Probing Transverse Momentum Broadening via Dihadron and Hadron-jet Angular Correlations in Relativistic Heavy-ion Collisions

Dijet, dihadron, hadron-jet angular correlations have been reckoned as important probes of the transverse momentum broadening effects in relativistic nuclear collisions. When a pair of high-energy jets created in hard collisions traverse the quark-gluon plasma produced in heavy-ion collisions, they become de-correlated due to the vacuum soft gluon radiation associated with the Sudakov logarithms and the medium-induced transverse momentum broadening. For the first time, we employ the systematical resummation formalism and establish a baseline calculation to describe the dihadron and hadron-jet angular correlation data in $pp$ and peripheral $AA$ collisions where the medium effect is negligible. We demonstrate that the medium-induced broadening $\langle p_\perp^2\rangle$ and the so-called jet quenching parameter $\hat q$ can be extracted from the angular de-correlations observed in $AA$ collisions. A global $\chi^2$ analysis of dihadron and hadron-jet angular correlation data renders the best fit $\langle p_\perp^2 \rangle \sim 13~\textrm{GeV}^2$ for a quark jet at RHIC top energy. Further experimental and theoretical efforts along the direction of this work shall significantly advance the quantitative understanding of transverse momentum broadening and help us acquire unprecedented knowledge of jet quenching parameter in relativistic heavy-ion collisions.Comment: 6 pages, 3 figure

Medium effects on the selection of sequences folding into stable proteins in a simple model

We study the medium effects on the selection of sequences in protein folding by taking account of the surface potential in HP-model. Our analysis on the proportion of H and P monomers in the sequences gives a direct interpretation that the lowly designable structures possess small average gap. The numerical calculation by means of our model exhibits that the surface potential enhances the average gap of highly designable structures. It also shows that a most stable structure may be no longer the most stable one if the medium parameters changed.Comment: 4 pages, 4 figure

Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino Detector

We provide a fast approach incorporating the usage of deep learning for evaluating the effects of photon sensors in an antineutrino detector on the event reconstruction performance therein. This work is an attempt to harness the power of deep learning for detector designing and upgrade planning. Using the Daya Bay detector as a benchmark case and the vertex reconstruction performance as the objective for the deep neural network, we find that the photomultiplier tubes (PMTs) have different relative importance to the vertex reconstruction. More importantly, the vertex position resolutions for the Daya Bay detector follow approximately a multi-exponential relationship with respect to the number of PMTs and hence, the coverage. This could also assist in deciding on the merits of installing additional PMTs for future detector plans. The approach could easily be used with other objectives in place of vertex reconstruction