139 citations · 159 across the 11 of their papers we have counts for
12 papers
HyperTrack: Neural Combinatorics for High Energy Physics
Mikael Mieskolainen
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…
GRANIITTI: towards a deep learning-enhanced Monte Carlo event generator for high-energy diffraction
Mikael Mieskolainen
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 s…
Statistical techniques to estimate the SARS-CoV-2 infection fatality rate
Mikael Mieskolainen, Robert Bainbridge, Oliver Buchmueller +2
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 respon…
GRANIITTI: A Monte Carlo Event Generator for High Energy Diffraction
Mikael Mieskolainen
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…
Combinatorial Superstatistics for Soft QCD
Mikael Mieskolainen
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…
On the Inversion of High Energy Proton
Mikael Mieskolainen
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 fi…
Algorithmics of Diffraction
Mikael Mieskolainen
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 fram…
DeepEfficiency - optimal efficiency inversion in higher dimensions at the LHC
Mikael Mieskolainen
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…
Turning the LHC Ring into a New Physics Search Machine
Risto Orava
The LHC Collider Ring is proposed to be turned into an ultimate automatic search engine for new physics in four consecutive phases: (1) Searches for heavy particles produced in Cen…
Observables of QCD Diffraction
Mikael Mieskolainen, Risto Orava
A new combinatorial vector space measurement model is introduced for soft QCD diffraction. The model independent mathematical construction resolves experimental complications; the…
LHC Forward Physics
K. Akiba, M. Akbiyik, M. Albrow +184
The goal of this report is to give a comprehensive overview of the rich field of forward physics, with a special attention to the topics that can be studied at the LHC. The report…
Turning the LHC Ring into a New Physics Search Machine
Matti Kalliokoski, Jerry W. Lämsä, Mikael Mieskolainen +1
By combining the LHC Beam Loss Monitoring (BLM) system with the LHC experiments, a powerful search machine for new physics beyond the standard model can be realised. The pair of fi…