Related papers: Simulation study of particle identification using …
Single-ion mass identification is important for atomic and nuclear physics experiments on ions produced with low yields. Cooling the ion to ultra-low temperatures by interacting with a laser-cooled ion will enhance the precision of the…
Clustering is an important exploratory data analysis technique to group objects based on their similarity. The widely used $K$-means clustering method relies on some notion of distance to partition data into a fewer number of groups. In the…
Tracker detectors can be used to identify charged particles based on their global chi value obtained during track fitting with the Kalman filter. This approach builds upon the knowledge of detector material and local position resolution,…
Gas-phase oxygen-rich iron oxide clusters Fe(O2)n+ (n=1-6), are produced in a molecular beam apparatus. Their stability and structure are investigated by measuring the fragmentation cross sections from collision-induced-dissociation…
The size and structure of spatial molecular and atomic clustering can significantly impact material properties and is therefore important to accurately quantify. Ripley's K-function (K(r)), a measure of spatial correlation, can be used to…
We present improved measurements of the differential production rates of stable charged particles in hadronic Z0 decays, and of charged pions, kaons and protons identified over a wide momentum range using the SLD Cherenkov Ring Imaging…
In the era of Big Data, scalable and accurate clustering algorithms for high-dimensional data are essential. We present new Bayesian Distance Clustering (BDC) models and inference algorithms with improved scalability while maintaining the…
Resonances play an important role in Standard Model phenomenology. In particular, hadronic resonances feature in $B$ and $D$ decays, which can be central for New Physics searches. Lattice QCD simulations combined with the finite-volume…
Using a test TPC, consisting of the ALICE TPC field cage prototype in combination with the final ALICE TPC readout and electronics, the energy loss distribution and resolution were measured for identified protons. The measurements were…
Identified particle spectra represent a crucial tool to understand the behavior of the matter created in high-energy heavy-ion collisions. The transverse momentum p_T distributions of identified hadrons contain informations about the…
In this work, we introduce a novel method for Particle Identification (PID) within the scope of the ALICE experiment at the Large Hadron Collider at CERN. Identifying products of ultrarelativisitc collisions delivered by the LHC is one of…
Coulomb crystals -- ordered structures of cold ions confined in ion traps -- find applications in a variety of research fields. The number and temperature of the ions forming the Coulomb crystals are two key attributes of interest in many…
Since 2015, with the restart of the LHC for its second run of data taking, the LHCb experiment has been empowered with a dedicated computing model to select and analyse calibration samples to measure the performance of the particle…
Track finding and fitting algorithm in the ALICE Time projection chamber (TPC) based on Kalman-filtering is presented. Implementation of particle identification (PID) using d$E$/d$x$ measurement is discussed. Filtering and PID algorithm is…
Particle identification using the energy loss in silicon detectors is a powerful technique for probing the Standard Model (SM) as well as searching for new particles beyond the SM. Traditionally, such techniques use the truncated mean of…
A novel experimental scheme has been developed in order to measure the heat capacity of mass selected clusters. It is based on controlled sticking of atoms on clusters. This allows one to construct the caloric curve, thus determining the…
Particle identification in large high-energy physics experiments typically relies on classifiers obtained by combining many experimental observables. Predicting the probability density function (pdf) of such classifiers in the multivariate…
We describe a scalable, high-speed, and robust architecture for measurement-based quantum-computing with trapped ions. Measurement-based architectures offer a way to speed-up operation of a quantum computer significantly by parallelizing…
In both high-energy physics and industry applications, a crowd-sourced approach to difficult problems is becoming increasingly common. These innovative approaches are ideal for the development of future facilities where the simulations can…
The problem of estimating the number of clusters (say k) is one of the major challenges for the partitional clustering. This paper proposes an algorithm named k-SCC to estimate the optimal k in categorical data clustering. For the…