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Novel approaches of data-mining in experimental physics

G.A.Ososkov, Laboratory of Information Technologies

Joint Intstitute for Nuclear Research, 141980 Dubna, Russiaemail: ososkov@jinr.ru

http://www.jinr.ru/~ososkov

XXIVXXIV International Symposium on International Symposium on Nuclear Electronics & ComputingNuclear Electronics & ComputingVarna, 09-16 September, 2013. Varna, 09-16 September, 2013.

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Data mining concept Classical approaches for processing experimental data supposed

to have a model describing a physical system and based on the advanced physical theory. Then observed data are used to verify the underlying models and to estimate its direct or indirect parameters.

Now, when the experimental data stream is terabytes/sec, we come to the BIG DATA erathe BIG DATA era, having often a lack of the corresponding lack of the corresponding theory,theory, our data handling paradigm shifts from classical modeling and data analyses to developing models and the corresponding analyses directly from datadirectly from data. (Data-driven detector alignment as an example)

The entire process of applying a computer-based methodology, including new techniques, for discovering knowledge from data is called data miningdata mining.

Wikipedia: “-it is the analysis of large amounts of data about experimental results held on a computer in order to get information about them that is not immediately available or obvious.”

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Data mining methodsIt is the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management.

Data mining Data mining commonly involves four classes of tasks: Association rule learning – searches for relationships between variables. Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data. Classification – is the task of generalizing known structure to apply to new data. Regression – attempts to find a function which models the data with the least error

Although Data Mining Methods (DMM) are oriented mostly on mining business and social science data, in recent years, data mining has been used widely in the areas of science and engineering, such as bioinformatics, genetics, medicine, education and electrical power engineering. So a great volume of DMM software is now exists as open-source and commercial. However one would not find experimental physics in DMM application domains.

Therefore we are going to understand how DMM would look Therefore we are going to understand how DMM would look like, if data will be taken from high energy physics?like, if data will be taken from high energy physics?

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Data mining peculiarities for experimental high energy physics (HEP)

Let us consider some examples

schematic view of the СВМ setup

1.1. СВМ СВМ experimentexperiment (Germany, GSI, to be running in 2018)107 events per sec, ~1000 tracks per event~100 numbers per trackTotal: terabytes/sec !Total: terabytes/sec !

RICH - Cherenkov radiation detector

simulated event of the central Au+Au collision in the vertex detector

Our problem is to recognize all of these rings and evaluate their parameters despite of their overlapping, noise and optical shape distortions

view of Cherenkov radiation rings registered by the CBM RICH detector

CondensedBarionMatter

RICH

TRD

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2. Transition radiation detector (TRD)TRD measurements allow for each particle to reconstruct its 3D track and calculate its energy loss (EL) during its passage through all 12 TRD stations in order to distinguish electrons e- from pions π± . Unlike π±, electrons generate additionally the transition radiation (TR) in TRD.

TR Production

Our problemOur problem is to use the distributions of EL+TR for e- and π± in order to test hypothesis about a particle attributingtest hypothesis about a particle attributing to one of these alternatives keeping the probability α of the 1st kind of error on the fixed level α =0.1 and the probability β of the 2nd kind of error on the level less than β< 0.004.

Both distributions were simulated by special program GEANT-4 taking into account all details of the experimental setup and corresponding physical assumptions related to heavy ion collisions. However the test based on direct cut on the sum of energy losses could not satisfy these requirements because both EL and TR have long-tailed Landau distributions.

The main lesson: a transformation needed to reduce Landau tails of ELa transformation needed to reduce Landau tails of EL

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Example 3: the OPERA experiment

OPERA

CNGS

CERN

LNGS

730 km

1600 m in depth

~100’000 m3 caverns’ volume

LNGS: the worldLNGS: the world largest underground physics laboratoryNeutrino beam

Search for neutrino oscillations

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Each wall is accompanied by two planes of electronic trackers made of scintillator strips

The crucial issue in OPERA is finding of that particular brick where the neutrino interaction takes place. Tracks formed by scintillator hits should originate from a single point - vertex. However the main obstacle is back-scattered particles (BSP) occuring in 50% of events, which do not contain useful information.Emulsion scanning to determine neutrino oscillation – is the separate task out of this talk

the OPERA experiment: Search for neutrino oscillations (OPERA is running)

BSP

Two types of OPERA events with BSP

Real vertex

Hadron shower axixHadron shower axix

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The particular features of the data from these detectors are as follows: data arrive with extremely high rate; recognized patterns are discrete and have complex texture; very high multiplicity of objects (tracks, Cherenkov radiation rings,

showers) to be recognized in each event; the number of background events, which are similar to “good”

events, is larger than the number of the latter events by several orders of magnitude;

noise counts are numerous and correlated.——————————————————————————————--—————-

The basic requirements to data processing in current experiments are:maximum speed of computing in combination with the highestattainable accuracy and high efficiency of methods of estimatingphysical parameters interesting for experimentalists.

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To understand the need for analyses of large, complex, information-rich data sets in HEP let us start from let us start from considering considering stages of HEP data processingstages of HEP data processing

1. Pre-processing is very important stage. It includes: Data acquisition: before data mining algorithms can be used, a

target data set must be assembled and converted from the rough format of detector counters into natural unit format.

Data Transformation: to transform data into forms appropriate for mining, they must be corrected from detector distortions and misalignment by special calibration and alignment transformation procedures.

Data selection: then data must be cleaned to remove noisy, inconsistent and other observations, which do not satisfy acceptance conditions. It can be accomplished in a special often quite sophisticate triggering procedure that usually causes a significant reduction of target data (several orders of magnitude).

Data mining in experimental HEP 1.

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2. HEP data processing involves following stages and methods: Pattern recognition: hit detection, tracking, vertex finding, revealing Cherenkov rings , fake objects removing etc employing the following methods:

Cluster analysisHough transform Kalman filterNeural networksCellular automataWavelet analysis

Physical parameters estimation - robust M-estimations Hypothesis testing - Likelihood ratio test - Neural network approach - Boosted decision trees

The next expounding will besome retrospections of the JINR experience to illustrate HEP data processing steps

Data mining in experimental HEP 2.

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Data mining in experimental HEP 3. Monte-Carlo simulationsMonte-Carlo simulations are used on all stages and

allow to - accomplish in advance the experimental designexperimental design of a hardware setup and data

mining algorithms and optimize them from money, materials and time point of view; - develop needed software frameworksoftware framework and test it; - optimize structure and needed equipmentoptimize structure and needed equipment of planned detectors minimizing

costs, timing with a proposed efficiency and accuracy; - calculate in advance all needed distributions or thresholds for goodness-of-fit

tests; Parallel programming of optimized algorithmsParallel programming of optimized algorithms is inevitable inevitable Software quality assuranceSoftware quality assurance (SQA) is the very important issue (SQA) is the very important issue

of any great programming system developmentof any great programming system development GRID technologiesGRID technologies changed considerably HEP data

processing stages, which now more and more correspond to the GRID Tier hierarchy.

Since each of theses items needs a long separate Since each of theses items needs a long separate expounding, they will be only briefly noted belowexpounding, they will be only briefly noted below

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Some retrospectionsSome retrospections1.1. Artificial Neural NetworksArtificial Neural Networks Why ANN for a contemporary HEP experiment? historically namely physicists wrote in 80-ties one of the first NN

programing packages – Jetnet. They were also among the first neuro-chip users

after being trained ANN is one of the most appropriate tools for implementing many of data handling tasks, while on the basis of some new physical model physicists have possibility to generate training samples of any arbitrary needed length by Monte Carlo

appearance of TMVA - Toolkit for Multivariate Data Analysis with TMVA - Toolkit for Multivariate Data Analysis with ROOTROOT

Thus, there are many real problems solved on the basis of ANN in experimental physics as

- Object recognizing and classifying - Statistical hypothesis testing - Expert system implementing - Approximation of many-dimensional functions - Solution of non-linear differential equations - etc

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A sketch of the RICH detector

Data processing stages:Ring recognition and their parameters

evaluation;Compensating the optical distortions lead to

elliptic shapes of rings; Matching found rings with tracks of particles

which are interesting to physicistsEliminating fake rings which could lead to wrong

physical conclusionsAccomplishing the particle identification with

the fixed level of the ring recognition efficiency

A fragment of photodetector plane. In average there are 1200 points per event forming 75 rings.

ElectronsElectrons

PionsPions

Radius versus momentum for reconstructed rings.

NN application examples: 1. RICH detector

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NN for the RICH detectorThe study has been made to select the most informative ring features needed to distinguish between good and fake rings and to distinguish between good and fake rings and to identify electronsidentify electrons. Ten of them have been chosen to be input to ANNs, they are:1.Number of points in the found ring 2.Its distance to the nearest track 3.The biggest angle between two neighbouring points 4.Number of points in the narrow corridor surrounding ring 5.Radial ring position on the photodetector plane 6.χ2 of ellipse fitting7.Both ellipse half-axes (A and B)8.angle φ of the ellipse inclination to abscissa 9.track azimuth10. track momentum

Two samples with 3000 e (+1)and 3000 π (-1)have been simulated to train NN. Electron recognition efficiency was fixed on 90%Probabilities of the 1-st kind error 0.018and the 2-d kind errors 0.0004 correspondingly were obtained

electrons

π-mesons40000 e and π rings to train

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NN application examples: 2. e- / π± separation by transition radiationTwo ideas to avoid obstacles with the easy cut test and long tails of energy loss (ΔE) distributions:1. Apply artificial neural network for testing2. Calculate likelihood ratio for ΔE of each TRD station as input to ANN

We use Monte-Carlo calculations to simulate a representative sample of TRD signals for given experimental conditions and then obtain energy losses from all n TRD stations for both e- and π± , sort them and calculate probability density functions (PDF) for ordered ΔEs. Then we repeat simulation in order to train neural network with n inputs and one output neuron, which should be equal +1 in case of electron and and -1 in case of pion. As inputs, the likelihood ratios for each ΔE were calculated

)()(

)(

EPDFEPDF

EPDFL

e

The result of testing the trained neural network gave the probability of the 2nd kind of error β= 0.002It satisfied the required experimental demands.

It is interesting to noteIt is interesting to note: Applying Busted decision Trees algorithm from TMVA allows to improve pion suppression result up to 15-20% comparing to NN

ANN output distribution

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NN application examples: 3. OPERA experiment

To facilitate the vertex location a considerable data preprocessing has been fulfilled in order to eliminate or, at least, reduce electronic noise. The method was based on cellular

automaton that rejects points having no nearest neighbours;Reconstruct muon tracks (Hough transform, Kalman filter) M-estimate hadron shower axis with 2D robust weights taking into account not only

distance of a point to the shower axis, but also amplitudes of scintillator signalsmake a study to determine 15 parameters to input them to ANN

.

According to 3 classes of events 3 neural networks of MLP type were then trained for each class on 20000 simulated events to make a decision about the wall with the event vertex. The wall finding efficiency on the level of 80 – 90% was then calculated by testing 10000 events.

NN results were then used in the brick finding procedure.

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Hopfield’s theorem: the energy function E(s) = - ½ Σij si wij sj

of a recurrent NN with the symmetrical weight matrix wij = wji , wii = 0

has local minima corresponding to NN stability points

Applications in JINR1. Track recognition by Denby- Peterson (1988) segment model with modifications was successfully used for tracking in the EXCHARM experiment

2. More rareMore rare: track recognition by rotor models of Hopfield networks

Recurrent ANNs and applicationsRecurrent ANNs and applications

The energy function: the first term forces neighbouring rotors to be close to each other. The second term is in charge of the same between rotors and track-segments.

Er

v vr

v rij

m i j

ij

mij

i ijij

1212

21 1 ( )

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Our innovations Our innovations (I.Kisel, 1992) j j ij jv v W vij ij

ij ij

cos sin

sin cos

2 2

2 2

E v vi j

ij

1

2

This approach has been applied in the ARES experiment with some extra efforts:

Therefore we obtain a simple energy function without any constrains

Analysis of ionograms. Up to now the corresponding program is in use in the Irkutsk Institute of the terrestrial magnetism, Russia and in the Lowell University, MA, USA

- prefiltering by cellular automaton; - local Hough algorithm for initial rotor set up; - special robust multipliers for synaptic weights.

Results: recognition efficiency - 98%

Data from the vertical sounding of the ionosphere

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Elastic neural networksANN drawbacks revealed by physicists in many HEP applications: ▪ too slow convergence of the ANN evolution due to too high degrees of freedom; ▪ only recognition is fulfilled without taking into account the known track model; ▪ over-sensitivity of ANNs to noise is indicated.Therefore it was suggested to combine both stages: recognition and fitting of a track in one procedure when deformable templates (elastic arm) formed by equations of particle motion are all bended in order to overlaid the data from the detector. A routine then has to evaluate whether or not the template matched a track. Ohlsson and Peterson (O&P, 1992 ) from the Lund University realized this idea as a special Hopfield net with the energy function depending from helix parameters describing a track and binary neurons Sia, each of them is equal to 1 or 0 when i-th point belongs or not to the a-th track, respectively. Gyulassy and Harlander (G & H, 1991) proposed their elastic tracking that can physically be described as interaction between the positively charged template and negatively charged spatial points measured in the track. The better the elastic template fits points, the lower the energy of their interaction.

Using Lorenz potential Using Lorenz potential with the time-dependent width where a is the maximal distance, at which points are still accredited to this template, b<< a is spatial resolution of a detector, G & H obtained the energy to be obtained the energy to be minimized by helix parameters minimized by helix parameters ππ

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Elastic neural networks applications

To avoid E(π,t) getting caught in local spurious minima the simulated annealing iterative procedure is applied. On the first iteration w(t) is taken for the highest temperature, when E(π,t) has the only one minimum. Then w(t) is narrowed gradually allowing more and more accurate search of the global minimum.

G&H elastic tracking was applied for the STAR TPC simulated data with remarkably high track-finding efficiency (1998)

O&P e elastic NNs after corresponding modifications were succesfully applied for Cherenkov ring search and track reconstructing (1997). Drift chamber tracks with their left-right ambiguity in magnetic field demanded to invent 2D neurons Si=(si

+, si —) to determine a point to a track accreditation (1998)

Important to note: a homogeneous magnetic field of NICA-MPD project will make it possible to apply this elastic arm approach for MPD TPC tracking

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Some retrospectionsSome retrospections2. 2. Robust estimates for heavy Robust estimates for heavy ccontaminated samplesontaminated samplesWhy robust estimates?In all preceeding experimental examples we must solve typical statistics problems of parameter estimations by sets of measured data.However we faced with not usual applied statistics, but with special mass production statisticsKeywords are: heavy data contaminationheavy data contamination due to

noisy measurements; measurements from neighbour objects.

need in very fast algorithmsvery fast algorithms of hypothesis testing and parameter estimating Comparison of LSF and robust fit in case of one point outlier

How to achieve that?- Robust approach, based on functional weights of each measuremet, preferably parallel algorithms

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M-estimate formalizmInstead of LSF with its crucial crucial assumption of residual normalityassumption of residual normality and quadratic and quadratic nature of minimized functionalnature of minimized functional we consider P.Huber’s M-estimate, i.e. replace quadratic functional S(p) to be minimized by L(p,σ)=Σi ρ(εi ), where measurement error ε is distributed according to J.Tukey's gross-error model f(ε) = (1-c) φ(ε) + c h(ε), c is a parameter of contamination, φ(ε) is the Gauss distribution and h(ε)is some long-tailed noise distribution density.Likelihood equation for the functional L(p,σ)

by denoting

can be modified to the form

which is similar to the normal LSF equations, but with replacement of the numerical weight coefficients to weight functions w(ε) to be recalculated on each step of an iterative procedure.

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How to choose the weight function w(ε) ?For a particular, but important case of the uniform contamination h(ε)=h0 we found the optimal weights w(ε)

which polynomial expansion of up to the fourth order leads to the approximation

It is the famous Tukey's bi-weights, which are easier to calculate than optimal ones.

Simulated annealing procedure is used to avoid sticking functional in local minima.

Recall the energy function of G&H elastic trackingRecall the energy function of G&H elastic trackingLorentz potential in this sum plays a role of the Lorentz potential in this sum plays a role of the robust functional weight. robust functional weight.

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Application examples: 1. Determination of the interaction vertex position for only two coordinate planes (NA-45)

One of two silicon disk with 1000 track and noise hits.One of two silicon disk with 1000 track and noise hits.

SoSo, , it is impossible to recognize individual tracks. it is impossible to recognize individual tracks. Tukey biweight function with cTukey biweight function with cT=3 was used. =3 was used.

Iterational procedure converged in five iterations Iterational procedure converged in five iterations with the initial approximation taken as the middle of with the initial approximation taken as the middle of Z-axis target region. Z-axis target region. The results after processing 4000 Pb+Au events The results after processing 4000 Pb+Au events provide satisfactory accuracy of 300 μ along Z-axis provide satisfactory accuracy of 300 μ along Z-axis and good local accuracy of a trackand good local accuracy of a track..

The target consists of eight 25-μ gold discs. The target consists of eight 25-μ gold discs.

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Application examples: 2.TDC calibration problem (HERA-B)It is caused by the fact that real track detectors, as drift chambers, for example, are measuring the drift time in TDC (Time-Digital Converter) counts. So to perform data processing, TDC counts are to be transferred, first of all, into drift radii. Such a transformation named calibration is inevitably data-driven, i.e. is carried out statistically from real TDC data of some current physical run. Here is an impressive example of the effectiveness of the robust approach. The fitting problem in such cases is radically different from any common one, since

for every abscissa we have not one, but many ordinates with different amplitudes. Therefore every point to be fitted was provided by 2D weight depending as of this point distance to the fitted curve, as of its amplitude. It is shown how a calibration function r(t) can be obtained by fitting cubic splines to directly 2D histogram of drift radii versus TDC counts, which consists of many thousand bins with various amplitudes. The fitted spline only for upper part is shown.

A lot of more applications were reported, in particular, for tracking in presence A lot of more applications were reported, in particular, for tracking in presence of of δδ-electrons in CMS -electrons in CMS muon endcupmuon endcup

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Some retrospectionsSome retrospections 3.Wavelet analysis3.Wavelet analysisWhat are continuous wavelets?What are continuous wavelets?In contrast to the most known mean of signal analysis as Fourier transformFourier transform, one-dimensional wavelet transform (WT) of the signal f(x) has 2D form2D form , where the function is the wavelet, b is a displacement (time shift), and a is a scale (or frequency). Condition Cψ < ∞ guarantees the existence of and the wavelet inverse transform. Due to the freedom in choice, many different wavelets were invented. The family of continuous wavelets with vanishing momenta is presented here by Gaussian wavelets, which are generated by derivatives of Gaussian function

Most known wavelet G2

is named “the Mexican hat”

The biparametric nature of wavelets renders it possible to analyze simultaneously both time and frequency characteristics of signals.

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Wavelets can be applied for extracting very special features of mixed and contaminated signal

G2 wavelet spectrum of this signal

Filtering results. Noise is removed and high frequency part perfectly localized.

NOTE: that is impossible by Fourier transformNOTE: that is impossible by Fourier transform

An example of the signal with a localized high frequency part and considerable contamination

then wavelet filtering is applied

Filtering works in the wavelet domain bythresholding of scales, to be eliminatedor extracted, and then by making the inverse transform

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Continuous wavelets: pro and contraPRO: - Using wavelets we overcome background estimation

- Wavelets are resistant to noise (robust)

CONTRA: - redundancy → slow speed of calculations - nonorthogonality (signal distotres after inverse transform!)

Besides, real signals to be analysed by computer are discrete, in principle

So orthogonal discrete wavelets should be preferable.

Denoising by DWT shrinkingshrinkingwavelet shrinkage means, certain wavelet coefficients are reduced to zero:

Our innovationOur innovation is the adaptive shrinkage, i.e. λk= 3σk where k is decomposition level (k=scale1,...,scalen), σk is RMS of Wψ for this level (recall: sample size is 2n) Small peak finding with coifletsSmall peak finding with coiflets

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NEW: NEW: Back to continuous waveletsBack to continuous wavelets

Peak parameter estimating by gaussian waveletsWhen a signal is bell-shaped one, it can be approximated by a gaussian

.

Then it can be derived analytically that its wavelet its wavelet transformation looks as the corresponding wavelet with transformation looks as the corresponding wavelet with parameters depending of the original signal parameters.parameters depending of the original signal parameters. Thus, we can calculate them directly in the wavelet domain instead of time/space domain.

The most remarkable point is, we do not need the inverse we do not need the inverse transform!transform!

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Estimating peak parameters in GEstimating peak parameters in G22 wavelet domain wavelet domainHow it works?How it works?Let us have a noisy Let us have a noisy invariant mass spectruminvariant mass spectrum

1.1. transform it by Gtransform it by G2 2 into wavelet domain into wavelet domain

2.2. 2. look for the wavelet 2. look for the wavelet surface surface maximum maximum

2

322

2

5 )ˆˆ(

ˆˆ

maxˆ

a

a

WA

bmax ,amax . 3. From the formula for WG2(a,b;x0,σ)g one can derive analyticalexpressions for its maximum x0 and . which should correspond to the found bmax ,amax . Thus we can use coordinates of the maximum as estimations of wanted peak parameters

ax ˆ,ˆ04. From them we can directly obtain halfwidth

amplitude

and even the integral 2AI

peak has bell-shape form

5

ˆˆ

a

5max a

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Application results to CBM invariant mass spectraApplication results to CBM invariant mass spectraLow-mass dileptons (muon channel) ω. Gauss fit

of reco signalM=0.7785σ =0.0125A=1.8166Ig=0.0569

ω. WaveletsM=0.7700σ =0.0143A=1.8430Iw=0.0598

- ω– wavelet spectrum

ω.

ω-meson

φ-meson

Even φ- and mesons have been visiblein the wavelet space, so we could extract their parameters.

Thanks to Anna Kiseleva

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NEW:NEW: Example with set of FOPI dataExample with set of FOPI data

Despite of the very jagged spectrum wavelets give visible peaks with σ1 = σ3 = 0.013, σ2 = 0.021

provided by N.Hermann, GSI, Darmstadt, Germany GSI, Darmstadt, Germany

Wavelets G4 are used. The formula for σ obtaining is σ = amax/3

12 3

noise level σ = 0.009

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Clustering in data miningClustering in data mining Clustering is one of important DM task because it allows to seek groups

and structures in the data that are in some way "similar", without using known structures in the data.

Clustering methods are widely used in HEP data processing to find the point of particle passage through coordinate plane of some cell-structure detector

NewNew application application of clustering analysis allows to develop the URQMD fragmentation model of nuclear collision at relativistic energies. Clusters or nuclear fragments are

generated via dynamical forces between nucleons during their evolution in coordinate and momentum space

NewNew two steps clusteringtwo steps clustering method is proposed for BIG DATA. It accomplishes the quantization of input data by generating so-called Voronoi Voronoi partitionpartition. The final clustering is done using any conventional methods of clustering. A new promising watershed clustering algorithmwatershed clustering algorithm is proposed.

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Parallel programmingParallel programming Fortunately the common structure of HEP experimental data naturally

organized as the sequence of events gives the possibility for the natural multithread parallelism by handling events simultaneously on different processors.

However the requirements of such experiments as the CBM to handle terabytes of data per second leads to the necessity of parallelism parallelism on the on the level of each eventlevel of each event by so-called SIMDization of algorithms, that demands their substantial optimization and vectoring of input data.

For instance, in case of CBM TRD and MuCh tracking algorithms we obtain

Resulting speedup of the track fitterResulting speedup of the track fitter on the Computer with 2xCPUs Intel Core i7 (8 cores in total) at 2.67 GHz

Time [μs/track] Speedup

Initial 1200 -

Optimization 13 92

SIMDization 4.4 3

Multithreading 0.5 8.8

Final 0.5 2400

Throughput: 2*106 tracks/s

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Software quality assurance (SQA)Software quality assurance (SQA) Since software framework of any contemporary HEP experiment is developed by

efforts of international team from thousand collaborants with various programming skills, software components, they wrote, can inevitably have bags, interconnection errors or output result different from specified before.

Therefore automation of experimental framework software testing is needed to provide the following:

- More reliable software, speedup of its developmentMore reliable software, speedup of its development - Reduce development cycles - Continues integration and deployment - High code coverage to test, ideally, all code in the repository

- Not only unit testing but also system test for simulation and reconstruction However known SQA systems could not be applied directly for these purposes,since

they are based on the theory of reliability methods and suppose to have a highly qualified team of programmers and testing with immediate failure repairing, while the most software in our experimental collaborations are written by physicists who are not highly qualified in programming and are not able to watch over immediate failure repairing

For discussionFor discussion an automatic test system for HEP experiment should performan automatic test system for HEP experiment should perform: Report generation for simulation studies; Automatic check of output results based on predefined values; Nightly monitoring of the simulation results; Designed to be modular in order to easy extend and add new histograms

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Conclusion and outlook remarks Importance of advanced Monte-Carlo simulations Robust estimates, neural networks and wavelet applications are

really significant for data-mining in HEP

It looks reasonable to provide wavelet analysis tools in ROOT The focus of developing data mining algorithms in HEP is shifted to

their optimization and parallelization in order to speed them up considerably while keeping their efficiency and accuracy

Parallelism is to be introduced inevitably on the basis of new technologies of computing and software

Software reliability concept is very essential Distributed or cloud computing are growing. In HEP it is

accomplished by GRID technologies

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Thanks for your attention!Thanks for your attention!

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SQA example for FAIR GSISQA example for FAIR GSISQA general structure1.1. Histogram CreatorHistogram Creator. It realizes the management of large number of

histograms2. Drawer. Feature extractor. Report generator. Result checker2. Drawer. Feature extractor. Report generator. Result checker. They provide - Base classes for simulation and study report generation;

- Base functionality for histogram drawing; - Base functionality for serializing/deserializing images to/from

XML/JSON - Report in HTML, text, Latex

3. SQA monitoring (SQAM) 3. SQA monitoring (SQAM) Its features allow users to easy increase number of tests for different collision systems, energies, detector geometries etc

SQAM provides: - Automatic testing of simulation, reconstruction and analysis - Automatic check of simulation results QAM current status: About 30 tests run nightly.

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