An analysis of the influence of background subtraction and quenching on jet observables in heavy-ion collisions

Subtraction of the large background in reconstruction is a key ingredient in jet studies in high-energy heavy-ion collisions at RHIC and the LHC. Here we address the question to which extent the most commonly used subtraction techniques are able to eliminate the effects of the background on the most commonly discussed observables at present: single inclusive jet distributions, dijet asymmetry and azimuthal distributions. We consider two different background subtraction methods, an area-based one implemented through the FastJet pack- age and a pedestal subtraction method, that resemble the ones used by the experimental collaborations at the LHC. We also analyze different ways of defining the optimal parame- ters in the second method. We use a toy model that easily allows variations of the background characteristics: average background level and fluctuations and azimuthal structure, but cross- checks are also done with a Monte Carlo simulator. Furthermore, we consider the influence of quenching using Q-PYTHIA on the dijet observables with the different background subtrac- tion methods and, additionally, we examine the missing momentum of particles. The average background level and fluctuations affect both single inclusive spectra and dijet asymmetries, although differently for different subtraction setups. A large azimuthal modulation of the background has a visible effect on the azimuthal dijet distributions. Quenching, as imple- mented in Q-PYTHIA, substantially affects the dijet asymmetry but little the azimuthal dijet distributions. Besides, the missing momentum characteristics observed in the experiment are qualitatively reproduced by Q-PYTHIA.Comment: 29 pages, 43 figures Accepted by JHE

Evolving Spatio-temporal Data Machines Based on the NeuCube Neuromorphic Framework: Design Methodology and Selected Applications

The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM). These are multi-modular computer systems designed to deal with large and fast spatio/spectro temporal data using spiking neural networks (SNN) as major processing modules. ECOS and eSTDM in particular can learn incrementally from data streams, can include ‘on the fly’ new input variables, new output class labels or regression outputs, can continuously adapt their structure and functionality, can be visualised and interpreted for new knowledge discovery and for a better understanding of the data and the processes that generated it. eSTDM can be used for early event prediction due to the ability of the SNN to spike early, before whole input vectors (they were trained on) are presented. A framework for building eSTDM called NeuCube along with a design methodology for building eSTDM using this are presented. The implementation of this framework in MATLAB, Java, and PyNN (Python) is presented. The latter facilitates the use of neuromorphic hardware platforms to run the eSTDM. Selected examples are given of eSTDM for pattern recognition and early event prediction on EEG data, fMRI data, multisensory seismic data, ecological data, climate data, audio-visual data. Future directions are discussed, including extension of the NeuCube framework for building neurogenetic eSTDM and also new applications of eSTDM

Personalised modelling with spiking neural networks integrating temporal and static information.

This paper proposes a new personalised prognostic/diagnostic system that supports classification, prediction and pattern recognition when both static and dynamic/spatiotemporal features are presented in a dataset. The system is based on a proposed clustering method (named d2WKNN) for optimal selection of neighbouring samples to an individual with respect to the integration of both static (vector-based) and temporal individual data. The most relevant samples to an individual are selected to train a Personalised Spiking Neural Network (PSNN) that learns from sets of streaming data to capture the space and time association patterns. The generated time-dependant patterns resulted in a higher accuracy of classification/prediction (80% to 93%) when compared with global modelling and conventional methods. In addition, the PSNN models can support interpretability by creating personalised profiling of an individual. This contributes to a better understanding of the interactions between features. Therefore, an end-user can comprehend what interactions in the model have led to a certain decision (outcome). The proposed PSNN model is an analytical tool, applicable to several real-life health applications, where different data domains describe a person's health condition. The system was applied to two case studies: (1) classification of spatiotemporal neuroimaging data for the investigation of individual response to treatment and (2) prediction of risk of stroke with respect to temporal environmental data. For both datasets, besides the temporal data, static health data were also available. The hyper-parameters of the proposed system, including the PSNN models and the d2WKNN clustering parameters, are optimised for each individual

Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays.

Resistive RAM crossbar arrays offer an attractive solution to minimize off-chip data transfer and parallelize on-chip computations for neural networks. Here, we report a hardware/software co-design approach based on low energy subquantum conductive bridging RAM (CBRAM®) devices and a network pruning technique to reduce network level energy consumption. First, we demonstrate low energy subquantum CBRAM devices exhibiting gradual switching characteristics important for implementing weight updates in hardware during unsupervised learning. Then we develop a network pruning algorithm that can be employed during training, different from previous network pruning approaches applied for inference only. Using a 512 kbit subquantum CBRAM array, we experimentally demonstrate high recognition accuracy on the MNIST dataset for digital implementation of unsupervised learning. Our hardware/software co-design approach can pave the way towards resistive memory based neuro-inspired systems that can autonomously learn and process information in power-limited settings

Bio-mimetic Spiking Neural Networks for unsupervised clustering of spatio-temporal data

Spiking neural networks aspire to mimic the brain more closely than traditional artificial neural networks. They are characterised by a spike-like activation function inspired by the shape of an action potential in biological neurons. Spiking networks remain a niche area of research, perform worse than the traditional artificial networks, and their real-world applications are limited. We hypothesised that neuroscience-inspired spiking neural networks with spike-timing-dependent plasticity demonstrate useful learning capabilities. Our objective was to identify features which play a vital role in information processing in the brain but are not commonly used in artificial networks, implement them in spiking networks without copying constraints that apply to living organisms, and to characterise their effect on data processing. The networks we created are not brain models; our approach can be labelled as artificial life. We performed a literature review and selected features such as local weight updates, neuronal sub-types, modularity, homeostasis and structural plasticity. We used the review as a guide for developing the consecutive iterations of the network, and eventually a whole evolutionary developmental system. We analysed the model’s performance on clustering of spatio-temporal data. Our results show that combining evolution and unsupervised learning leads to a faster convergence on the optimal solutions, better stability of fit solutions than each approach separately. The choice of fitness definition affects the network’s performance on fitness-related and unrelated tasks. We found that neuron type-specific weight homeostasis can be used to stabilise the networks, thus enabling longer training. We also demonstrated that networks with a rudimentary architecture can evolve developmental rules which improve their fitness. This interdisciplinary work provides contributions to three fields: it proposes novel artificial intelligence approaches, tests the possible role of the selected biological phenomena in information processing in the brain, and explores the evolution of learning in an artificial life system

Medium response in JEWEL and its impact on jet shape observables in heavy ion collisions

Realistic modeling of medium-jet interactions in heavy ion collisions is becoming increasingly important to successfully predict jet structure and shape observables. In JEWEL, all partons belonging to the parton showers initiated by hard scattered partons undergo collisions with thermal partons from the medium, leading to both elastic and radiative energy loss. The recoiling medium partons carry away energy and momentum from the jet. Since the thermal component of these recoils' momenta is part of the soft background activity, comparison with data requires the implementation of a subtraction procedure. We present two independent procedures through which background subtraction can be performed and discuss the impact of the medium recoil on jet shape observables. Keeping track of the medium response significantly improves the JEWEL description of jet shape measurements.Comment: 23 pages, 16 figure