Related papers: Shower Identification in Calorimeter using Deep Le…
Recent developments on a deep learning feed-forward network for estimating elliptic flow ($v_2$) coefficients in heavy-ion collisions have shown us the prediction power of this technique. The success of the model is mainly the estimation of…
The granularity of calorimeter has been revolutionary boosted for future collider experiments. The calorimeter has been pushed to a stage that the sub structure of showers especially hadronic showers can be recorded to a high precision. New…
Measurements from the Large Hadron Collider (LHC) and the Relativistic Heavy Ion Collider (RHIC) can be used to study the properties of quark-gluon plasma. Systematic constraints on these properties must combine measurements from different…
In the reconstruction of physics events at future e$^+$e$^-$ colliders the calorimeter design has a crucial role in the overall detector performance. The reconstruction of events with many jets in their final state sets stringent…
Algorithms based on the particle flow approach are becoming increasingly utilized in collider experiments due to their superior jet energy and missing energy resolution compared to the traditional calorimeter-based measurements. Such…
We outline a physics program at Fermilab that would significantly improve our ability to understand the behavior of hadronic showers in calorimeters. This would involve a two-pronged approach designed to measure particle production cross…
Recently, there has been a growing focus on the search for anomalous objects beyond standard model (BSM) signatures at the Large Hadron Collider (LHC). This study investigates novel signatures involving highly collimated photons, referred…
The jet quenching phenomenon in heavy ion collisions provides a strong evidence of the modification of parton shower in the quark-gluon plasma (QGP). Jet substructure observables can probe various aspects of the jet formation mechanism.…
Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. To establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark…
The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the…
Jet quenching is a phenomenon in heavy-ion collisions arising from jet interactions with the quark-gluon plasma (QGP). Its study is complicated by the interplay of multiple physics processes that affect jet observables. In addition,…
There has been considerable recent activity applying deep convolutional neural nets (CNNs) to data from particle physics experiments. Current approaches on ATLAS/CMS have largely focussed on a subset of the calorimeter, and for identifying…
Vector boson fusion established itself as a highly reliable channel to probe the Higgs boson and an avenue to uncover new physics at the Large Hadron Collider. This channel provides the most stringent bound on Higgs' invisible decay…
The goal of the ultra-relativistic heavy ion program is to study Quantum Chromodynamics under finite temperature and density conditions. After a couple of decades of experiment, the focus at the top RHIC and the LHC energy has evolved to…
Production of quarks and gluons in hadron collisions tests Quantum Chromodynamics (QCD) over a wide range of energy. Models of QCD are implemented in event generators to simulate hadron collisions and evolution of quarks and gluons into…
Extensive air showers are complex objects, resulting of billions of particle reactions initiated by single cosmic ray at ultra-high-energy. Their characteristics are sensitive both to the mass of the primary cosmic ray and to the details of…
We study whether machine-learning models for fast calorimeter simulations can learn meaningful representations of calorimeter signatures that account for variations in the full particle detector's configuration. This may open new…
With the great promise of deep learning, discoveries of new particles at the Large Hadron Collider (LHC) may be imminent. Following the discovery of a new Beyond the Standard model particle in an all-hadronic channel, deep learning can also…
The high-luminosity upgrade of the LHC will come with unprecedented physics and computing challenges. One of these challenges is the accurate reconstruction of particles in events with up to 200 simultaneous proton-proton interactions. The…
A hot, dense medium called a Quark Gluon Plasma (QGP) is created in ultrarelativistic heavy ion collisions. Hard parton scatterings generate high momentum partons that traverse the medium, which then fragment into sprays of particle called…