57 research outputs found

    Inclusive cross section and double helicity asymmetry for \pi^0 production in p+p collisions at sqrt(s)=200 GeV: Implications for the polarized gluon distribution in the proton

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    The PHENIX experiment presents results from the RHIC 2005 run with polarized proton collisions at sqrt(s)=200 GeV, for inclusive \pi^0 production at mid-rapidity. Unpolarized cross section results are given for transverse momenta p_T=0.5 to 20 GeV/c, extending the range of published data to both lower and higher p_T. The cross section is described well for p_T < 1 GeV/c by an exponential in p_T, and, for p_T > 2 GeV/c, by perturbative QCD. Double helicity asymmetries A_LL are presented based on a factor of five improvement in uncertainties as compared to previously published results, due to both an improved beam polarization of 50%, and to higher integrated luminosity. These measurements are sensitive to the gluon polarization in the proton, and exclude maximal values for the gluon polarization.Comment: 375 authors, 7 pages, 3 figures. Submitted to Phys. Rev. D, Rapid Communications. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.htm

    System Size and Energy Dependence of Jet-Induced Hadron Pair Correlation Shapes in Cu+Cu and Au+Au Collisions at sqrt(s_NN) = 200 and 62.4 GeV

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    We present azimuthal angle correlations of intermediate transverse momentum (1-4 GeV/c) hadrons from {dijets} in Cu+Cu and Au+Au collisions at sqrt(s_NN) = 62.4 and 200 GeV. The away-side dijet induced azimuthal correlation is broadened, non-Gaussian, and peaked away from \Delta\phi=\pi in central and semi-central collisions in all the systems. The broadening and peak location are found to depend upon the number of participants in the collision, but not on the collision energy or beam nuclei. These results are consistent with sound or shock wave models, but pose challenges to Cherenkov gluon radiation models.Comment: 464 authors from 60 institutions, 6 pages, 3 figures, 2 tables. Submitted to Physical Review Letters. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.htm

    Scaling properties of azimuthal anisotropy in Au+Au and Cu+Cu collisions at sqrt(s_NN) = 200 GeV

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    Detailed differential measurements of the elliptic flow for particles produced in Au+Au and Cu+Cu collisions at sqrt(s_NN) = 200 GeV are presented. Predictions from perfect fluid hydrodynamics for the scaling of the elliptic flow coefficient v_2 with eccentricity, system size and transverse energy are tested and validated. For transverse kinetic energies KE_T ~ m_T-m up to ~1 GeV, scaling compatible with the hydrodynamic expansion of a thermalized fluid is observed for all produced particles. For large values of KE_T, the mesons and baryons scale separately. A universal scaling for the flow of both mesons and baryons is observed for the full transverse kinetic energy range of the data when quark number scaling is employed. In both cases the scaling is more pronounced in terms of KE_T rather than transverse momentum.Comment: 422 authors from 58 institutions, 6 pages, 3 figures. Submitted to Physical Review Letters. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.htm

    Dynamic Edit Distance Table under a General Weighted Cost Function

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    Online Approximate Matching with Non-local Distances

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    Online Approximate Matching with Non-local Distances

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    A black box method was recently given that solves the problem of online approximate matching for a class of problems whose distance functions can be classified as being local. A distance function is said to be local if for a pattern P of length m and any substring T [i, i + m − 1] of a text T , the distance between P and T [i, i + m − 1] is equal to Σ_j ∆(P [j], T [i + j − 1]), where ∆ is any distance function between individual characters. We extend this line of work by showing how to tackle online approximate matching when the distance function is non-local. We give solutions which are applicable to a wide variety of matching problems including function and parameterised matching, swap matching, swap-mismatch, k-difference, k-difference with transpositions, overlap matching, edit distance/LCS, flipped bit, faulty bit and L1 and L2 rearrangement distances. The resulting unamortised online algorithms bound the worst case running time per input character to within a log factor of their comparable offline counterpart.A black box method was recently given that solves the problem of online approximate matching for a class of problems whose distance functions can be classified as being local. A distance function is said to be local if for a pattern P of length m and any substring T [i, i + m − 1] of a text T , the distance between P and T [i, i + m − 1] is equal to Σ_j ∆(P [j], T [i + j − 1]), where ∆ is any distance function between individual characters. We extend this line of work by showing how to tackle online approximate matching when the distance function is non-local. We give solutions which are applicable to a wide variety of matching problems including function and parameterised matching, swap matching, swap-mismatch, k-difference, k-difference with transpositions, overlap matching, edit distance/LCS, flipped bit, faulty bit and L1 and L2 rearrangement distances. The resulting unamortised online algorithms bound the worst case running time per input character to within a log factor of their comparable offline counterpart

    Efficient Algorithms for Finding A Longest Common Increasing Subsequence

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    We study the problem of finding a longest common increasing subsequence (LCIS) of multiple sequences of numbers. The LCIS problem is a fundamental issue in various application areas, including the whole genome alignment. In this paper we give an efficient algorithm to find the LCIS of two sequences in O(min(r log ℓ, nℓ+r) log log n+Sort(n)) time where n is the length of each sequence and r is the number of ordered pairs of positions at which the two sequences match, ℓ is the length of the LCIS, and Sort(n) is the time to sort n numbers. For m sequences where m ≥ 3, we find the LCIS in O(min(mr 2, r log ℓ log m r)+m·Sort(n)) time where r is the total number of m-tuples of positions at which the m sequences match. The previous results find the LCIS of two sequences in O(n 2) and O(nℓ log log n+Sort(n)) time. Our algorithm is faster when r is relatively small, e.g., for r &lt; min(n 2 /(log ℓ log log n), nℓ / log ℓ).
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