Daniel Abercrombie

This document is the final report of the ATLAS-CMS Dark Matter Forum, a forum organized by the ATLAS and CMS collaborations with the participation of experts on theories of Dark Matter, to select a minimal basis set of dark matter simplified models that should support the design of the early LHC Run-2 searches. A prioritized, compact set of benchmark models is proposed, accompanied by studies of the parameter space of these models and a repository of generator implementations. This report also addresses how to apply the Effective Field Theory formalism for collider searches and present the results of such interpretations.

The central Monte-Carlo production of the CMS experiment utilizes the WLCG infrastructure and manages daily thousands of tasks, each up to thousands of jobs. The distributed computing system is bound to sustain a certain rate of failures of various types, which are currently handled by computing operators a posteriori. Within the context of computing operations, and operation intelligence, we propose a Machine Learning technique to learn from the operators with a view to reduce the operational workload and delays. This work is in continuation of CMS work on operation intelligence to try and reach accurate predictions with Machine Learning. We present an approach to consider the log files of the workflows as regular text to leverage modern techniques from Natural Language Processing (NLP). In general, log files contain a substantial amount of text that is not human language. Therefore, different log parsing approaches are studied in order to map the log files’ words to high dimensional vectors. These vectors are then exploited as feature space to train a model that predicts the action that the operator has to take. This approach has the advantage that the information of the log files is extracted automatically and the format of the logs can be arbitrary. In this work the performance of the log file analysis with NLP is presented and compared to previous approaches.

Traditional design rules to ensure device functionality and yield are defined by multiple criteria. However, in a complex and mature design and manufacturing environment and a quickly changing marketplace, one should consider a more comprehensive and efficient way to gradually replace the rigid design rules with yield and product models based on the extracted layout features, process capabilities, and market conditions. This methodology may be of particular attractiveness for custom products such as Systems-on-Chip where the complexity of customized design rules can be a detriment for product optimization. In addition, the key criterion for product development: profitability in the marketplace, or Design-for-Profit (DfP), has usually not been taken into account in the verification of design kits. In this paper, we would discuss how yield models can help re-derive the existing design rule and "recommended rule" methodology and take it to the next level of a comprehensive product development using model based design.

Abstract Dynamo is a full-stack software solution for scientific data management. Dynamo’s architecture is modular, extensible, and customizable, making the software suitable for managing data in a wide range of installation scales, from a few terabytes stored at a single location to hundreds of petabytes distributed across a worldwide computing grid. This article documents the core system design of Dynamo and describes the applications that implement various data management tasks. A brief report is also given on the operational experiences of the system at the CMS experiment at the CERN Large Hadron Collider and at a small-scale analysis facility.

Fractal-based phase screens are compared to subharmonic-augmented FFT-based phase screens using both analytic and numerical statistical methods. Properties such as homogeneity and stationarity are investigated. It is shown analytically that augmented FFT-based screens are homogeneous and strict-sense stationary. Analytic means are also used to show that fractal-based phase screens are not stationary based on the definition of fractal Brownian surfaces. Corresponding numerical results show that the structure functions in both cases appear to be stationary or nearly so. It is shown that both types of phase screens must have “creasing” that has been observed in the power spectrum, due to edge effects. FFT-based screens without subharmonic augmentation, on the other hand, are shown to avoid such creasing. Sample results are also presented for imaging reconstructions with both types of screens.

Machine and Deep Learning techniques are being used in various areas of CMS operations at the LHC collider, like data taking, monitoring, processing and physics analysis. A review a few selected use cases - with focus on CMS software and computing - shows the progress in the field, with highlight on most recent developments, as well as an outlook to future applications in LHC Run III and towards the High-Luminosity LHC phase.

The automatization of failing workflow handling is discussed. Implementation of a pipeline for DAQ and ML of error logs using big data analysis tools is available at github. Development of a prototype NLP model in Keras is explored.