Bounds on Cubic Lorentz-Violating Terms in the Fermionic Dispersion Relation

We study the recently proposed Lorentz-violating dispersion relation for fermions and show that it leads to two distinct cubic operators in the momentum. We compute the leading order terms that modify the non-relativistic equations of motion and use experimental results for the hyperfine transition in the ground state of the ${}^9\textrm Be^+$ ion to bound the values of the Lorentz-violating parameters $\eta_1$ and $\eta_2$ for neutrons. The resulting bounds depend on the value of the Lorenz-violating background four-vector in the laboratory frame.Comment: Revtex 4, four pages. Version to match the one to appear in Physical Review

Axial and Vector Correlator Mixing in Hot and Dense Hadronic Matter

We study the manifestations of chiral symmetry restoration which have a significance for the parity mixing. Restricting to pions and nucleons we establish a formalism for the expression of the vector correlator, which displays the mixing of the axial correlator into the vector one and unifies the cases of the heat bath and the dense medium. We give examples of mixing cross-sections. We also establish a link between the energy integrated mixing cross-sections and the pion scalar density which governs the quenching factors of coupling constants, such as the pion decay one, as well as the quark condensate evolution.Comment: 12 pages, Latex, 4 PostScript Figure

A nature-inspired feature selection approach based on hypercomplex information

Feature selection for a given model can be transformed into an optimization task. The essential idea behind it is to find the most suitable subset of features according to some criterion. Nature-inspired optimization can mitigate this problem by producing compelling yet straightforward solutions when dealing with complicated fitness functions. Additionally, new mathematical representations, such as quaternions and octonions, are being used to handle higher-dimensional spaces. In this context, we are introducing a meta-heuristic optimization framework in a hypercomplex-based feature selection, where hypercomplex numbers are mapped to real-valued solutions and then transferred onto a boolean hypercube by a sigmoid function. The intended hypercomplex feature selection is tested for several meta-heuristic algorithms and hypercomplex representations, achieving results comparable to some state-of-the-art approaches. The good results achieved by the proposed approach make it a promising tool amongst feature selection research

Handling dropout probability estimation in convolution neural networks using meta-heuristics

Deep learning-based approaches have been paramount in recent years, mainly due to their outstanding results in several application domains, ranging from face and object recognition to handwritten digit identification. Convolutional Neural Networks (CNN) have attracted a considerable attention since they model the intrinsic and complex brain working mechanisms. However, one main shortcoming of such models concerns their overfitting problem, which prevents the network from predicting unseen data effectively. In this paper, we address this problem by means of properly selecting a regularization parameter known as Dropout in the context of CNNs using meta-heuristic-driven techniques. As far as we know, this is the first attempt to tackle this issue using this methodology. Additionally, we also take into account a default dropout parameter and a dropout-less CNN for comparison purposes. The results revealed that optimizing Dropout-based CNNs is worthwhile, mainly due to the easiness in finding suitable dropout probability values, without needing to set new parameters empirically

Electrical Characterization of a Thin Edgeless N-on-p Planar Pixel Sensors For ATLAS Upgrades

In view of the LHC upgrade phases towards the High Luminosity LHC (HL-LHC), the ATLAS experiment plans to upgrade the Inner Detector with an all-silicon system. Because of its radiation hardness and cost effectiveness, the n-on-p silicon technology is a promising candidate for a large area pixel detector. The paper reports on the joint development, by LPNHE and FBK of novel n-on-p edgeless planar pixel sensors, making use of the active trench concept for the reduction of the dead area at the periphery of the device. After discussing the sensor technology, and presenting some sensors' simulation results, a complete overview of the electrical characterization of the produced devices will be given.Comment: 9 pages, 9 figures, to appear in the proceedings of the 15th International Workshops on Radiation Imaging Detector

Performance of Irradiated Thin Edgeless N-on-P Planar Pixel Sensors for ATLAS Upgrades

In view of the LHC upgrade phases towards the High Luminosity LHC (HL-LHC), the ATLAS experiment plans to upgrade the Inner Detector with an all-silicon system. Because of its radiation hardness and cost effectiveness, the n-on-p silicon technology is a promising candidate for a large area pixel detector. The paper reports on the joint development, by LPNHE and FBK of novel n-on-p edgeless planar pixel sensors, making use of the active trench concept for the reduction of the dead area at the periphery of the device. After discussing the sensor technology, a complete overview of the electrical characterization of several irradiated samples will be discussed. Some comments about detector modules being assembled will be made and eventually some plans will be outlined.Comment: 6 pages, 13 figures, to appear in the proceedings of the 2013 Nuclear Science Symposium and Medical Imaging Conference. arXiv admin note: text overlap with arXiv:1311.162

Development of Edgeless n-on-p Planar Pixel Sensors for future ATLAS Upgrades

The development of n-on-p "edgeless" planar pixel sensors being fabricated at FBK (Trento, Italy), aimed at the upgrade of the ATLAS Inner Detector for the High Luminosity phase of the Large Hadron Collider (HL-LHC), is reported. A characterizing feature of the devices is the reduced dead area at the edge, achieved by adopting the "active edge" technology, based on a deep etched trench, suitably doped to make an ohmic contact to the substrate. The project is presented, along with the active edge process, the sensor design for this first n-on-p production and a selection of simulation results, including the expected charge collection efficiency after radiation fluence of $1 \times 10^{15} {\rm n_{eq}}/{\rm cm}^2$ comparable to those expected at HL-LHC (about ten years of running, with an integrated luminosity of 3000 fb$^{-1}$) for the outer pixel layers. We show that, after irradiation and at a bias voltage of 500 V, more than 50% of the signal should be collected in the edge region; this confirms the validity of the active edge approach.Comment: 20 pages, 9 figures, submitted to Nucl. Instr. and Meth.

Novel Silicon n-on-p Edgeless Planar Pixel Sensors for the ATLAS upgrade

In view of the LHC upgrade phases towards HL-LHC, the ATLAS experiment plans to upgrade the Inner Detector with an all-silicon system. The n-on-p silicon technology is a promising candidate for the pixel upgrade thanks to its radiation hardness and cost effectiveness, that allow for enlarging the area instrumented with pixel detectors. We report on the development of novel n-in-p edgeless planar pixel sensors fabricated at FBK (Trento, Italy), making use of the "active edge" concept for the reduction of the dead area at the periphery of the device. After discussing the sensor technology and fabrication process, we present device simulations (pre- and post-irradiation) performed for different sensor configurations. First preliminary results obtained with the test-structures of the production are shown.Comment: 6 pages, 5 figures, to appear in the proceedings of the 9th International Conference on Radiation Effects on Semiconductor Materials Detectors and Device