Abstract: The ATLAS experiment is constructing new all-silicon inner tracking system for HL-LHC. The strip detectors cover the radial extent of 40 to 100 cm. A new approach is adopted to use p-type silicon material, making the readout in n+-strips, so-called n+-in-p sensors. This allows for enhanced radiation tolerance against an order of magnitude higher particle fluence compared to the LHC. To cope with varying hit rates and occupancies as a function of radial distance, there are two barrel sensor types, the short strips (SS) for the inner 2 and the long strips (LS) for the outer 2 barrel cylinders, respectively. The barrel sensors exhibit a square, 9.8 x 9.8 cm2, geometry, the largest possible sensor area from a 6-inch wafer. The strips are laid out in parallel with a strip pitch of 75.5 μm and 4 or 2 rows of strip segments. The strips are AC-coupled and biased via polysilicon resistors. The endcap sensors employ a “stereo-annulus” geometry exhibiting a skewed-trapezoid shapes with circular edges. They are designed in 6 unique shapes, R0 to R5, corresponding to progressively increasing radial extents and which allows them to fit within the petal geometry and the 6-inch wafer maximally. The strips are in fan-out geometry with an in-built rotation angle, with a mean pitch of approximately 75 μm and 4 or 2 rows of strip segments. The eight sensor types are labeled as ATLAS18xx where xx stands for SS, LS, and R0 to R5. According to the mechanical and electrical specifications, CAD files for wafer processing were laid out, following the successful designs of prototype barrel and endcap sensors, together with a number of optimizations. A pre-production was carried out prior to the full production of the wafers. The quality of the sensors is reviewed and judged excellent through the test results carried out by vendor. These sensors are used for establishing acceptance procedures and to evaluate their performance in the ATLAS collaboration, and subsequently for pre-production of strip modules and stave and petal structures.

Abstract: NEXT-100 experiment aims at searching the neutrinoless double-beta decay of the Xe-136 isotope using a TPC filled with a 100 kg of high-pressure gaseous xenon, with 90% isotopic enrichment. The experiment will take place at the Laboratorio Subterraneo de Canfranc (LSC), Spain. NEXT-100 uses electroluminescence (EL) technology for energy measurement with a resolution better than 1% FWHM. The gaseous xenon in the TPC additionally allows the tracks of the two beta particles to be recorded, which are expected to have a length of up to 30 cm at 10 bar pressure. The ability to record the topological signature of the beta beta 0 nu events provides a powerful background rejection factor for the beta beta experiment. In this paper, we present a novel 3D imaging concept using SiPMs coated with tetraphenyl butadiene (TPB) for the EL read out and its first implementation in NEXT-DEMO, a large-scale prototype of the NEXT-100 experiment. The design and the first characterization measurements of the NEXT-DEMO SiPM tracking system are presented. The SiPM response uniformity over the tracking plane drawn from its gain map is shown to be better than 4%. An automated active control system for the stabilization of the SiPMs gain was developed, based on the voltage supply compensation of the gain drifts. The gain is shown to be stabilized within 0.2% relative variation around its nominal value, provided by Hamamatsu, in a temperature range of 10 degrees C. The noise level from the electronics and the SiPM dark noise is shown to lay typically below the level of 10 photoelectrons (pe) in the ADC. Hence, a detection threshold at 10 pe is set for the acquisition of the tracking signals. The ADC full dynamic range (4096 channels) is shown to be adequate for signal levels of up to 200 pe/mu s, which enables recording most of the tracking signals.

Abstract: For the High-Luminosity upgrade of the Large Hadron Collider an increased instantaneous luminosity of up to 7.5 . 10(34) cm(-2) s(-1), leading to a total integrated luminosity of up to 3000 fb(-1), is foreseen. The current silicon and transition radiation tracking detectors of the ATLAS experiment will be unable to cope with the increased track densities and radiation levels, and will need to be replaced. The new tracking detector will consist entirely of silicon pixel and strip detectors. In this paper, results on the development and tests of prototype components for the new silicon strip detector in the forward regions (end-caps) of the ATLAS detector are presented. Flex-printed readout boards with fast readout chips, referred to as hybrids, and silicon detector modules are investigated. The modules consist of a hybrid glued onto a silicon strip sensor. The channels on both are connected via wire-bonds for readout and powering. Measurements of important performance parameters and a comparison of two possible readout schemes are presented. In addition, the assembly procedure is described and recommendations for further prototyping are derived.

Abstract: Active pixels sensors based on the DEPFET technology will be used for the innermost vertex detector of the future Belle-II experiment. The increased luminosity of the e(+) e(-) SuperKEKB collider entails challenging detector requirements, namely: low material budget, low power consumption, high precision and efficiency, and a large readout rate. The DEPFET active pixel technology has shown to be a suitable solution for this purpose. A review of the different aspects of the detector design (sensors, readout ASICS and supplementary infrastructure) and the results of the latest thinned sensor prototypes (50 μm) are described.

Abstract: A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.