Related papers: Reconstructing Sparticle masses at the LHC using G…
We propose a new strategy to probe heavy neutrinos with non-universal fermion couplings at the Large Hadron Collider (LHC) using a novel production mechanism and machine-learning algorithms. Focusing on proton--proton collisions at…
Neutrinos, dark matter, and long-lived neutral particles traverse the particle detectors unnoticed, carrying away information about their parent particles and interaction sources needed to reconstruct key variables like resonance peaks in…
We apply object detection techniques based on deep convolutional blocks to end-to-end jet identification and reconstruction tasks encountered at the CERN Large Hadron Collider (LHC). Collision events produced at the LHC and represented as…
We explore the potential to use machine learning methods to search for heavy neutrinos, from their hadronic final states including a fat-jet signal, via the processes $pp \rightarrow W^{\pm *}\rightarrow \mu^{\pm} N \rightarrow \mu^{\pm}…
Reconstructing charged particle tracks is a fundamental task in modern collider experiments. The unprecedented particle multiplicities expected at the High-Luminosity Large Hadron Collider (HL-LHC) pose significant challenges for track…
Magnetic resonance imaging serves as an essential tool for clinical diagnosis. However, it suffers from a long acquisition time. The utilization of deep learning, especially the deep generative models, offers aggressive acceleration and…
Models of neutrino mass generation provide well motivated scenarios of Beyond-the-Standard-Model physics. The synergy between low energy and high energy LHC searches facilitates an effective approach to rule out, constrain or ideally…
Generative models have demonstrated strong performance in conditional settings and can be viewed as a form of data compression, where the condition serves as a compact representation. However, their limited controllability and…
We present a method to reconstruct the initial conditions of the universe using observed galaxy positions and luminosities under the assumption that the luminosities can be calibrated with weak lensing to give the mean halo mass. Our method…
We investigate the possibility of learning the representations of cosmological multifield dataset from the CAMELS project. We train a very deep variational encoder on images which comprise three channels, namely gas density (Mgas), neutral…
In this paper we propose a new reconstruction method to explore the low mass region in the associated production of top-quark pairs ($t\bar{t}$) with a generic scalar boson ($\phi$) at the LHC. The new method of mass reconstruction shows an…
The high-luminosity era of the LHC will offer greatly increased number of events for more precise Standard Model measurements and Beyond Standard Model searches, but will also pose unprecedented challenges to the detectors. To meet these…
A search for electroweak production of supersymmetric particles is performed in two-lepton and three-lepton final states using recursive jigsaw reconstruction. The search uses data collected in 2015 and 2016 by the ATLAS experiment in…
Reconstructing the initial conditions of the Universe from late-time observations has the potential to optimally extract cosmological information. Due to the high dimensionality of the parameter space, a differentiable forward model is…
Processes of the form $pp\to anything \to X_i X_j \to x\bar{x} + y\bar{y} (+ \slashchar{E})$ are studied via a technique that may be viewed as an adaptation of time-honoured Dalitz plot analyses. $X_i$ and $X_j$ are new heavy states (with…
Recent results from the ATLAS and the CMS experiments at the Large Hadron Collider indicate the presence of a top-quark pair bound state near the threshold region. We present a way to reconstruct a toponium state at the $t\bar{t}$ threshold…
Using generative adversarial networks (GANs), we investigate the possibility of creating large amounts of analysis-specific simulated LHC events at limited computing cost. This kind of generative model is analysis specific in the sense that…
Recently deep generative models have achieved impressive progress in modeling the distribution of training data. In this work, we present for the first time a generative model for 4D light field patches using variational autoencoders to…
Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High…
In some supersymmetric models like split supersymmetry or models with non-universal gaugino mass, bino (LSP) and winos (NLSP) may have rather small mass splitting in order to provide the correct dark matter relic density through bino/wino…