论文标题
优化$ \ tilde {x}^{\ pm} _ {w} \ rightArrow h^0 \ ell^{\ pm} _ {i} $带有small-r（r = 0.4）和大r（r = 1.0）jets的重建效率
Optimizing $\tilde{X}^{\pm}_{W} \rightarrow h^0\ell^{\pm}_{i}$ Reconstruction Efficiency with Small-R (R = 0.4) and Large-R (R = 1.0) Jets
论文作者
论文摘要
除标准模型搜索以外的许多人都采用喷气机来简化事件重建。这些喷气机聚类粒子淋浴产品成可计算的对象，然后将其用于获取有关父粒子的信息。大-R（r = 1.0）喷气机将这些产品组合成一个跨2个弧度的喷气机，而小-R（r = 0.4）喷气机用于进一步完善单个B Quark轨迹。优化喷气机的使用对于对具有高横向动量的希格斯玻色子进行精确测量至关重要，并且本文使用Wino Chargino LSP衰减中产生的B Quarks来识别最好的参数。蒙特卡洛模拟发现，诸如希格斯玻色子与B-jets之间的距离之类的参数与选择最准确的小-R重建相关。真实数据分析证实了本文的发现，尤其是对于质量较高的Charginos。然后使用这些参数来完善小-R射流的选择，从而提高了整个Chargino质量的重建效率。进一步分析大-R射流选择将增强这些结果，尤其是在较高的Chargino质量下。神经网络还将被证明可用于探索跨夏尔尼诺群众参数组合的影响。
Many Beyond the Standard Model searches at ATLAS employ jets to simplify event reconstruction. These jets cluster particle shower products into calculable objects, which are then used to obtain information about parent particles. Large-R (R = 1.0) jets combine these products into one jet that spans 2 radians, while small-R (R = 0.4) jets are used to further refine individual b-quark trajectories. Optimizing the use of jets is crucial for making precision measurements of Higgs bosons with high transverse momenta, and this paper uses b-quarks produced in the Wino chargino LSP decay to identify parameters that best do so. Monte Carlo simulation found that parameters such as the distance between Higgs bosons and the distance between b-jets were relevant in selecting the most accurate small-R reconstructions. Truth data analysis corroborated the paper's findings, especially for charginos with higher mass. These parameters were then used to refine small-R jet selection, increasing reconstruction efficiency across chargino masses. Further analysis into large-R jet selection would enhance these results, especially at higher chargino masses. A neural network would also prove useful for exploring the effects of combinations of parameters across chargino masses.
