Evolution of online algorithms in ATLAS and CMS in Run 2

The Large Hadron Collider has entered a new era in Run 2, with centre-of-mass energy of 13 TeV and instantaneous luminosity reaching $\mathcal{L}_\textrm{inst} = 1.4\times$10$^{34}$ cm$^{-2}$ s$^{-1}$ for pp collisions. In order to cope with those harsher conditions, the ATLAS and CMS collaborations have improved their online selection infrastructure to keep a high efficiency for important physics processes - like W, Z and Higgs bosons in their leptonic and diphoton modes - whilst keeping the size of data stream compatible with the bandwidth and disk resources available. In this note, we describe some of the trigger improvements implemented for Run 2, including algorithms for selection of electrons, photons, muons and hadronic final states.Comment: 6 pages. Presented at The Fifth Annual Conference on Large Hadron Collider Physics (LHCP 2017), Shanghai, China, May 15-20, 201

The CMS Trigger Upgrade for the HL-LHC

The CMS experiment has been designed with a two-level trigger system: the Level-1 Trigger, implemented on custom-designed electronics, and the High Level Trigger, a streamlined version of the CMS offline reconstruction software running on a computer farm. During its second phase the LHC will reach a luminosity of $7.5\times10^{34}\,\textrm{cm}^{-2}\,\textrm{s}^{-1}$ with a pileup of 200 collisions, producing integrated luminosity greater than 3000 fb$^{-1}$ over the full experimental run. To fully exploit the higher luminosity, the CMS experiment will introduce a more advanced Level-1 Trigger and increase the full readout rate from 100 kHz to 750 kHz. CMS is designing an efficient data-processing hardware trigger (Level-1) that will include tracking information and high-granularity calorimeter information. The current conceptual system design is expected to take full advantage of advances in FPGA and link technologies over the coming years, providing a high-performance, low-latency system for large throughput and sophisticated data correlation across diverse sources. The higher luminosity, event complexity and input rate present an unprecedented challenge to the High Level Trigger, that aims to achieve a similar efficiency and rejection factor as today despite the higher pileup and more pure preselection. In this presentation we will discuss the ongoing studies and prospects for the online reconstruction and selection algorithms for the high-luminosity era.Comment: 6 pages, 4 figures. Presented at CHEP 2019 - 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, November 04-08, 2019. Replaced with published versio

Evaluating generative models in high energy physics

There has been a recent explosion in research into machine-learning-based generative modeling to tackle computational challenges for simulations in high energy physics (HEP). In order to use such alternative simulators in practice, we need well-defined metrics to compare different generative models and evaluate their discrepancy from the true distributions. We present the first systematic review and investigation into evaluation metrics and their sensitivity to failure modes of generative models, using the framework of two-sample goodness-of-fit testing, and their relevance and viability for HEP. Inspired by previous work in both physics and computer vision, we propose two new metrics, the Fr\'echet and kernel physics distances (FPD and KPD, respectively), and perform a variety of experiments measuring their performance on simple Gaussian-distributed, and simulated high energy jet datasets. We find FPD, in particular, to be the most sensitive metric to all alternative jet distributions tested and recommend its adoption, along with the KPD and Wasserstein distances between individual feature distributions, for evaluating generative models in HEP. We finally demonstrate the efficacy of these proposed metrics in evaluating and comparing a novel attention-based generative adversarial particle transformer to the state-of-the-art message-passing generative adversarial network jet simulation model. The code for our proposed metrics is provided in the open source JetNet Python library.Comment: 11 pages, 5 figures, 3 tables, and a 5 page appendi

Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders

We study how to use Deep Variational Autoencoders for a fast simulation of jets of particles at the LHC. We represent jets as a list of constituents, characterized by their momenta. Starting from a simulation of the jet before detector effects, we train a Deep Variational Autoencoder to return the corresponding list of constituents after detection. Doing so, we bypass both the time-consuming detector simulation and the collision reconstruction steps of a traditional processing chain, speeding up significantly the events generation workflow. Through model optimization and hyperparameter tuning, we achieve state-of-the-art precision on the jet four-momentum, while providing an accurate description of the constituents momenta, and an inference time comparable to that of a rule-based fast simulation.Comment: 11 pages, 8 figure

Background Monte Carlo Samples for a Future Hadron Collider

A description of Standard Model background Monte Carlo samples produced for studies related to future hadron colliders