In this paper, we point out a novel signature of physics beyond the Standard Model which could potentially be observed both at the Large Hadron Collider (LHC) and at future colliders. This signature, which emerges naturally within many proposed extensions of the Standard Model, results from the multiple displaced vertices associated with the successive decays of unstable, long-lived particles along the same decay chain. We call such a sequence of displaced vertices a "tumbler." We examine the prospects for observing tumblers at the LHC and assess the extent to which tumbler signatures can be distinguished from other signatures of new physics which also involve multiple displaced vertices within the same collider event. As part of this analysis, we also develop a procedure for reconstructing the masses and lifetimes of the particles involved in the corresponding decay chains. We find that the prospects for discovering and distinguishing tumblers can be greatly enhanced by exploiting precision timing information such as would be provided by the CMS timing layer at the high-luminosity LHC. Our analysis therefore provides strong additional motivation for continued efforts to improve the timing capabilities of collider detectors at the LHC and beyond.

The high accuracy of detector simulation is crucial for modern particle physics experiments. However, this accuracy comes with a high computational cost, which will be exacerbated by the large datasets and complex detector upgrades associated with nextgeneration facilities such as the High Luminosity LHC. We explore the viability of regressionbased machine learning (ML) approaches using convolutional neural networks (CNNs) to "denoise" faster, lower-quality detector simulations, augmenting them to produce a higherquality final result with a reduced computational burden. The denoising CNN works in concert with classical detector simulation software rather than replacing it entirely, increasing its reliability compared to other ML approaches to simulation. We obtain promising results from a prototype based on photon showers in the CMS electromagnetic calorimeter. Future directions are also discussed.

No abstract

No abstract

Motivation• Detector simulation is a critical part of High Energy Physics (HEP), but is computationally expensive • Generative Neural Networks (GANs) have been explored as a machine learning solution (Ref.[1])• GANs can generate new simulated events from a large database of existing events, but could have mathematical and practical concerns • We use of a Convolutional Neural Network (CNN) as a solution• This CNN takes in quickly generated low-quality events and enhances them to a more useful resolution (See Fig. 1) Figure 5: Average energy in an event. Again, enhanced and high-quality match much more closely than with low-quality.Figure 2: This use of a CNN was inspired by a similar regression algorithm used by Disney Pixar to speed up the production of their computer-simulated films, where instead of enhancing images for films, we enhance photon simulation data Analysis• We want to generate a reasonable physical prediction, not predict a particular image, so pixel-by-pixel comparison may not be the only useful metric • Previous metrics for AI simulation Ref. [1] • centroid of the energy data • number of bins above a given energy threshold • We use these same metrics, but as we use a CNN and not a GAN, we can compare high quality, low quality, and CNN-enhanced events metrics against each other • Enhanced images more closely match energy centroid data (see Fig. 3 & 4) than low-quality input data • Also match aggregate energy data well (Fig. 5 & 6)