The many ways in which machine and deep learning are transforming the analysis and simulation of data in particle physics are reviewed. The main methods based on boosted decision trees and various types of neural networks are introduced, and cutting-
edge applications in the experimental and theoretical/phenomenological domains are highlighted. After describing the challenges in the application of these novel analysis techniques, the review concludes by discussing the interactions between physics and machine learning as a two-way street enriching both disciplines and helping to meet the present and future challenges of data-intensive science at the energy and intensity frontiers.
The dilepton decay channels provide clean signatures and are an ideal hunting ground for high mass resonant, like Z, or non-resonant, like contact interactions or extra dimensions, searches at the LHC. The production of high invariant mass opposite s
ign lepton pairs in proton-proton collisions in the Standard Model is dominated by the Drell-Yan process. In addition to this photon or Z exchange mediated mechanism, photons radiated by the incoming protons can collide and produce lepton pairs. In this paper detailed calculations of the Drell-Yan process at next-to-next-to-leading order in QCD and next-to-leading order in the electroweak corrections, augmented with the photon-induced effects, are presented in the typical acceptance of a multi-purpose LHC detector at center of mass energy 13 TeV. Estimates of the expected backgrounds for new physics searches are provided for dilepton invariant masses up to the LHC kinematic limit.
The production of high invariant mass opposite sign lepton pairs in proton-proton collisions at the LHC is dominated by the Drell-Yan process. In addition to this photon or Z exchange mediated mechanism, gamma-gamma collisions, where photons radiated
by the incoming protons collide, can produce lepton pairs. This is an important additional source of background for high mass resonant (like Z) or non-resonant (like contact interactions) searches. In this paper detailed calculations of the Drell-Yan and photon-induced cross sections in the typical acceptance of a multi-purpose LHC detector at center of mass energy 13 TeV are presented. The hint for a diphoton excess at a mass around 750 GeV, reported by the ATLAS and CMS experiments from the analysis of the 2015 data at 13 TeV, raises the possibility that such an excess could be produced through gamma-gamma collisions. A good theoretical understanding and measurements in the dilepton channels at these energies can help to elucidate this production hypothesis.
The apparent unification of gauge couplings in Grand Unified Theories around 10$^{16}$ GeV is one of the strong arguments in favor of Supersymmetric extensions of the Standard Model. In this paper, an analysis of the measurements of the strong coupli
ng running from the CMS experiment at the LHC is combined with a traditional gauge coupling unification analysis using data at the Z peak. This approach places powerful constraints on the possible scales of new physics and on the parameters around the unification scale. A supersymmetric analysis without GUT threshold corrections describes the CMS data well and provides perfect unification. The favored scales are $M_{SUSY} = 2820 +670 -540$ GeV and $M_{GUT} = 1.05 pm 0.06 cdot 10^{16}$ GeV. For zero or small threshold corrections the scale of new physics may be well within LHC reach.
The importance of the H -> ZZ -> 4l golden channel was shown by its major role in the discovery, by the ATLAS and CMS collaborations, of a Higgs-like boson with mass near 125 GeV. We analyze the discrimination power of the matrix element method both
for separating the signal from the irreducible ZZ background and for distinguishing various spin and parity hypotheses describing a signal in this channel. We show that the proper treatment of interference effects associated with permutations of identical leptons in the four electron and four muon final states plays an important role in achieving the best sensitivity in measuring the properties of the newly discovered boson. We provide a code, MEKD, that calculates kinematic discriminants based on the full leading order matrix elements and which will aid experimentalists and phenomenologists in their continuing studies of the H -> ZZ -> 4l channel.
A review of the discovery potential of LHC for new phenomena beyond the Standard Model (BSM) other than Supersymmetry in the early phase of running is presented. Topics covered include searches for extra dimensions in different scenarios (ADD, Randal
l-Sundrum, black holes ...), resonance hunting in di-lepton, di-photon and di-jet final states, searches for contact interactions, heavy stable charged particles, technicolor, etc. The strategies of the ATLAS and CMS experiments to understand the detectors and prepare them for search mode and the prospects for discoveries using early data are described.
The apparent unification of gauge couplings around 10^16 GeV is one of the strong arguments in favor of Supersymmetric extensions of the Standard Model (SM). In this contribution two new analyses of the gauge coupling running, the latter using in con
trast to previous studies not data at the Z peak but at LEP2 energies, are presented. The generic SUSY scale in the more precise novel approach is 93 < M_SUSY < 183 GeV, easily within LHC, and possibly even within Tevatron reach.
The apparent unification of gauge couplings around 10^16 GeV is one of the strong arguments in favor of Supersymmetric extensions of the Standard Model (SM). In this contribution a new analysis, using the latest experimental data, is performed. The s
trong coupling alpha_s emerges as the key factor for evaluating the results of the fits, as the experimental and theoretical uncertainties in its measurements are substantially higher than for the electromagnetic and weak couplings. The present analysis pays special attention to numerical and statistical details. The results, combined with the current limits on the supersymmetric particle masses, favor a value for the SUSY scale <~ 150 GeV and for alpha_s = 0.118-0.119.
A key feature of collaboration in science and software development is to have a {em log} of what and how is being done - for private use and reuse and for sharing selected parts with collaborators, which most often today are distributed geographicall
y on an ever larger scale. Even better if this log is {em automatic}, created on the fly while a scientist or software developer is working in a habitual way, without the need for extra efforts. The {tt CAVES} and {tt CODESH} projects address this problem in a novel way, building on the concepts of {em virtual state} and {em virtual transition} to provide an automatic persistent logbook for sessions of data analysis or software development in a collaborating group. A repository of sessions can be configured dynamically to record and make available the knowledge accumulated in the course of a scientific or software endeavor. Access can be controlled to define logbooks of private sessions and sessions shared within or between collaborating groups.
The Collaborative Analysis Versioning Environment System (CAVES) project concentrates on the interactions between users performing data and/or computing intensive analyses on large data sets, as encountered in many contemporary scientific disciplines
. In modern science increasingly larger groups of researchers collaborate on a given topic over extended periods of time. The logging and sharing of knowledge about how analyses are performed or how results are obtained is important throughout the lifetime of a project. Here is where virtual data concepts play a major role. The ability to seamlessly log, exchange and reproduce results and the methods, algorithms and computer programs used in obtaining them enhances in a qualitative way the level of collaboration in a group or between groups in larger organizations. The CAVES project takes a pragmatic approach in assessing the needs of a community of scientists by building series of prototypes with increasing sophistication. In extending the functionality of existing data analysis packages with virtual data capabilities these prototypes provide an easy and habitual entry point for researchers to explore virtual data concepts in real life applications and to provide valuable feedback for refining the system design. The architecture is modular based on Web, Grid and other services which can be plugged in as desired. As a proof of principle we build a first system by extending the very popular data analysis framework ROOT, widely used in high energy physics and other fields, making it virtual data enabled.
