C. Melachrinos

As the LHC luminosity is ramped up to 3 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$\,\times 10^{34}~{\hbox {cm}}^{2}~s^{1}$</tex></formula> and beyond, the high rates, multiplicities, and energies of particles seen by the detectors will pose a unique challenge. Only a tiny fraction of the produced collisions can be stored offline and immense real-time data reduction is needed. An effective trigger system must maintain high trigger efficiencies for the physics we are most interested in while suppressing the enormous QCD backgrounds. This requires massive computing power to minimize the online execution time of complex algorithms. A multi-level trigger is an effective solution to meet this challenge. The Fast Tracker (FTK) is an upgrade to the current ATLAS trigger system that will operate at full Level-1 output rates and provide high-quality tracks reconstructed over the entire inner detector by the start of processing in the Level-2 Trigger. FTK solves the combinatorial challenge inherent to tracking by exploiting the massive parallelism of associative memories that can compare inner detector hits to millions of pre-calculated patterns simultaneously. The tracking problem within matched patterns is further simplified by using pre-computed linearized fitting constants and relying on fast DSPs in modern commercial FPGAs. Overall, FTK is able to compute the helix parameters for all tracks in an event and apply quality cuts in less than 100 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$\mu{\hbox {s}}$</tex> </formula> . The system design is defined and the performance presented with respect to high transverse momentum (high- <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$p_{\rm T}$</tex></formula> ) Level-2 objects: b jets, tau jets, and isolated leptons. We test FTK algorithms using the full ATLAS simulation with WH events up to <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$3\times 10^{34}~{\hbox {cm}}^{2}{\hbox {s}}^{1}$</tex></formula> luminosity and compare the FTK results with the offline tracking capability. We present the architecture and the reconstruction performance for the mentioned high- <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex Notation="TeX">$p_{\rm T}$</tex></formula> Level-2 objects.

Presented in this paper is a detailed and direct comparison of the LIGO and Virgo binary neutron star detection pipelines. In order to test the search programs, numerous inspiral signals were added to 24 hours of simulated detector data. The efficiencies of the different pipelines were tested, and found to be comparable. Parameter estimation routines were also tested. We demonstrate that there are definite benefits to be had if LIGO and Virgo conduct a joint coincident analysis; these advantages include increased detection efficiency and the providing of source sky location information.

The search procedure for burst gravitational waves has been studied using 24 hours of simulated data in a network of three interferometers (Hanford 4-km, Livingston 4-km and Virgo 3-km are the example interferometers). Several methods to detect burst events developed in the LIGO Scientific Collaboration (LSC) and Virgo collaboration have been studied and compared. We have performed coincidence analysis of the triggers obtained in the different interferometers with and without simulated signals added to the data. The benefits of having multiple interferometers of similar sensitivity are demonstrated by comparing the detection performance of the joint coincidence analysis with LSC and Virgo only burst searches. Adding Virgo to the LIGO detector network can increase by 50% the detection efficiency for this search. Another advantage of a joint LIGO-Virgo network is the ability to reconstruct the source sky position. The reconstruction accuracy depends on the timing measurement accuracy of the events in each interferometer, and is displayed in this paper with a fixed source position example.

Dendritic morphology is a key determinant of how individual neurons acquire a unique signal processing profile. The highly branched dendritic structure that originates from the cell body, explores the surrounding 3D space in a fractal-like manner, until it reaches a certain amount of complexity. Its shape undergoes significant alterations under various physiological or neuropathological conditions. Yet, despite the profound effect that these alterations can have on neuronal function, the causal relationship between the two remains largely elusive. The lack of a systematic approach for remodeling neural cells and their dendritic trees is a key limitation that contributes to this problem. Such causal relationships can be inferred via the use of large-scale neuronal models whereby the anatomical plasticity of neurons is accounted for, in order to enhance their biological relevance and hence their predictive performance. To facilitate this effort, we developed a computational tool named REMOD that allows the structural remodeling of any type of virtual neuron. REMOD is written in Python and can be accessed through a dedicated web interface that guides the user through various options to manipulate selected neuronal morphologies. REMOD can also be used to extract meaningful morphology statistics for one or multiple reconstructions, including features such as sholl analysis, total dendritic length and area, path length to the soma, centrifugal branch order, diameter tapering and more. As such, the tool can be used both for the analysis and/or the remodeling of neuronal morphologies of any type.

We describe the design and expected performance of a the Fast Tracker Trigger (FTK) system for the ATLAS detector at the Large Hadron Collider. The FTK is a highly parallel hardware system designed to operate at the Level 1 trigger output rate. It is designed to provide global tracks reconstructed in the inner detector with resolution comparable to the full offline reconstruction as input of the Level 2 trigger processing. The hardware system is based on associative memories for pattern recognition and fast FPGAs for track reconstruction. The FTK is expected to dramatically improve the performance of track based isolation and b-tagging with little to no dependencies of pile-up interactions.