Top quark physics at CDF

We present the recent results of top-quark physics using up to 6 fb$^{-1}$ of $p\bar{p}$ collisions analyzed by the CDF collaboration. The large number of top quark events analyzed, of the order of several thousands, allows stringent checks of the standard model predictions. Also, the top quark is widely believed to be a window to new physics. We present the latest measurements of top quark intrinsic properties as well as direct searches for new physics in the top sector.Comment: PoS(EPS-HEP2011)35

The upgraded Pixel detector and the commissioning of the Inner Detector tracking of the ATLAS experiment for Run-2 at the Large Hadron Collider

Run-2 of the Large Hadron Collider (LHC) will provide new challenges to track and vertex reconstruction with higher energies, denser jets and higher rates. Therefore the ATLAS experiment has constructed the first 4-layer Pixel Detector in HEP, installing a new pixel layer, also called Insertable B-Layer (IBL). The IBL is a fourth layer of pixel detectors, and has been installed in May 2014 at a radius of 3.3 cm between the existing Pixel Detector and a new smaller radius beam-pipe. The new detector, built to cope with the high radiation and expected occupancy, is the first large scale application of 3D sensors and CMOS 130~nm readout electronics. In addition, the Pixel Detector was improved with a new service quarter panel to recover about 3\% of defective modules lost during Run-1 and a new optical readout system to readout the data at higher speed while reducing the occupancy when running with increased luminosity. Complementing detector improvements, many improvements to Inner Detector track and vertex reconstruction were developed during the two-year shutdown of the LHC. These include novel techniques developed to improve the performance in the dense cores of jets, optimisation for the expected conditions, and a software campaign which lead to a factor of three decrease in the CPU time needed to process each recorded event.Comment: 15 pages, EPS-HEP 2015 Proceeding

Search for the Standard Model Higgs boson in final states with bb quarks at the Tevatron

We present the result of searches for a low mass Standard Model Higgs boson produced in association with a $W$ or a $Z$ boson at a center-of-mass energy of $\sqrt{s}=$1.96 TeV with the CDF and D0 detectors at the Fermilab Tevatron collider. The search is performed in events containing one or two $b$ tagged jets in association with either two leptons, or one lepton and an imbalance in transverse energy, or simply a large imbalance in transverse energy. Datasets corresponding to up to 8.5 fb$^{-1}$ of integrated luminosity are considered in the analyses. These are the most powerful channels in the search for a low mass Higgs boson at the Tevatron. Recent sensitivity improvements are discussed. For a Higgs mass of 115 \gevcc, the expected sensitivity for the most sensitive individual analyses reaches 2.3 times the SM prediction at 95% confidence level (C.L.), with all limits below 5 times the SM. Additionally, a $WZ/ZZ$ cross-section measurement is performed to validate the analysis techniques deployed for searching for the Higgs

Combination of Standard Model Higgs searches at CDF

We present the latest combination of searches for a standard model (SM) Higgs boson in ppbar collisions at \sqrts= 1.96 TeV recorded by the CDF~II detector at the Fermilab Tevatron. Using data corresponding to 2.3-5.9 fb-1 of integrated luminosity, we perform searches in a number of different production and decay modes and then combine them to improve sensitivity. No excess in data above that expected from backgrounds is observed; therefore, we set upper limits on the production cross section times branching fraction as a function of the SM Higgs boson mass (mH). The combined observed (expected) limit is 1.9 (1.8) times the SM prediction at mH = 115 Gev/c^2 and 1.0 (1.1) times the SM prediction at mH = 165 GeV/c^2.Comment: ICHEP 2010 Conference proceeding, 4 page

Structural Attention Neural Networks for improved sentiment analysis

We introduce a tree-structured attention neural network for sentences and small phrases and apply it to the problem of sentiment classification. Our model expands the current recursive models by incorporating structural information around a node of a syntactic tree using both bottom-up and top-down information propagation. Also, the model utilizes structural attention to identify the most salient representations during the construction of the syntactic tree. To our knowledge, the proposed models achieve state of the art performance on the Stanford Sentiment Treebank dataset.Comment: Submitted to EACL2017 for revie

SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression

Neural sequence-to-sequence models are currently the dominant approach in several natural language processing tasks, but require large parallel corpora. We present a sequence-to-sequence-to-sequence autoencoder (SEQ^3), consisting of two chained encoder-decoder pairs, with words used as a sequence of discrete latent variables. We apply the proposed model to unsupervised abstractive sentence compression, where the first and last sequences are the input and reconstructed sentences, respectively, while the middle sequence is the compressed sentence. Constraining the length of the latent word sequences forces the model to distill important information from the input. A pretrained language model, acting as a prior over the latent sequences, encourages the compressed sentences to be human-readable. Continuous relaxations enable us to sample from categorical distributions, allowing gradient-based optimization, unlike alternatives that rely on reinforcement learning. The proposed model does not require parallel text-summary pairs, achieving promising results in unsupervised sentence compression on benchmark datasets.Comment: Accepted to NAACL 201