We present a new publicly available dataset that contains simulated data of a
novel calorimeter to be installed at the CERN Large Hadron Collider. This
detector will have more than six-million channels with each channel capable of
position, ionisation and precision time measurement. Reconstructing these
events in an efficient way poses an immense challenge which is being addressed
with the latest machine learning techniques. As part of this development a
large prototype with 12,000 channels was built and a beam of high-energy
electrons incident on it. Using machine learning methods we have reconstructed
the energy of incident electrons from the energies of three-dimensional hits,
which is known to some precision. By releasing this data publicly we hope to
encourage experts in the application of machine learning to develop efficient
and accurate image reconstruction of these electrons.

我们介绍了一个新的公开可用的数据集，其中包含欧洲核子研究中心大型强子对撞机即将安装的新型量热计的模拟数据。通过使用最新的机器学习技术，我们已经构建了一个拥有 12,000 个通道的大型原型，并通过高能电子束的照射对其进行测试。通过释放这些数据，我们希望鼓励机器学习应用专家开发出高效准确的电子能量重建和图像重建方法。

CMS 高粒度电磁量能器原型中的电子能量回归

Electron Energy Regression in the CMS High-Granularity Calorimeter  Prototype

Higgs bosons which decay principally to top quarks, such as in the minimal
supersymmetric model, produce a peak-dip structure in the $gg\to t\bar t$
invariant-mass spectrum. This structure is potentially observable at the CERN
Large Hadron Collider. (see BNL theory home page
this http URL for recent preprints)

研究表明，Higgs 玻色子主要衰变为顶夸克，例如在最小超对称模型中，会在 $ gg	o tar t $ 不变质量谱中产生峰 - 陷结构。该结构在 CERN 大型强子对撞机上有可能被观测到。