Fast b-tagging at the high-level trigger of the ATLAS experiment in LHC Run 3
Fast b-tagging at the high-level trigger of the ATLAS experiment in LHC Run 3
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DOI：
10.1088/1748-0221/18/11/p11006
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发表时间：
2023-06
影响因子：
1.3
通讯作者：
G. Aad;B. Abbott;K. Abeling;N. J. Abicht;S. Abidi;A. Aboulhorma;H. Abramowicz;H. Abreu
中科院分区：
文献类型：
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作者：
G. Aad;B. Abbott;K. Abeling;N. J. Abicht;S. Abidi;A. Aboulhorma;H. Abramowicz;H. Abreu
ATLAS实验依赖于实时强子喷注重建和b-标记来记录包含b-喷注的完整强子事件。这些算法需要轨迹重建，这在计算上是昂贵的，并且即使在通过ATLAS第一阶段基于硬件的触发的事件率降低的情况下，也可能压倒高级触发场。在LHC运行3中，ATLAS通过引入基于快速神经网络的b标记器来减轻这些计算需求，该b标记器使用强子喷流和轨道的输入作为低精度滤波器。它在硬件触发之后和剩余的高级别触发重建之前运行。这种设计依赖于与跟踪重建相比可以忽略不计的神经网络推理成本，以及将跟踪限制在检测器的特定区域的成本降低。在标准模型HH → bb BBB BBB的情况下，一个依赖于b-jet触发器的密钥签名，滤波器将剩余的高级别触发器的输入速率降低了五倍，但总体信号效率降低了大约2%。
The ATLAS experiment relies on real-time hadronic jet reconstruction and b-tagging to record fully hadronic events containing b-jets. These algorithms require track reconstruction, which is computationally expensive and could overwhelm the high-level-trigger farm, even at the reduced event rate that passes the ATLAS first stage hardware-based trigger. In LHC Run 3, ATLAS has mitigated these computational demands by introducing a fast neural-network-based b-tagger, which acts as a low-precision filter using input from hadronic jets and tracks. It runs after a hardware trigger and before the remaining high-level-trigger reconstruction. This design relies on the negligible cost of neural-network inference as compared to track reconstruction, and the cost reduction from limiting tracking to specific regions of the detector. In the case of Standard Model HH → bb̅bb̅, a key signature relying on b-jet triggers, the filter lowers the input rate to the remaining high-level trigger by a factor of five at the small cost of reducing the overall signal efficiency by roughly 2%.