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One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled. We review how different analysis strategies solve this issue, including the traditional histogram approach used in most particle physics analyses, the Matrix Element Method, Optimal Observables, and modern techniques based on neural density estimation. We then discuss powerful new inference methods that use a combination of matrix element information and machine learning to accurately estimate the likelihood function. The MadMiner package automates all necessary data-processing steps. In first studies we find that these new techniques have the potential to substantially improve the sensitivity of the LHC legacy measurements.

We present a new way to define and compute the maximum significance achievable for signal and background processes at the LHC, using all available phase space information. As an example, we show that a light Higgs boson produced in weak-boson fusion with a subsequent decay into muons can be extracted from the backgrounds. The method, aimed at phenomenological studies, can be incorporated in parton-level event generators and accommodate parametric descriptions of detector effects for selected observables.

Likelihood ratio tests are a key tool in many fields of science. In order to evaluate the likelihood ratio the likelihood function is needed. However, it is common in fields such as High Energy Physics to have complex simulations that describe the distribution while not having a description of the likelihood that can be directly evaluated. In this setting it is impossible or computationally expensive to evaluate the likelihood. It is, however, possible to construct an equivalent version of the likelihood ratio that can be evaluated by using discriminative classifiers. We show how this can be used to approximate the likelihood ratio when the underlying distribution is a weighted sum of probability distributions (e.g. signal plus background model). We demonstrate how the results can be considerably improved by decomposing the ratio and use a set of classifiers in a pairwise manner on the components of the mixture model and how this can be used to estimate the unknown coefficients of the model, such as the signal contribution.

AbstractThe potential for the discovery of a Standard Model Higgs boson in the mass range mH < 2 mZ in the vector boson fusion mode has been studied for the ATLAS experiment at the LHC. The characteristic signatures of additional jets in the forward regions of the detector and of low jet activity in the central region allow for an efficient background rejection. Analyses for the H → WW(*) and H → ττ decay modes have been performed using a realistic simulation of the expected detector performance. The results obtained demonstrate the large discovery potential in the H → WW(*) decay channel and the sensitivity to Higgs boson decays into τ-pairs in the low-mass region around 120 GeV/c2.

Complementary to parallel open access and analysis preservation initiatives, ATLAS is taking steps to ensure that the data taken by the experiment during Run-1 remain accessible and available for future analysis by the collaboration. An evaluation of what is required to achieve this is underway, examining the ATLAS data production chain to establish the effort required and potential problems. Several alternatives are explored, but the favoured solution is to bring the Run 1 data and software in line with the equivalent to that which will be used for Run 2. This will result in a coherent ATLAS dataset for the data already taken and that to come in the future.

We present a set of recommendations for the presentation of LHC results on searches for new physics, which are aimed at providing a more efficient flow of scientific information between the experimental collaborations and the rest of the high energy physics community, and at facilitating the interpretation of the results in a wide class of models. Implementing these recommendations would aid the full exploitation of the physics potential of the LHC.

  Source Code Repositories * Github (http://github.com): A web-based hosting service for software development projects that use the Git revision control system, including many open-source projects. * Git (http://git-scm.com): A free and open-source distributed version control system designed to handle everything from small to very large projects with speed and efficiency. * Mercurial (http://mercurial.selenic.com): A free, distributed source control management tool. Systems to Package, Access, and Execute Data and Code * IPython Notebook (http://ipython.org/notebook.html): A web-based interactive computational environment where you can combine code execution, text, mathematics, plots, and rich media into a single document. * ROpenSci (http://ropensci.org): A suite of packages that allow access to data repositories through the R statistical programming environment. * Authorea (https://authorea.com): A collaborative online word processor for scholarly papers that allows the writing of web-native, living, dynamic, \"executable\" articles that include text, mathematical notation, images, and data.

The Parallel ROOT Facility – PROOF is a distributed analysis system which allows to exploit inherent event level parallelism of high energy physics data. PROOF can be configured to work with centralized storage systems, but it is especially effective together with distributed local storage systems – like Xrootd, when data are distributed over computing nodes. It works efficiently on different types of hardware and scales well from a multi-core laptop to large computing farms. From that point of view it is well suited for both large central analysis facilities and Tier 3 type analysis farms. PROOF can be used in interactive or batch like regimes. The interactive regime allows the user to work with typically distributed data from the ROOT command prompt and get a real time feedback on analysis progress and intermediate results. We will discuss our experience with PROOF in the context of ATLAS Collaboration distributed analysis. In particular we will discuss PROOF performance in various analysis scenarios and in multi-user, multi-session environments. We will also describe PROOF integration with the ATLAS distributed data management system and prospects of running PROOF on geographically distributed analysis farms.

ATLAS is one of the four major LHC experiments, designed to cover a wide range of physics topics. In order to cope with a rate of 40MHz and 25 interactions per bunch crossing, the ATLAS trigger system is divided in three different levels. The jet selection starts at first level with dedicated processors that search for high ET hadronic energy depositions. At the LVL2, the jet signatures are verified with the execution of a dedicated, fast jet reconstruction algorithm, followed by a calibration algorithm. Three possible granularities have been proposed and are being evaluated: cell based (standard), energy sums calculated at each Front-End Board and the use of the LVL1 Trigger Towers. In this presentation, the design and implementation of the jet trigger of ATLAS will be discussed in detail, emphasazing the major difficulties of each selection step. The performance of the jet algorithm, including timing, efficiencies and rates will also be shown, with detailed comparisons of the different unpacking modes.

Hydrolysis and adsorption of the sulfonylurea herbicides rimsulfuron and primisulfuron were investigated under laboratory conditions. Degradation in aqueous solutions was determined between pH 3 and 8 and between 5 and 35 C. Rimsulfuron degraded faster than primisulfuron. Half-lives at different temperatures at pH 4 ranged from 0.16 to 5.8 d for rimsulfuron and from 0.47 to 16.1 d for primisulfuron. Primisulfuron hydrolysis at 30 C decreased with increasing pH, but rimsulfuron decreased up to pH 7, then increased, suggesting rapid degradation in alkaline conditions. Adsorption, measured in six Colorado soils, was complete within 2 h in all soils. Adsorption isotherms were determined by batch equilibration of 75 to 600 μg L−1. Rimsulfuron was adsorbed in amounts 3.7 to 7 times higher than primisulfuron, with adsorption distribution coefficients (Kd) ranging from 0.1 to to 1.18 for primisulfuron and from 0.71 to 5.1 for rimsulfuron. Adsorption of both herbicides was slightly correlated with soil clay content, and primisulfuron adsorption was also correlated with organic matter content.