here, AutoSourceID-FeatureExtractor (ASID-FE), uses single-band cutouts of 32x32 pixels around the localised sources to estimate flux, sub-pixel centre coordinates, and their uncertainties. ASID-FE employs a two-step mean variance estimation (TS-MVE) approach to first estimate the features and then their uncertainties without the need for additional information, for example the point spread function (PSF). For this proof of concept, we generated a synthetic dataset comprising only point sources directly derived from real images, ensuring a controlled yet authentic testing environment.Results. We show that ASID-FE, trained on synthetic images derived from the MeerLICHT telescope, can predict more accurate features with respect to similar codes such as SourceExtractor and that the two-step method can estimate well-calibrated uncertainties that are better behaved compared to similar methods that use deep ensembles of simple MVE networks. Finally, we evaluate the model on real images from the MeerLICHT telescope and the Zwicky Transient Facility (ZTF) to test its transfer learning abilities.

Abstract: Mixed damping is a physical effect that occurs when a heavy species is coupled to a relativistic fluid which is itself free streaming. As a cross-case between collisional damping and free-streaming, it is crucial in the context of neutrino-dark matter interactions. In this work, we establish the parameter space relevant for mixed damping, and we derive an analytical approximation for the evolution of dark matter perturbations in the mixed damping regime to illustrate the physical processes responsible for the suppression of cosmological perturbations. Although extended Boltzmann codes implementing neutrino-dark matter scattering terms automatically include mixed damping, this effect has not been systematically studied. In order to obtain reliable numerical results, it is mandatory to reconsider several aspects of neutrino-dark matter interactions, such as the initial conditions, the ultra-relativistic fluid approximation and high order multiple moments in the neutrino distribution. Such a precise treatment ensures the correct assessment of the relevance of mixed damping in neutrino-dark matter interactions.

Abstract: Oscillation studies play a central role in elucidating at least some aspects of the flavor problem. Here we examine the status of the predictions of a lepton quarticity flavor theory of neutrino oscillations against the existing global sample of oscillation data. By performing quantitative simulations we also determine the potential of the upcoming DUNE experiment in narrowing down the currently ill-measured oscillation parameters theta(23) and delta(CP). We present the expected improved sensitivity on these parameters for different assumptions.

Abstract: In pure-glue QCD, gluon-gluon scattering in the J(PC) = 0(-+) channel is described by a very simple equation, especially if one considers just the leading contribution to the scattering kernel. Of all components in this kernel, only the three-gluon vertex, V-mu nu rho, is poorly constrained by contemporary analyses; hence, calculations of 0(-+) glueball properties serve as a clear window onto the character and form of V-mu nu rho. This is important given that many modern calculations of V-mu nu rho predict the appearance of an infrared suppression in the scalar function which comes to modulate the bare vertex after the nonperturbative resummation of interactions. Such behaviour is a peculiar prediction; but we find that the suppression is essential if one is to achieve agreement with lattice-QCD predictions for the 0(-+) glueball mass. Hence, it is likely that this novel feature of V-mu nu rho is real and has observable implications for the spectrum, decays and interactions of all QCD bound-states.