Related papers: Identification of heavy, energetic, hadronically d…
Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the tradeoff between accuracy and computational cost. Here, we assess multiple ML approaches for predicting the atomization…
This paper presents a search for new heavy particles decaying into a pair of top quarks using 139 fb$^{-1}$ of proton--proton collision data recorded at a centre-of-mass energy of $\sqrt{s}=13$ TeV with the ATLAS detector at the Large…
Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning…
Machine learning (ML) is a rapidly growing area of research in the field of particle physics, with a vast array of applications at the CERN LHC. ML has changed the way particle physicists conduct searches and measurements as a versatile…
A search is presented for a heavy resonance $Y$ decaying into a Standard Model Higgs boson $H$ and a new particle $X$ in a fully hadronic final state. The full Large Hadron Collider Run 2 dataset of proton-proton collisions at $\sqrt{s}=…
The results of a search for new heavy $W^\prime$ bosons decaying to an electron or muon and a neutrino using proton-proton collision data at a centre-of-mass energy of $\sqrt{s} = 13$ TeV are presented. The dataset was collected in 2015 and…
A search for long-lived particles in events with significant missing transverse momentum and at least one displaced vertex is presented. This analysis is performed using 137 $\text{fb}^{-1}$ of $pp$ collision data collected between…
Many models beyond the standard model predict the existence of vector-like quarks or other types of heavy resonances. Using proton-proton collision data at center-of-mass energies of 8 and 13 TeV, the ATLAS and CMS Collaborations have…
A search is performed for heavy neutrinos in the decay of a $W$ boson into two muons and a jet. The data set corresponds to an integrated luminosity of approximately $3.0 \text{ fb}^{-1}$ of proton-proton collision data at centre-of-mass…
An overview of analyses using data at sqrt(s) = 7 TeV and 8 TeV of proton-proton collisions at the LHC is presented. These analyses use boosted techniques to search for new phenomena involving top quarks and to measure the production of top…
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics processes, we extract extra information from the simulator.…
Quantum machine learning (QML) leverages the potential from machine learning to explore the subtle patterns in huge datasets of complex nature with quantum advantages. This exponentially reduces the time and resources necessary for…
Holograms of colloidal particles can be analyzed with the Lorenz-Mie theory of light scattering to measure individual particles' three-dimensional positions with nanometer precision while simultaneously estimating their sizes and refractive…
Top quarks produced in multi-TeV processes will have large Lorentz boosts, and their decay products will be highly collimated. In semileptonic decay modes, this often leads to the merging of the b-jet and the hard lepton according to…
Leveraging scanning tunneling microscopy (STM) for atomic-scale fabrication has led to many advancements such as the creation of atomic electron-spin qubit structures on surfaces. However, the time-consuming and tedious nature of this…
Machine learning techniques are used for treating jets as images to explore the performance of boosted top quark tagging. Tagging performances are studied in both hadronic and leptonic channels of top quark decay, employing a convolutional…
A search for heavy neutral leptons (HNLs) decaying in the CMS muon system is presented. A data sample is used corresponding to an integrated luminosity of 138 fb$^{-1}$ of proton-proton collisions at $\sqrt{s}$ = 13 TeV, recorded at the…
Machine learning (ML) is shown to predict new alloys and their performances in a high dimensional, multiple-target-property design space that considers chemistry, multi-step processing routes, and characterization methodology variations. A…
Convolutional Neural Nets, which is a powerful method of Deep Learning, is applied to classify equation of state of heavy-ion collision event generated within the UrQMD model. Event-by-event transverse momentum and azimuthal angle…
We describe a method to obtain point and dispersion estimates for the energies of jets arising from b quarks produced in proton-proton collisions at an energy of $\sqrt{s} =$ 13 TeV at the CERN LHC. The algorithm is trained on a large…