GNAM and OHP: Monitoring Tools for ATLAS experiment at LHC.

ATLAS is one of the four experiments under construction along the Large Hadron Collider (LHC) ring at CERN. The LHC will produce interactions at a center-of-mass energy equal to âs = 14 TeV at 40 MHz rate. The detector consists of more than 140 million electronic channels. The challenging experimental environment and the extreme detector complexity impose the necessity of a common scalable distributed monitoring framework, which can be tuned for the optimal use by different ATLAS sub-detectors at the various levels of the ATLAS data flow. This note presents two monitoring tools that have been developed for this aim within the architecture ATLAS Monitoring Framework and the Data Acquisition System: GNAM and OHP. The first one is a framework for online histogram production; the second one is graphical application for histogram presentation. This tools are now widely used during the ATLAS commissioning and their performances are reported in this not

The IBMAP approach for Markov networks structure learning

In this work we consider the problem of learning the structure of Markov networks from data. We present an approach for tackling this problem called IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC algorithm, designed for avoiding important limitations of existing independence-based algorithms. These algorithms proceed by performing statistical independence tests on data, trusting completely the outcome of each test. In practice tests may be incorrect, resulting in potential cascading errors and the consequent reduction in the quality of the structures learned. IBMAP contemplates this uncertainty in the outcome of the tests through a probabilistic maximum-a-posteriori approach. The approach is instantiated in the IBMAP-HC algorithm, a structure selection strategy that performs a polynomial heuristic local search in the space of possible structures. We present an extensive empirical evaluation on synthetic and real data, showing that our algorithm outperforms significantly the current independence-based algorithms, in terms of data efficiency and quality of learned structures, with equivalent computational complexities. We also show the performance of IBMAP-HC in a real-world application of knowledge discovery: EDAs, which are evolutionary algorithms that use structure learning on each generation for modeling the distribution of populations. The experiments show that when IBMAP-HC is used to learn the structure, EDAs improve the convergence to the optimum

Upper bounds for the eigenvalues of Hessian equations

We prove some upper bounds for the Dirichlet eigenvalues of a class of fully nonlinear elliptic equations, namely the Hessian equationsComment: 15 pages, 1 figur

Ageing test of the ATLAS RPCs at X5-GIF

An ageing test of three ATLAS production RPC stations is in course at X5-GIF, the CERN irradiation facility. The chamber efficiencies are monitored using cosmic rays triggered by a scintillator hodoscope. Higher statistics measurements are made when the X5 muon beam is available. We report here the measurements of the efficiency versus operating voltage at different source intensities, up to a maximum counting rate of about 700Hz/cm^2. We describe the performance of the chambers during the test up to an overall ageing of 4 ATLAS equivalent years corresponding to an integrated charge of 0.12C/cm^2, including a safety factor of 5.Comment: 4 pages. Presented at the VII Workshop on Resistive Plate Chambers and Related Detectors; Clermont-Ferrand October 20th-22nd, 200

System Test of the ATLAS Muon Spectrometer in the H8 Beam at the CERN SPS

An extensive system test of the ATLAS muon spectrometer has been performed in the H8 beam line at the CERN SPS during the last four years. This spectrometer will use pressurized Monitored Drift Tube (MDT) chambers and Cathode Strip Chambers (CSC) for precision tracking, Resistive Plate Chambers (RPCs) for triggering in the barrel and Thin Gap Chambers (TGCs) for triggering in the end-cap region. The test set-up emulates one projective tower of the barrel (six MDT chambers and six RPCs) and one end-cap octant (six MDT chambers, A CSC and three TGCs). The barrel and end-cap stands have also been equipped with optical alignment systems, aiming at a relative positioning of the precision chambers in each tower to 30-40 micrometers. In addition to the performance of the detectors and the alignment scheme, many other systems aspects of the ATLAS muon spectrometer have been tested and validated with this setup, such as the mechanical detector integration and installation, the detector control system, the data acquisition, high level trigger software and off-line event reconstruction. Measurements with muon energies ranging from 20 to 300 GeV have allowed measuring the trigger and tracking performance of this set-up, in a configuration very similar to the final spectrometer. A special bunched muon beam with 25 ns bunch spacing, emulating the LHC bunch structure, has been used to study the timing resolution and bunch identification performance of the trigger chambers. The ATLAS first-level trigger chain has been operated with muon trigger signals for the first time

A survey on independence-based Markov networks learning

This work reports the most relevant technical aspects in the problem of learning the \emph{Markov network structure} from data. Such problem has become increasingly important in machine learning, and many other application fields of machine learning. Markov networks, together with Bayesian networks, are probabilistic graphical models, a widely used formalism for handling probability distributions in intelligent systems. Learning graphical models from data have been extensively applied for the case of Bayesian networks, but for Markov networks learning it is not tractable in practice. However, this situation is changing with time, given the exponential growth of computers capacity, the plethora of available digital data, and the researching on new learning technologies. This work stresses on a technology called independence-based learning, which allows the learning of the independence structure of those networks from data in an efficient and sound manner, whenever the dataset is sufficiently large, and data is a representative sampling of the target distribution. In the analysis of such technology, this work surveys the current state-of-the-art algorithms for learning Markov networks structure, discussing its current limitations, and proposing a series of open problems where future works may produce some advances in the area in terms of quality and efficiency. The paper concludes by opening a discussion about how to develop a general formalism for improving the quality of the structures learned, when data is scarce.Comment: 35 pages, 1 figur

Complex Patterns of Chromosome 11 Aberrations in Myeloid Malignancies Target CBL, MLL, DDB1 and LMO2

Exome sequencing of primary tumors identifies complex somatic mutation patterns. Assignment of relevance of individual somatic mutations is difficult and poses the next challenge for interpretation of next generation sequencing data. Here we present an approach how exome sequencing in combination with SNP microarray data may identify targets of chromosomal aberrations in myeloid malignancies. The rationale of this approach is that hotspots of chromosomal aberrations might also harbor point mutations in the target genes of deletions, gains or uniparental disomies (UPDs). Chromosome 11 is a frequent target of lesions in myeloid malignancies. Therefore, we studied chromosome 11 in a total of 813 samples from 773 individual patients with different myeloid malignancies by SNP microarrays and complemented the data with exome sequencing in selected cases exhibiting chromosome 11 defects. We found gains, losses and UPDs of chromosome 11 in 52 of the 813 samples (6.4%). Chromosome 11q UPDs frequently associated with mutations of CBL. In one patient the 11qUPD amplified somatic mutations in both CBL and the DNA repair gene DDB1. A duplication within MLL exon 3 was detected in another patient with 11qUPD. We identified several common deleted regions (CDR) on chromosome 11. One of the CDRs associated with de novo acute myeloid leukemia (P=0.013). One patient with a deletion at the LMO2 locus harbored an additional point mutation on the other allele indicating that LMO2 might be a tumor suppressor frequently targeted by 11p deletions. Our chromosome-centered analysis indicates that chromosome 11 contains a number of tumor suppressor genes and that the role of this chromosome in myeloid malignancies is more complex than previously recognized

Study of Z Boson Pair Production in e+e- Collisions at LEP at \sqrt{s}=189 GeV

The pair production of Z bosons is studied using the data collected by the L3 detector at LEP in 1998 in e+e- collisions at a centre-of-mass energy of 189 GeV. All the visible final states are considered and the cross section of this process is measured to be 0.74 +0.15 -0.14 (stat.) +/- 0.04 (syst.) pb. Final states containing b quarks are enhanced by a dedicated selection and their production cross section is found to be 0.18 +0.09 -0.07 (stat.) +/- 0.02 (syst.) pb. Both results are in agreement with the Standard Model predictions. Limits on anomalous couplings between neutral gauge bosons are derived from these measurements

Study of Z Boson Pair Production in e^+e^- Interactions at \sqrt{s}=192 - 202 GeV

The cross section for the production of Z boson pairs is measured using the data collected by the L3 detector at LEP in 1999 in e^+e^- collisions at centre-of-mass energies ranging from 192 GeV up to 202 GeV. Events in all the visible final states are selected, measuring the cross section of this process. The special case of final states containing b quarks is also investigated. All results are in agreement with the Standard Model predictions