Adriano Di Florio

The interest in change detection in the field of remote sensing has increased in the last few years. Searching for changes in satellite images has many useful applications, ranging from land cover and land use analysis to anomaly detection. In particular, urban change detection provides an efficient tool to study urban spread and growth through several years of observation. At the same time, change detection is often a computationally challenging and time-consuming task; therefore, a standard approach with manual detection of the elements of interest by experts in the domain of Earth Observation needs to be replaced by innovative methods that can guarantee optimal results with unquestionable value and within reasonable time. In this paper, we present two different approaches to change detection (semantic segmentation and classification) that both exploit convolutional neural networks to address these particular needs, which can be further refined and used in post-processing workflows for a large variety of applications.

In order to test the computing capabilities of GPUs with respect to traditional CPU cores a high-statistics toy Monte Carlo technique has been implemented both in ROOT/RooFit and GooFit frameworks with the purpose to estimate the statistical significance of the structure observed by CMS close to the kinematical boundary of the Jψϕ invariant mass in the three-body decay B+→JψϕK+. GooFit is a data analysis open tool under development that interfaces ROOT/RooFit to CUDA platform on nVidia GPU. The optimized GooFit application running on GPUs hosted by servers in the Bari Tier2 provides striking speed-up performances with respect to the RooFit application parallelised on multiple CPUs by means of PROOF-Lite tool. The considerably resulting speed-up, while comparing concurrent GooFit processes allowed by CUDA Multi Process Service and a RooFit/PROOF-Lite process with multiple CPU workers, is presented and discussed in detail. By means of GooFit it has also been possible to explore the behaviour of a likelihood ratio test statistic in different situations in which the Wilks Theorem may apply or does not apply because its regularity conditions are not satisfied.

Starting with Run II, future development projects for the Large Hadron Collider will constantly bring nominal luminosity increase, with the ultimate goal of reaching a peak luminosity of 5·1034cm−2s−1 for ATLAS and CMS experiments planned for the High Luminosity LHC (HL-LHC) upgrade. This rise in luminosity will directly result in an increased number of simultaneous proton collisions (pileup), up to 200, that will pose new challenges for the CMS detector and, specifically, for track reconstruction in the Silicon Pixel Tracker. One of the first steps of the track finding work-flow is the creation of track seeds, i.e. compatible pairs of hits from different detector layers, that are subsequently fed to higher level pattern recognition steps. However, the set of compatible hit pairs is highly affected by combinatorial background resulting in the next steps of the tracking algorithm to process a significant fraction of fake doublets. A possible way of reducing this effect is taking into account the shape of the hit pixel cluster to check the compatibility between two hits. To each doublet is attached a collection of two images built with the ADC levels of the pixels forming the hit cluster. Thus the task of fake rejection can be seen as an image classification problem for which Convolutional Neural Networks (CNNs) have been widely proven to provide reliable results. In this work we present our studies on CNNs applications to the filtering of track pixel seeds. We will show the results obtained for simulated event reconstructed in CMS detector, focusing on the estimation of efficiency and fake rejection performances of our CNN classifier.

In order to test the computing capabilities of GPUs with respect to traditional CPU cores a high-statistics toy Monte Carlo technique has been implemented both in ROOT/RooFit and GooFit frameworks with the purpose to estimate the statistical significance of the structure observed by CMS close to the kinematical boundary of the J/ψϕ invariant mass in the three-body decay B+ → J/ψϕK+. GooFit is a data analysis open tool under development that interfaces ROOT/RooFit to CUDA platform on nVidia GPU. The optimized GooFit application running on GPUs hosted by servers in the Bari Tier2 provides striking speed-up performances with respect to the RooFit application parallelised on multiple CPUs by means of PROOF-Lite tool. The considerable resulting speed-up, evident when comparing concurrent GooFit processes allowed by CUDA Multi Process Service and a RooFit/PROOF-Lite process with multiple CPU workers, is presented and discussed in detail. By means of GooFit it has also been possible to explore the behaviour of a likelihood ratio test statistic in different situations in which the Wilks Theorem may or may not apply because its regularity conditions are not satisfied.

GPUs represent one of the most sophisticated and versatile parallel computing architectures that have recently entered in the HEP field. GooFit is an open source tool interfacing ROOT/RooFit to the CUDA platform that allows to manipulate probability density functions and perform fitting tasks. The computing capabilities of GPUs with respect to traditional CPU cores have been explored with a high-statistics pseudo-experiment method implemented in GooFit with the purpose of estimating the local statistical significance of an already known signal. The striking performance obtained by using GooFit on GPUs has been discussed in the previous edition (XII) of this conference. This method has been extended to situations when, dealing with an unexpected new signal, a global significance must be estimated. The LEE is taken into account by means of a scanning/clustering technique in order to consider, within the same background only fluctuation and anywhere in the relevant mass spectrum, any fluctuating peaking behaviour with respect to the background model. The presented results clearly indicate that the systematic uncertainty associated to the method is negligible and that the p-value estimation is not affected by the clustering configuration. A comparison with the evaluation of the global significance provided by the method of trial factors is also provided.

In order to test the computing capabilities of GPUs with respect to traditional CPU cores a high-statistics toy Monte Carlo technique has been implemented both in ROOT/RooFit and GooFit frameworks with the purpose to estimate the statistical significance of the structure observed by CMS close to the kinematical boundary of the J/ψϕ invariant mass in the three-body decay B+ → J/ψϕK+. GooFit is a data analysis open tool under development that interfaces ROOT/RooFit to CUDA platform on nVidia GPU. The optimized GooFit application running on GPUs hosted by servers in the Bari Tier2 provides striking speed-up performances with respect to the RooFit application parallelised on multiple CPUs by means of PROOF-Lite tool. The considerable resulting speed-up, evident when comparing concurrent GooFit processes allowed by CUDA Multi Process Service and a RooFit/PROOF-Lite process with multiple CPU workers, is presented and discussed in detail. By means of GooFit it has also been possible to explore the behaviour of a likelihood ratio test statistic in different situations in which the Wilks Theorem may or may not apply because its regularity conditions are not satisfied.

Graphical Processing Units (GPUs) represent one of the most sophisticated and versatile parallel computing architectures that are currently entering the High Energy Physics field. GooFit is an open source tool interfacing ROOT/RooFit to the CUDA platform on nVidia GPUs that acts as an interface between the MINUIT minimization algorithm and a parallel processor which allows a Probability Density Function to be evaluated in parallel.

Studies of heavy flavour production in pp collisions at the LHC both provide the means to verify the predictions of perturbative quantum chromodynamics (pQCD) and are crucial for an accurate description of background channels in Standard Model (SM) measurements and new physics searches.A wide program of studies of heavy flavour production at the LHC is performed by the ATLAS and CMS collaborations and their recent results in this field are reviewed in this paper.For the CMS collaboration, we report the measurement of differential prompt production cross sections of S-wave quarkonia in pp collisions at p s = 13 TeV and of production cross sections of B + c (! J/yp + ) and B + (! J/yK + ) in pp collisions at p s = 7 TeV.The first observation of resolved c b1 (3P) and c b2 (3P) peaks and the measurement of their masses through their decays to °(3S)g is also reported.For the ATLAS collaboration, recent results in measurements of pair b-quark production and the measurement of quarkonium production in pp collisions at 5.02 TeV are reviewed.Also, differential cross sections and lifetimes measurements are presented for the prompt and non-prompt production of the states X(3872) and y(2S), in their decay mode to J/yp + p in pp collisions at p s = 8 TeV.

Abstract The demand for computational resources is steadily increasing in experimental high energy physics as the current collider experiments continue to accumulate huge amounts of data and physicists indulge in more complex and ambitious analysis strategies. This is especially true in the fields of hadron spectroscopy and flavour physics where the analyses often depend on complex multidimensional unbinned maximum-likelihood fits, with several dozens of free parameters, with the aim to study the internal structure of hadrons. Graphics processing units (GPUs) represent one of the most sophisticated and versatile parallel computing architectures that are becoming popular toolkits for high energy physicists to meet their computational demands. GooFit is an upcoming open-source tool interfacing ROOT/RooFit to the CUDA platform on NVIDIA GPUs that acts as a bridge between the MINUIT minimization algorithm and a parallel processor, allowing probability density functions to be estimated on multiple cores simultaneously. In this article, a full-fledged amplitude analysis framework developed using GooFit is tested for its speed and reliability. The four-dimensional fitter framework, one of the firsts of its kind to be built on GooFit, is geared towards the search for exotic tetraquark states in the [[EQUATION]] decays and can also be seamlessly adapted for other similar analyses. The GooFit fitter, running on GPUs, shows a remarkable improvement in the computing speed compared to a ROOT/RooFit implementation of the same analysis running on multi-core CPU clusters. Furthermore, it shows sensitivity to components with small contributions to the overall fit. It has the potential to be a powerful tool for sensitive and computationally intensive physics analyses.

Abstract The demand for computational resources is steadily increasing in experimental high energy physics as the current collider experiments continue to accumulate huge amounts of data while physicists indulge in more complex and ambitious analysis strategies. This is especially true in the fields of hadron spectroscopy and flavour physics where the analyses often depend on complex multidimensional unbinned maximum-likelihood fits, with several dozens of free parameters, with the aim to study the quark structure of hadrons. Graphics processing units (GPUs) represent one of the most sophisticated and versatile parallel computing architectures that are becoming popular toolkits for high energy physicists to meet their computational demands. GooFit is an upcoming open-source tool interfacing ROOT/RooFit to the CUDA platform on NVIDIA GPUs that acts as a bridge between the MINUIT minimization algorithm and a parallel processor, allowing probability density functions to be estimated on multiple cores simultaneously. In this article, a full-fledged amplitude analysis framework developed using GooFit is tested for its speed and reliability. The four-dimensional fitter framework, one of the firsts of its kind to be built on GooFit, is geared towards the search for exotic tetraquark states in the [[EQUATION]] decays that can also be seamlessly adapted for other similar analyses. The GooFit fitter running on GPUs shows a remarkable speed-up in the computing performance when compared to a ROOT/RooFit implementation of the same, running on multicore CPU clusters. Furthermore, it shows sensitivity to components with small contributions to the overall fit. It has the potential to be a powerful tool for sensitive and computationally intensive physics analyses.

Abstract The demand for computational resources is steadily increasing in experimental high energy physics as the current collider experiments continue to accumulate huge amounts of data and physicists indulge in more complex and ambitious analysis strategies. This is especially true in the fields of hadron spectroscopy and flavour physics where the analyses often depend on complex multidimensional unbinned maximum-likelihood fits, with several dozens of free parameters, with an aim to study the internal structure of hadrons. Graphics processing units (GPUs) represent one of the most sophisticated and versatile parallel computing architectures that are becoming popular toolkits for high energy physicists to meet their computational demands. GooFit is an upcoming open-source tool interfacing ROOT/RooFit to the CUDA platform on NVIDIA GPUs that acts as a bridge between the MINUIT minimization algorithm and a parallel processor, allowing probability density functions to be estimated on multiple cores simultaneously. In this article, a full-fledged amplitude analysis framework developed using GooFit is tested for its speed and reliability. The four-dimensional fitter framework, one of the firsts of its kind to be built on GooFit, is geared towards the search for exotic tetraquark states in the [[EQUATION]] decays and can also be seamlessly adapted for other similar analyses. The GooFit fitter, running on GPUs, shows a remarkable improvement in the computing speed compared to a ROOT/RooFit implementation of the same analysis running on multi-core CPU clusters. Furthermore, it shows sensitivity to components with small contributions to the overall fit. It has the potential to be a powerful tool for sensitive and computationally intensive physics analyses.

Abstract The demand for computational resources is steadily increasing in experimental high energy physics as the current collider experiments continue to accumulate huge amounts of data and physicists indulge in more complex and ambitious analysis strategies. This is especially true in the fields of hadron spectroscopy and flavour physics where the analyses often depend on complex multidimensional unbinned maximum-likelihood fits, with several dozens of free parameters, with an aim to study the internal structure of hadrons. Graphics processing units (GPUs) represent one of the most sophisticated and versatile parallel computing architectures that are becoming popular toolkits for high energy physicists to meet their computational demands. GooFit is an upcoming open-source tool interfacing ROOT/RooFit to the CUDA platform on NVIDIA GPUs that acts as a bridge between the MINUIT minimization algorithm and a parallel processor, allowing probability density functions to be estimated on multiple cores simultaneously. In this article, a full-fledged amplitude analysis framework developed using GooFit is tested for its speed and reliability. The four-dimensional fitter framework, one of the firsts of its kind to be built on GooFit, is geared towards the search for exotic tetraquark states in the $$B^0 \rightarrow J/\psi K \pi$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mi>B</mml:mi> <mml:mn>0</mml:mn> </mml:msup> <mml:mo>→</mml:mo> <mml:mi>J</mml:mi> <mml:mo>/</mml:mo> <mml:mi>ψ</mml:mi> <mml:mi>K</mml:mi> <mml:mi>π</mml:mi> </mml:mrow> </mml:math> decays and can also be seamlessly adapted for other similar analyses. The GooFit fitter, running on GPUs, shows a remarkable improvement in the computing speed compared to a ROOT/RooFit implementation of the same analysis running on multi-core CPU clusters. Furthermore, it shows sensitivity to components with small contributions to the overall fit. It has the potential to be a powerful tool for sensitive and computationally intensive physics analyses.

The interest for change detection in the field of remote sensing has increased in the last few years. Searching for changes in satellite images has many useful applications, ranging from land cover and land use analysis to anomaly detection. In particular, urban change detection provides an efficient tool to study urban spread and growth through several years of observation. At the same time, change detection is often a computationally challenging and time-consuming task, which requires innovative methods to guarantee optimal results with unquestionable value and within reasonable time. In this paper we present two different approaches to change detection (semantic segmentation and classification) that both exploit convolutional neural networks to achieve good results, which can be further refined and used in a post-processing workflow for a large variety of applications.

New results in conventional and exotic spectroscopyAdriano Di Florio Search for resonances decaying to Υ(1 ) + -Quarkonium pair production is an important probe of both perturbative and nonperturbative processes in quantum chromodynamics.In [1] CMS measured the cross section for Υ(1 ) pair production with both mesons decaying to and it also performed a search for a narrow resonance decaying to Υ(1 ) + -.Such a resonance could indicate the existence of a tetraquark that is a bound state of two quarks and two ¯ antiquarks.The search for ¯ ¯ tetraquarks is performed for 17.5 ≤ (4 ) ≤ 19.5 GeV, while the generic search for a narrow resonance is done for 16.5 ≤ (4 ) ≤ 27 GeV.In both cases the Υ(1 ) pair production serves as a reference, since the final state is the same and a similar event selection is used.The measurement of the Υ(1 ) pair production cross section is reported in another conference contribution.The paper relies on protonproton collision data collected at √ = 13 TeV by the CMS detector in 2016 (L = 35.9fb - ).

GPUs represent one of the most sophisticated and versatile parallel computing architectures that have recently been introduced in the High Energy Physics (HEP) field.GooFit is an open source tool interfacing ROOT/RooFit to the CUDA platform that allows to manipulate probability density functions and perform fitting tasks.The striking performances and computing capabilities of GPUs, in comparison to traditional CPU cores, have been exploited in the application of a highstatistics pseudo-experiment method implemented in GooFit, with the purpose of estimating the local or global statistical significance of a physics signal, already known or new respectively.When dealing with an unexpected new signal, a global significance must be estimated to take into account the Look-Elsewhere-Effect and this is accomplished coupling a clustering-based scanning technique to the pseudo-experiments method, also without introducing any relevant systematic uncertainty.By means of these tools it has been possible to investigate the approximation characterizing modern, and currently widely used, statistical methods.In particular two studies have been carried out: 1) the asymptotic behaviour of a likelihood ratio test statistics (Cowan-Cranmer-Gross-Vitells) has been investigated while estimating the local statistical significance of a known signal, 2) the approximation of the Gross-Vitells method (trial factors) has been explored while estimating the global statistical significance of a new signal.These studies have been collected and presented here coherently with a didactic approach.Indeed this work is currently used in lectures about Statistics for Data Analysis.However the presented results can be a useful reference for the confirmation -by means of GPUs -of the validity of few asymptotic formulas/methods now commonly used in HEP.

Abstract The demand for computational resources is steadily increasing in experimental high energy physics as the current collider experiments continue to accumulate huge amounts of data and physicists indulge in more complex and ambitious analysis strategies. This is especially true in the fields of hadron spectroscopy and flavour physics where the analyses often depend on complex multidimensional unbinned maximum-likelihood fits, with several dozens of free parameters, with an aim to study the internal structure of hadrons.Graphics processing units (GPUs) represent one of the most sophisticated and versatile parallel computing architectures that are becoming popular toolkits for high energy physicists to meet their computational demands. GooFit is an upcoming open-source tool interfacing ROOT/RooFit to the CUDA platform on NVIDIA GPUs that acts as a bridge between the MINUIT minimization algorithm and a parallel processor, allowing probability density functions to be estimated on multiple cores simultaneously.In this article, a full-fledged amplitude analysis framework developed using GooFit is tested for its speed and reliability. The four-dimensional fitter framework, one of the firsts of its kind to be built on GooFit, is geared towards the search for exotic tetraquark states in the B 0 → J/ψKπ decays and can also be seamlessly adapted for other similar analyses. The GooFit fitter, running on GPUs, shows a remarkable improvement in the computing speed compared to a ROOT/RooFit implementation of the same analysis running on multi-core CPU clusters. Furthermore, it shows sensitivity to components with small contributions to the overall fit. It has the potential to be a powerful tool for sensitive and computationally intensive physics analyses.

The interest for change detection in the field of remote sensing has increased in the last few years. Searching for changes in satellite images has many useful applications, ranging from land cover and land use analysis to anomaly detection. In particular, urban change detection provides an efficient tool to study urban spread and growth through several years of observation. At the same time, change detection is often a computationally challenging and time-consuming task, which requires innovative methods to guarantee optimal results with unquestionable value and within reasonable time. In this paper we present two different approaches to change detection (semantic segmentation and classification) that both exploit convolutional neural networks to achieve good results, which can be further refined and used in a post-processing workflow for a large variety of applications.