Direct jet reconstruction in p + p and Cu + Cu at PHENIX

The Relativistic Heavy Ion Collider collides heavy nuclei at ultrarelativistic energies, creating a strongly interacting, partonic medium that is opaque to the passage of high energy quarks and gluons. Direct jet reconstruction applied to these collision systems provides a crucial constraint on the mechanism for in-medium parton energy loss and jet-medium interactions. However, traditional jet reconstruction algorithm operating in the large soft background at RHIC give rise to fake jets well above the intrinsic production rate of high-pT partons, impeding the detection of the low cross section jet signal at RHIC energies. We developed a new jet reconstruction algorithm that uses a Gaussian filter to locate and reconstruct the jet energy. This algorithm is combined with a fake jet rejection scheme that provides efficient jet reconstruction with acceptable fake rate in a background environment up to the central Au + Au collision at sqrt(s_NN) = 200 GeV. We present results of its application in p + p and Cu + Cu collisions using data from the PHENIX detector, namely p + p cross section, Cu + Cu jet yields, the Cu + Cu nuclear modification factor, and Cu + Cu jet-jet azimuthal correlation.Comment: To be published in the proceedings of DPF-2009, Detroit, MI, July 2009, eConf C09072

Direct Jet Reconstruction in Proton-Proton and Copper-Copper Collisions at √sNN = 200 GeV

Collision of heavy nuclei at the Relativistic Heavy Ion Collider (RHIC) recreates the state of high temperature quark-gluon plasma that existed shortly after the Big Bang. Measurement using single particle spectra and two-particle correlation shows that this medium is largely opaque to the transit of a high energy quark or gluon. Reconstructing the kinematics of these quarks and gluons can provide additional constraints for the property of their interaction with the medium. While the direct reconstruction of quantum chromodynamics jets, the final state showers of quarks and gluons, has become an indispensable tool at hadron and electron accelerator experiments, the application of this technique to heavy ion collisions at the RHIC energy has been considered a hard problem. The relatively low yield of high transverse momentum jets would have to be detected within a large, fluctuating background that can give rise to a false jet signal. At the RHIC PHENIX experiment, jet reconstruction also has to cope with the limited aperture of the central arm spectrometers. To overcome both problems, which can distort the jet signal in the traditional reconstruction algorithms, this thesis develops an algorithm that reconstructs the jets as maxima of the Gaussian filtered event transverse momentum distribution. The Gaussian angular weighting causes the algorithm to become more sensitive to the jet core versus the jet periphery. It is then combined with a fake jet rejection discriminant to remove the background fluctuation from the jet signal. This algorithm is used to obtain the first jet measurement in heavy ion environment at PHENIX, using data from the 2004/2005 RHIC run. The result includes the proton-proton inclusive jet spectrum, the proton-proton fragmentation function, the copper-copper jet nuclear modification factor, the copper-copper jet central-to-peripheral modification factor, and the copper-copper dijet azimuthal correlation. The measured copper-copper jet nuclear modification factor shows that there is a significant initial state effect to the jet suppression. The observation of no broadening in the copper-copper dijet azimuthal correlation indicates that the traditional energy loss picture via multiple soft scattering may not be applicable to the quark-gluon plasma

Explainable machine learning of the underlying physics of high-energy particle collisions

We present an implementation of an explainable and physics-aware machine learning model capable of inferring the underlying physics of high-energy particle collisions using the information encoded in the energy-momentum four-vectors of the final state particles. We demonstrate the proof-of-concept of our White Box AI approach using a Generative Adversarial Network (GAN) which learns from a DGLAP-based parton shower Monte Carlo event generator. We show, for the first time, that our approach leads to a network that is able to learn not only the final distribution of particles, but also the underlying parton branching mechanism, i.e. the Altarelli-Parisi splitting function, the ordering variable of the shower, and the scaling behavior. While the current work is focused on perturbative physics of the parton shower, we foresee a broad range of applications of our framework to areas that are currently difficult to address from first principles in QCD. Examples include nonperturbative and collective effects, factorization breaking and the modification of the parton shower in heavy-ion, and electron-nucleus collisions.Comment: 11 pages, 4 figure

Algebraic Bethe ansatz for the supersymmetric t−Jt-J model with reflecting boundary conditions

In the framework of the graded quantum inverse scattering method (QISM), we obtain the eigenvalues and eigenvectors of the supersymmetric $t-J$ model with reflecting boundary conditions in FFB background. The corresponding Bethe ansatz equations are obtained.Comment: Latex file, 23 Page

Widely targeted metabolomics analysis of Sanghuangporus vaninii mycelia and fruiting bodies at different harvest stages

Sanghuangprous vaninii is a medicinal macrofungus cultivated extensively in China. Both the mycelia and fruiting bodies of S. vaninii have remarkable therapeutic properties, but it remains unclear whether the mycelia may serve as a substitute for the fruiting bodies. Furthermore, S. vaninii is a perennial fungus with therapeutic components that vary significantly depending on the growing year of the fruiting bodies. Hence, it is critical to select an appropriate harvest stage for S. vaninii fruiting bodies for a specific purpose. With the aid of Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), metabolomics based on ultra-high performance liquid chromatography coupled to triple quadrupole mass spectrometry (UHPLC-QQQ-MS) was used to preliminarily determine 81 key active metabolites and 157 active pharmaceutical metabolites in S. vaninii responsible for resistance to the six major diseases. To evaluate the substitutability of the mycelia and fruiting bodies of S. vaninii and to select an appropriate harvest stage for the fruiting bodies of S. vaninii, we analyzed the metabolite differences, especially active metabolite differences, among the mycelia and fruiting bodies during three different harvest stages (1-year-old, 2-year-old, and 3-year-old). Moreover, we also determined the most prominent and crucial metabolites in each sample of S. vaninii. These results suggested that the mycelia show promise as a substitute for the fruiting bodies of S. vaninii and that extending the growth year does not necessarily lead to higher accumulation levels of active metabolites in the S. vaninii fruiting bodies. This study provided a theoretical basis for developing and using S. vaninii

Reconstructed Jets at RHIC

To precisely measure jets over a large background such as pile up in high luminosity p+p collisions at LHC, a new generation of jet reconstruction algorithms is developed. These algorithms are also applicable to reconstruct jets in the heavy ion environment where large event multiplicities are produced. Energy loss in the medium created in heavy ion collisions are already observed indirectly via inclusive hadron distributions and di-hadron correlations. Jets can be used to study this energy loss in detail with reduced biases. We review the latest results on jet-medium interactions as seen in A+A collisions at RHIC, focusing on the recent progress on jet reconstruction in heavy ion collisions.Comment: Proceedings for the 26th Winter Workshop on Nuclear Dynamic