Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Mieskolainen, Mikael"'
Autor:
Mieskolainen, Mikael
Combinatorial inverse problems in high energy physics span enormous algorithmic challenges. This work presents a new deep learning driven clustering algorithm that utilizes a space-time non-local trainable graph constructor, a graph neural network, a
Externí odkaz:
http://arxiv.org/abs/2309.14113
Autor:
Mieskolainen, Mikael
We introduce GRANIITTI, a new Monte Carlo event generator designed especially to solve the enigma of glueballs at the LHC. We discuss the available physics processes, compare the simulations against STAR data from RHIC and span ambitious future direc
Externí odkaz:
http://arxiv.org/abs/2304.06010
Autor:
Mieskolainen, Mikael1,2 m.mieskolainen@imperial.ac.uk
Publikováno v:
EPJ Web of Conferences. 5/6/2024, Vol. 295, p1-8. 8p.
Autor:
Mieskolainen, Mikael, Bainbridge, Robert, Buchmueller, Oliver, Lyons, Louis, Wardle, Nicholas
The determination of the infection fatality rate (IFR) for the novel SARS-CoV-2 coronavirus is a key aim for many of the field studies that are currently being undertaken in response to the pandemic. The IFR together with the basic reproduction numbe
Externí odkaz:
http://arxiv.org/abs/2012.02100
Autor:
Mieskolainen, Mikael
We describe the physics and computational power of GRANIITTI Monte Carlo event generator, a new fully multithreaded engine designed for high energy diffraction, written in modern C++. The emphasis is especially on the low-mass domain of central exclu
Externí odkaz:
http://arxiv.org/abs/1910.06300
Autor:
Mieskolainen, Mikael
High energy diffraction and soft QCD span exciting final state topologies and fluctuations which have not yet been measured or characterized in a fully exhaustive way. In this work, we go beyond the standard measures and formulate a new framework to
Externí odkaz:
http://arxiv.org/abs/1910.06279
Autor:
Mieskolainen, Mikael
Inversion of the K-fold stochastic autoconvolution integral equation is an elementary nonlinear problem, yet there are no de facto methods to solve it with finite statistics. To fix this problem, we introduce a novel inverse algorithm based on a comb
Externí odkaz:
http://arxiv.org/abs/1905.12585
Autor:
Mieskolainen, Mikael
We discuss novel ways to probe high energy diffraction, first inclusive diffraction and then central exclusive processes at the LHC. Our new Monte Carlo synthesis and analysis framework, Graniitti, includes differential screening, an expendable set o
Externí odkaz:
http://arxiv.org/abs/1811.01730
Autor:
Barbone Marco, Brown Christopher, Gaydadjiev Georgi, Maguire Thomas, Mieskolainen Mikael, Radburn-Smith Benjamin, Luk Wayne, Tapper Alexander
Publikováno v:
EPJ Web of Conferences, Vol 295, p 09014 (2024)
Neural Networks (NN) are often trained offline on large datasets and deployed on specialised hardware for inference, with a strict separation between training and inference. However, in many realistic applications the training environment differs fro
Externí odkaz:
https://doaj.org/article/402bd0b13e1642c598e559cd2ae21f09
Autor:
Mieskolainen, Mikael
We introduce a new high dimensional algorithm for efficiency corrected, maximally Monte Carlo event generator independent fiducial measurements at the LHC and beyond. The approach is driven probabilistically using a Deep Neural Network on an event-by
Externí odkaz:
http://arxiv.org/abs/1809.06101