Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMCNNTUNES: tuning Shower Monte Carlo generators with machine learning
253
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
The parameters tuning of event generators is a research topic characterized by complex choices: the generator response to parameter variations is difficult to obtain on a theoretical basis, and numerical methods are hardly tractable due to the long computational times required by generators. Event generator tuning has been tackled by parametrisation-based techniques, with the most successful one being a polynomial parametrisation. In this work, an implementation of tuning procedures based on artificial neural networks is proposed. The implementation was tested with closure testing and experimental measurements from the ATLAS experiment at the Large Hadron Collider.
rate research
Read More
We present MadFlow, a first general multi-purpose framework for Monte Carlo (MC) event simulation of particle physics processes designed to take full advantage of hardware accelerators, in particular, graphics processing units (GPUs). The automation process of generating all the required components for MC simulation of a generic physics process and its deployment on hardware accelerator is still a big challenge nowadays. In order to solve this challenge, we design a workflow and code library which provides to the user the possibility to simulate custom processes through the MadGraph5_aMC@NLO framework and a plugin for the generation and exporting of specialized code in a GPU-like format. The exported code includes analytic expressions for matrix elements and phase space. The simulation is performed using the VegasFlow and PDFFlow libraries which deploy automatically the full simulation on systems with different hardware acceleration capabilities, such as multi-threading CPU, single-GPU and multi-GPU setups. The package also provides an asynchronous unweighted events procedure to store simulation results. Crucially, although only Leading Order is automatized, the library provides all ingredients necessary to build full complex Monte Carlo simulators in a modern, extensible and maintainable way. We show simulation results at leading-order for multiple processes on different hardware configurations.
The leading-order accurate description of top quark pair production, as usually employed in standard Monte Carlo event generators, gives no rise to the generation of a forward--backward asymmetry. Yet, non-negligible -- differential as well as inclusive -- asymmetries may be produced if coherent parton showering is used in the hadroproduction of top quark pairs. In this contribution we summarize the outcome of our study of this effect. We present a short comparison of different parton shower implementations and briefly comment on the phenomenology of the colour coherence effect at the Tevatron.
In this talk the most recent results obtained by interfacing GoSam with external Monte Carlo event generators are presented and summarized. In the last year the automatic one-loop amplitude generator GoSam has been used for the computation of several processes relevant for the LHC physics program. In the first part of the talk the latest results are summarized and the status of the interfaces to several external Monte Carlo programs, based on the Binoth-Les-Houches-Accord, is reported. The second part is dedicated to two selected computations. One concerning the associated production of a Higgs and a vector boson in association with 0 and 1 jet computed with GoSam+Powheg, and one focusing on the analysis of the forward-backward asymmetry in the production of top quark pairs using 0 and 1 jet merged samples with GoSam+Sherpa. Finally some recent results on Beyond-Standard-Model physics are also presented.
In this talk I gave a brief summary of leading order, next-to-leading order and shower calculations. I discussed the main ideas and approximations of the shower algorithms and the related matching schemes. I tried to focus on QCD issues and open questions instead of making a inventory of the existing programs.
We discuss prospects for Monte Carlo event generators incorporating the dynamics of transverse momentum dependent (TMD) parton distribution functions. We illustrate TMD evolution in the parton branching formalism, and present Monte Carlo applications of the method.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
