Advanced Multi-Variate Analysis for New Physics Searches at the LHC
AMVA4NewPhysics
H2020-EU.1.3.1.
€ 2 393 361,31
ISTITUTO NAZIONALE DI FISICA NUCLEARE
With the 2012 discovery of the Higgs boson at the Large Hadron Collider, LHC, the Standard Model of particle physics has been completed, emerging as a most successful description of matter at the smallest distance scales. But as is always the case, the observation of this particle has also heralded the dawn of a new era in the field: particle physics is now turning to the mysteries posed by the presence of dark matter in the universe, as well as the very existence of the Higgs. The upcoming run of the LHC at 13 TeV will probe possible answers to both issues, providing detailed measurements of the properties of the Higgs and extending significantly the sensitivity to new phenomena.
Since the LHC is the only accelerator currently exploring the energy frontier, it is imperative that the analyses of the collected data use the most powerful possible techniques. In recent years several analyses have utilized multi-variate analysis techniques, obtaining higher sensitivity; yet there is ample room for further improvement. With our programme we will import and specialize the most powerful advanced statistical learning techniques to data analyses at the LHC, with the objective of maximizing the chance of new physics discoveries.
We aim at creating a network of European institutions to foster the development and exploitation of Advanced Multi-Variate Analysis (AMVA) for New Physics searches. The network will offer extensive training in both physics and advanced analysis techniques to graduate students, focusing on providing them with the know-how and the experience to boost their career prospects in and outside academia. The network will develop ties with non-academic partners for the creation of interdisciplinary software tools, allowing a successful knowledge transfer in both directions. The network will study innovative techniques and identify their suitability to problems encountered in searches for new physics at the LHC and detailed studies of the Higgs boson sector.
particle accelerator
H2020-EU.1.3.1. - Fostering new skills by means of excellent initial training of researchers
MSCA-ITN-2015-ETN - Marie Skłodowska-Curie Innovative Training Networks (ITN-ETN)
H2020-MSCA-ITN-2015
MSCA-ITN-ETN - European Training Networks
Via Enrico Fermi 40
00044 Frascati
THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
UNIVERSITE BLAISE PASCAL CLERMONT-FERRAND II
UNIVERSITE CLERMONT AUVERGNE
UNIVERSITE CATHOLIQUE DE LOUVAIN
LABORATORIO DE INSTRUMENTACAO E FISICA EXPERIMENTAL DE PARTICULAS LIP
INSTITUTE OF ACCELERATING SYSTEMS AND APPLICATIONS
UNIVERSITA DEGLI STUDI DI PADOVA
EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH
TECHNISCHE UNIVERSITAET MUENCHEN
ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
B12 CONSULTING
SDG CONSULTING ITALIA SPA
Periodic Reporting for period 1 - AMVA4NewPhysics (Advanced Multi-Variate Analysis for New Physics Searches at the LHC)
Reporting period: 2015-09-01 to 2017-08-31
Summary of the context and overall objectives of the project
In the course of the last 40 years the standard model (SM) has received increasing verification by precise experimental tests of its predictions. There are, however, compelling reasons to believe the SM is not a complete theory but only an "effective" low-energy one, breaking down at energies higher than those probed so far; hence surprises could await us, in particular in the Higgs sector of the SM. The Higgs boson may thus constitute the door through which a whole class of new phenomena and a deeper understanding of Nature can be accessed.
Among the theories proposed as possible extensions of the SM perhaps the best-motivated one is SUSY. Most of the other proposed theories that extend the SM also predict new physics that the LHC experiments should be sensitive to. Experiments are looking for new particles predicted in many extensions of the SM, but it is equally important to pursue a model-independent approach, and search for any rare new processes that may be hiding in the high-energy collisions. It is therefore necessary to simultaneously investigate all possible directions and to broaden the ways of conducting searches for new physics.
The advent of statistical learning (SL) techniques is bringing dramatic changes to the potential of data analysis. High-energy physics (HEP) has been slow in adapting to these changes. We want to tackle the two big challenges mentioned above with cutting-edge SL tools recently made available, optimizing them to those specific tasks, as well as developing new ones. This will give us a chance to train a new generation of data scientists. Physics has always been a breeding ground for skilled individuals who have later moved on to successful careers outside academia. Also, HEP has a history of developing tools that later become of exceptional importance for society as a whole (e.g. the internet, proton therapy). Hence we claim that research in HEP, employing the cutting-edge SL techniques now available, may produce new important advancements for tomorrow's society as a whole.
The overall objectives of the AMVA4NewPhysics project are:
O1: Develop and improve advanced statistical learning tools for data analysis in particle physics.
O2: Bring together academic and non-academic partners to create innovative training opportunities for talented students in statistical learning, computational tools, and data science.
O3: Deepen our knowledge of Nature by providing answers to fundamental physics questions with the LHC.
After two years, the AMVA4NewPhysics network is following very closely the path laid out in its proposal; indeed, objectives 1 and 2 are on track to be achieved at the level we aimed for. Of course, objective 3 is work in progress; whether we will be fully successful by being able to find precise answers to fundamental questions, this is not within our power, as it depends on still unknown choices of parameters of fundamental physics which Nature has set for us.
Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far
The results of our action in the reporting period can be divided into scientific, training, dissemination, and outreach ones.
As far as scientific results are concerned, we must quote five main produtcs. The first is a new method for data analysis which combines regression and classification techniques, as described in Deliverable 1.1. The document was produced thanks to the collaboration of several ESRs, who got together in the summer of 2016 to produce the datasets required for the studies of classification tools capable of discriminating the Higgs pair production signal from competing backgrounds. This included preliminary regression studies aimed at improving the measurement of variables critical for signal discrimination. The use of deep neural networks for this combined task of regression-cum-classification is a novel technique in HEP, and it was shown to provide excellent results especially in the case of HHbbtautau final states. A second result was the production of an optimization of classification algorithms for Hbb searches in di-Higgs production, performed by the Oxford node for the ATLAS experiment. A third result in the reporting period was the production of an improved algorithm for b-tagging for the CMS experiment, called "Deep CSV", and based on a complex deep neural network architecture mainly developed by the CERN node members. A fourth result was the construction of a new method for the multi-dimensional modelling of QCD multijet production processes entirely data-driven and based on the mixing of half-events. The statistical properties of the method were assessed by the UNIPD node and the method was presented in two international conferences. A fifth scientific product is the MoMEMta package produced by the UCL node of the network. This is a software for computing event weights in the simulation of physics processes, based on the matrix element calculation. Finally, a result about to be released, and developed by UNIPD and UCA, concerns the study of anomaly detection with a new algorithm.
Concerning training results, we achieved all objectives we had set to ourselves concerning the organization of schools and workshops, which were attended by our ESRs. For each of them a career development plan was carefully laid out and updated along the course of these two years. All students achieved the qualifications requested by their Ph.D. courses.
As far as dissemination is concerned, we may quote the organization of the parallel session on Statistical Methods at QCHS XII, which allowed the discussion of network results in talks by Vischia, De Castro, and Scarpa; the talk by Dorigo and Dall'Osso at EPS 2017 concerning results based on network activities; public reports (D1.1,D4.1); and the seminars given by A. Saggio on the MoMEMta package. Other dissemination activities are detailed in the draft periodic report.
For outreach activities, there were a number of them; the most visible ones were a talk by D. Bortoletto at a public conference organized on March 3 2016, and the "Art & Science" initiative, the seminars by the ESRs and the others by Dorigo, the artwork production by high-school students, and the final event at the "Sala Perla".
Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)
The products delivered so far already constitute progress beyond the state of the art in the considered field of research. The new algorithms have started to find use within the experimental collaborations, have been already published in proceedings articles and are the subject of articles in preparation. Their use in LHC analyses is foreseen in the second part of the network timeline. The impact on the research in HEP of these tools will largely depend on whether there is new physics at reach of the present generation of HEP instruments. More tools are being developed and are the focus of future deliverables, and the same considerations apply to them.
The potential impact on society of our action is very high, as the discovery of new physics -eased by the added sensitivity provided by our statistical learning procedures- has the potential of completely revolutionize our understanding of fundamental physics as well as the universe. A discovery of corpuscular dark matter producible at accelerators, for example, or of a fifth force of nature, could impact society in a way that is beyond anybody's guess. As already stated, the concretization of this potential is unfortunately not in our hands, although we are certainly facilitating it with our action.
Documents, reports (13)
Final report on performance of algorithms for LHC searches of new physics
Final Activity Report: Performance of the algorithms optimized and developed anew for LHC searches of new physics.
Organization of network events 2
Organization of network events (schools and workshops) (month 48). A report will be delivered at Month 48 with the results of the network events of the first reportind period.
Report of performance of algorithms for data-driven background shape modelling
Periodic Activity Report. Report of the detailed implementation, the performance, and the result of robustness tests of algorithms applied to the determination of background shape densities.
Report on SL method for new searches
Periodic Activity Report. Document describing a customized SL method optimized for model independent searches of new physics
Final report on classification and regression tools for Higgs measurements
Final Activity Report: Report on classification and regression tools in Higgs measurements. This document describes the performance of the optimized algorithms on the classification and regression problems of WP1.
Report on the inverse bagging algorithm
Report describing the implementation, the performance, and the HEP use cases of the inverse bagging algorithm
Report on MVA for Higgs studies
Periodic Activity Report: Report on MVA for Higgs studies. Document with description of algorithms to be used as defined in task 2 and testing with Delphes simulations of collider data. Tailored algorithms will be identified for four specific use cases: classification for H →bb, H →ττ final states; and regression for H →bb and H →ττ mass reconstruction (month 18).
Publication on MEM and its implementations
Publication(s) on the method and its implementations in specialized journals (Computer Physics Comm., or EPJC section on MC).
Report of studied SL methods for targeted and global searches of new physics
Periodic Activity Report: Report on MVA for signature-specific searches. This is a document containing a technical description of the algorithms identified and tested in tasks 1 and 2 and the result of the application of such algorithms to simulated data of the benchmarks defined in task 1 of WP2.
Draft Periodic Report
Draft Periodic Report for the purpose of the mid-term review meeting. The final version will be submitted at the end of the first reporting period.
Online web documentation of package
On-line web documentation and support of the developed package
Publication on report of the implementation of the AToM MVA recasting package
Scientific publication: Report of the implementation of the ATOM MVA recasting package
Soft Skills training
Training in soft skills (month 18 or 34, depending on ESR hiring period).
Public seminars in high schools and public events by ESRs
Public seminars in high schools and public events by ESRs (month 24-47).
Assessment of impact of outreach activities in outreach workshop talks
Outreach workshop where ESRs will report their experience and their achievements, and where the impact of the outreach activities engaged by the ESRs will be assessed and critically studied (month 37).
C++ package to evaluate MEM weights for arbitrary processes in the SM and beyond
C++ package to evaluate matrix-element weights on an event-by-event basis for any process in the SM and BSM that can be directly run in ROOT and exploit the statistical tools already available there
Software implementation of the inverse bagging algorithm
Software implementation of Inverse Bagging algorithm
Websites, patent fillings, videos etc. (1)
Network Blog - Hashtag
High-visibility and high-traffic blog with a horizontal relationship between writers and readers (month 4). High-visibility Twitter hashtag distributing news on the programme (month 4).
Thesis dissertations (2)
Model independent searches for New Physics using Machine Learning at the ATLAS experiment
Author(s): Fabricio Jimenez
Published in: 2019
Advanced Statistical Methods for Data Analysis in Particle Physics
Author(s): Kotkowski, G. M.
Peer reviewed articles (3)
Measurement of VH, H → b b ¯ $$ \mathrm{H}\to \mathrm{b}\overline{\mathrm{b}} $$ production as
Author(s): M. Aaboud, G. Aad, B. Abbott, D. C. Abbott, O. Abdinov, A. Abed Abud, D. K. Abhayasinghe, S. H. Abidi, O. S. AbouZeid, N. L. Abraham, H. Abramowicz, H. Abreu, Y. Abulaiti, B. S. Acharya, S. Adachi, L. Adam, C. Adam Bourdarios, L. Adamczyk, L. Adamek, J. Adelman, M. Adersberger, A. Adiguzel, S. Adorni, T. Adye, A. A. Affolder, Y. Afik, C. Agapopoulou, M. N. Agaras, A. Aggarwal, C. Agheorghiesei, J.
Published in: Journal of High Energy Physics, Issue 2019/5, 2019, ISSN 1029-8479
DOI: 10.1007/jhep05(2019)141
Search for nonresonant Higgs boson pair production in the b b ¯ b b ¯
Author(s): A. M. Sirunyan, A. Tumasyan, W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, M. Dragicevic, J. Erö, A. Escalante Del Valle, M. Flechl, R. Frühwirth, V. M. Ghete, J. Hrubec, M. Jeitler, N. Krammer, I. Krätschmer, D. Liko, T. Madlener, I. Mikulec, N. Rad, H. Rohringer, J. Schieck, R. Schöfbeck, M. Spanring, D. Spitzbart, A. Taurok, W. Waltenberger, J. Wittmann, C.-E. Wulz, M. Zaruc
Combination of Searches for Higgs Boson Pair Production in Proton-Proton Collisions at s = 13 TeV
Author(s): A. M. Sirunyan, A. Tumasyan, W. Adam, F. Ambrogi, E. Asilar, T. Bergauer, J. Brandstetter, M. Dragicevic, J. Erö, A. Escalante Del Valle, M. Flechl, R. Frühwirth, V. M. Ghete, J. Hrubec, M. Jeitler, N. Krammer, I. Krätschmer, D. Liko, T. Madlener, I. Mikulec, N. Rad, H. Rohringer, J. Schieck, R. Schöfbeck, M. Spanring, D. Spitzbart, A. Taurok, W. Waltenberger, J. Wittmann, C.-E. Wulz, M. Z
Published in: Physical Review Letters, Issue 122/12, 2019, ISSN 0031-9007
The DAB: Detecting Anomalies via Bootstrapping
Author(s): Vischia, P.; MENARDI, GIOVANNA; KOTKOWSKI, GRZEGORZ MICHAL;
Published in: EMS 2019 - Program and Book of Abstracts, 2019
A novel approach for pre-filtering event sources using the von Mises distribution
Author(s): COSTANTIN, DENISE; MENARDI, GIOVANNA; BRAZZALE, ALESSANDRA ROSALBA
Published in: EMS 2019 Program and Book of Abstracts, 2019
Nonparametric Semisupervised Classification and Variable Selection for New Physics Searches
Author(s): Casa, Alessandro; MENARDI, GIOVANNA
Hemisphere Mixing: A Fully Data-Driven Model Of QCD Multijet Backgrounds For LHC Searches
Author(s): Tommaso Dorigo, Martino Dall'Osso, Pablo de Castro Manzano, Livio Finos, Grzegorz Kotkowski, Giovanna Menardi, Bruno Scarpa
Published in: Proceedings of The European Physical Society Conference on High Energy Physics — PoS(EPS-HEP2017), 2018, Page(s) 370
DOI: 10.22323/1.314.0370
Last update: 15 April 2019
Record number: 198293