Indian Institute of Technology Bombay

About: Indian Institute of Technology Bombay is a education organization based out in Mumbai, India. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 16756 authors who have published 33588 publications receiving 570559 citations.

Measurement of charm production at central rapidity in proton-proton collisions at √s = 2.76 TeV

Abstract: The p t-differential production cross sections of the prompt (B feed-down subtracted) charmed mesons D0, D+, and D*+ in the rapidity range |y| < 0.5, and for transverse momentum 1 < p t < 12 GeV/c, were measured in proton-proton collisions at $ \sqrt {s} = 2.76\;{\text{TeV}} $ with the ALICE detector at the Large Hadron Collider. The analysis exploited the hadronic decays D0 → K−π+, D+ → K−π+π+, D*+ → D0π+, and their charge conjugates, and was performed on a $ {\mathcal{L}_{{{\rm int} }}} = 1.1\;{\text{n}}{{\text{b}}^{{ - 1}}} $ event sample collected in 2011 with a minimum-bias trigger. The total charm production cross section at $ \sqrt {s} = 2.76\;{\text{TeV}} $ and at 7 TeV was evaluated by extrapolating to the full phase space the p t-differential production cross sections at $ \sqrt {s} = 2.76\;{\text{TeV}} $ and our previous measurements at $ \sqrt {s} = 7\;{\text{TeV}} $ . The results were compared to existing measurements and to perturbative-QCD calculations. The fraction of $ {\text{c}}\overline {\text{d}} $ D mesons produced in a vector state was also determined.

Anisotropic Flow of Charged Particles in Pb-Pb Collisions at √sNN=5.02 TeV

Abstract: We report the first results of elliptic (v2), triangular (v3), and quadrangular (v4) flow of charged particles in Pb-Pb collisions at a center-of-mass energy per nucleon pair of √sNN=5.02 TeV with the ALICE detector at the CERN Large Hadron Collider. The measurements are performed in the central pseudorapidity region |η|<0.8 and for the transverse momentum range 0.2

Forward neutral pion production in p+p and d+Au collisions at root(S)(NN)=200 GeV

Abstract: Measurements of the production of forward pi(0) mesons from p+p and d+Au collisions at root s(NN) = 200 GeV are reported. The p+p yield generally agrees with next-to-leading order perturbative QCD calculations. The d+Au yield per binary collision is suppressed as eta increases, decreasing to similar to 30% of the p+p yield at =4.00, well below shadowing expectations. Exploratory measurements of azimuthal correlations of the forward pi(0) with charged hadrons at eta approximate to 0 show a recoil peak in p+p that is suppressed in d+Au at low pion energy. These observations are qualitatively consistent with a saturation picture of the low-x gluon structure of heavy nuclei.

Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades

Abstract: Creating datasets for Neuromorphic Vision is a challenging task. A lack of available recordings from Neuromorphic Vision sensors means that data must typically be recorded specifically for dataset creation rather than collecting and labelling existing data. The task is further complicated by a desire to simultaneously provide traditional frame-based recordings to allow for direct comparison with traditional Computer Vision algorithms. Here we propose a method for converting existing Computer Vision static image datasets into Neuromorphic Vision datasets using an actuated pan-tilt camera platform. Moving the sensor rather than the scene or image is a more biologically realistic approach to sensing and eliminates timing artifacts introduced by monitor updates when simulating motion on a computer monitor. We present conversion of two popular image datasets (MNIST and Caltech101) which have played important roles in the development of Computer Vision, and we provide performance metrics on these datasets using spike-based recognition algorithms. This work contributes datasets for future use in the field, as well as results from spike-based algorithms against which future works can compare. Furthermore, by converting datasets already popular in Computer Vision, we enable more direct comparison with frame-based approaches.