12th International Workshop on Boosted Object Phenomenology, Reconstruction and Searches in HEP (BOOST 2020 webinars)

Europe/Zurich
Online

Online

Roman Kogler (Hamburg University (DE)) , Andreas Hinzmann (Hamburg University (DE))
Description

BOOST 2020 is the twelfth conference of a series of successful joint theory/experiment workshops that bring together the world's leading experts from theory and LHC experiments. Our aim are lively discussions about the latest progress in this field and the exchange of expertise to generate new ideas, in order develop new approaches on the reconstruction and use of boosted decay topologies in particle physics and beyond.

Due to international travel restrictions, the BOOST workshop will be carried out in a special online format in July 2020. A one-week series of web seminars is planned with two seminars and a poster discussion session per day. We call for abstracts for a poster session with more details on the call for abstracts page.

The next in-person workshop in Hamburg is foreseen in 2021.

The BOOST conference series cover the following topics:

  • Phenomenology and searches using jet substructure
  • New jet substructure observables and algorithms
  • QCD measurements and modeling
  • Jet reconstruction performance
  • Machine learning
  • Pileup mitigation
  • Heavy-ion collisions
  • Future colliders

On July 16-17 (just before BOOST starts), there will be a satellite mini-workshop on anomaly detection: LHC Summer Olympics 2020.

Previous BOOST editions:

Participants
  • Aaron Angerami
  • Aaron Paul O'Neill
  • Abhaya Kumar Swain
  • Abhijith Gandrakota
  • Abhisek Saha
  • Abhishek Roy
  • Abu Ubaidah Amir Bin Ab Maalek
  • AC Williams
  • Adam Takacs
  • Aditya Pathak
  • Afiq Azraei Bin Rishinsa
  • Afiq Azraei Bin Rishinsa
  • Alba Soto Ontoso
  • Alberto Prades Ibañez
  • Alejandro Gomez Espinosa
  • Alessandro Guida
  • Alexander Schmidt
  • Aliaksei Hrynevich
  • Alicia Wongel
  • Anabel Romero
  • Anastasia Kotsokechagia
  • Andrea Piccinelli
  • Andreas Hinzmann
  • Andrew Larkoski
  • Andrzej Siodmok
  • Anjishnu Bandyopadhyay
  • Anna Albrecht
  • Anna Benecke
  • Anna Rinaudo
  • Annapaola De Cosa
  • Anne Marie Sickles
  • Antonio Giannini
  • Antonis Agapitos
  • Aparajita Dattagupta
  • Arthur Linss
  • Ashley Marie Parker
  • Asmaa Aboulhorma
  • Ayana Arce
  • Balasubramaniam K M
  • Basem El-Menoufi
  • Behzad Salmassian
  • Ben Bruers
  • Bibhuti Parida
  • Bin Yan
  • Björn Tiedemann
  • Bryan Ostdiek
  • Cari Cesarotti
  • Cecilia Tosciri
  • Chang Wu
  • Charanjit K. Khosa
  • Chloe Derocher
  • Chris Malena Delitzsch
  • Christine McLean
  • Christopher Don Milke
  • Christopher Garner
  • Christopher Matthies
  • Christopher Young
  • Clemencia Mora Herrera
  • Clemens Lange
  • Congqiao Li
  • Cristina Ana Mantilla Suarez
  • Daniel Camarero Munoz
  • Danish Farooq Meer
  • DARIUS FAROUGHY
  • David Yu
  • Davide Melini
  • Davide Napoletano
  • Debajyoti Sengupta
  • Debarati Roy
  • Deepak Kar
  • Dengfeng Zhang
  • Dennis Schwarz
  • Dilia Maria Portillo Quintero
  • Dingyu Shao
  • Disha Bhatia
  • Doojin Kim
  • Dylan Sheldon Rankin
  • Edson Carquin Lopez
  • Eimear Isobel Conroy
  • Eirini Tziaferi
  • Eric Ballabene
  • Erik Buhmann
  • Fabio Iemmi
  • Fang-Ying Tsai
  • Francesco Conventi
  • Frank Taylor
  • Gabriel Palacino
  • Gabriele Benelli
  • Gang Zhang
  • Garvita Agarwal
  • Gavin Salam
  • Georgios Bakas
  • Giordon Holtsberg Stark
  • Giovanni Stagnitto
  • Giuseppe Callea
  • Giuseppe Latino
  • Gregor Kasieczka
  • Gregory Soyez
  • Grigorios Chachamis
  • Guilherme Milhano
  • Hamed Abdolmaleki
  • Helena Santos
  • Henning Kirschenmann
  • Henrik Jabusch
  • Herjuno Rah Nindhito
  • Hofie Hannesdottir
  • Holly Ann Pacey
  • HuaXing Zhu
  • Huilin Qu
  • Ian Moult
  • Ines Ochoa
  • Ioannis Papakrivopoulos
  • Irene Zoi
  • Ish Kaul
  • Jack Araz
  • Jack Helliwell
  • Jack Holguin
  • Jack Joseph Hall
  • James Dolen
  • James Frost
  • James Grundy
  • James Mulligan
  • Jannik Geisen
  • Javier Aparisi
  • Javier Mauricio Duarte
  • Jean-Roch Vlimant
  • Jeffrey Krupa
  • Jennifer Kathryn Roloff
  • Jennifer Ngadiuba
  • Jeremi Niedziela
  • Jesse Liu
  • Jesse Thaler
  • Jie Xiao
  • Jindrich Lidrych
  • Jinmian Li
  • Joey Huston
  • Johannes Hamre Isaksen
  • John Conway
  • Jonathan Butterworth
  • Juan Ramón Muñoz de Nova
  • Judita Mamuzic
  • Juhi Dutta
  • Julia Lynne Gonski
  • Julie Hogan
  • Julio Lozano Bahilo
  • Juska Pekkanen
  • Justin Tan
  • Karla Pena
  • Katerina Tzanetou
  • Katherine Fraser
  • Katherine Rybacki
  • Kaustuv Datta
  • Kimmo Kallonen
  • Kristin Dona
  • Ksenia de Leo
  • Kuan-Yu Lin
  • Kyle James Read Cormier
  • Lauren Meryl Hay
  • Leonid Didukh
  • Lihan Liu
  • Liliana Apolinario
  • LINGFENG LI
  • Lisa Benato
  • Louise Skinnari
  • Loukas Gouskos
  • Lucia Masetti
  • Lucy Upton
  • Lydia Audrey Beresford
  • Magda Diamantopoulou
  • Manan Shah
  • Manimala Mitra
  • Marat Freytsis
  • Marcel Vos
  • Marcelo Gameiro Munhoz
  • Maria Vittoria Garzelli
  • Mario Campanelli
  • Marisilvia Donadelli
  • Mark Neubauer
  • Marta Verweij
  • Martin Krivos
  • Martin Murin
  • Martin Rybar
  • Maryam Bayat Makou
  • Matt LeBlanc
  • Maximilian J Swiatlowski
  • Maxwell Cui
  • Michael Heinz
  • Michael James Fenton
  • Miguel Villaplana
  • Mihoko Nojiri
  • Milos Dordevic
  • Monoranjan Guchait
  • Mrinal Dasgupta
  • Muge Karagoz
  • Muhammad Farooq
  • Negin Shafiei
  • Nhan Viet Tran
  • Nicola De Biase
  • Nilanjana Kumar
  • Nils Faltermann
  • Ning Wang
  • Nuno Castro
  • Nurfikri Norjoharuddeen
  • Olaf Nackenhorst
  • Ovidiu Miu
  • Oz Amram
  • Pablo Martín
  • Pantelis Kontaxakis
  • Patrick Komiske
  • Pedro Cal
  • Pedro Candido da Silva
  • Pekka Sinervo
  • Peter Loch
  • Petr Jacka
  • Philip Coleman Harris
  • Philipp Windischhofer
  • Piyush Karande
  • Prasanth Shyamsundar
  • Praveen Chennavajhula
  • QAMARUL HASSAN
  • Qianshu Lu
  • Rachik Soualah
  • Radha Mastandrea
  • Raghav Kunnawalkam Elayavalli
  • Raif Rafideen Bin Norisam
  • Ramon Barrio
  • RANJIT NAYAK
  • Rashmish Mishra
  • Reina Coromoto Camacho Toro
  • Reyer Edmond Band
  • Ricardo Barrué
  • Robin Erbacher
  • Rogelio Reyes Almanza
  • ROJALIN PADHAN
  • Roman Kogler
  • Ron Soltz
  • Roy Lemmon
  • Rui Zhang
  • Saehanseul Oh
  • Sahibjeet Singh
  • Salah Nasri
  • Sang Eon Park
  • SangEun Lee
  • Sanmay Ganguly
  • Santeri Laurila
  • Sara Nabili
  • SARAN RAJ K
  • Sascha Daniel Diefenbacher
  • Sascha Liechti
  • Saunak Dutta
  • Sebastien Rettie
  • Semra Demircali
  • Shalini Epari
  • Shira Jackson
  • Shubham Bansal
  • Silvia Auricchio
  • Simone Caletti
  • Simone Marzani
  • Sinjini Chandra
  • Soumya Dansana
  • Soumyadip Barman
  • Steffen Albrecht
  • Steven Schramm
  • Sukanya Sinha
  • Suman Chatterjee
  • Sung Hak Lim
  • Swagata Ghosh
  • Syed Mohamed Syakir Syed Omar
  • Tania Robens
  • Taoli Cheng
  • Tasnuva Chowdhury
  • Tatjana Lenz
  • Teng Jian Khoo
  • Todd Huffman
  • Toshi Sumida
  • Vinicius Massami Mikuni
  • Wasikul Islam
  • Wenkai Zou
  • Wouter Waalewijn
  • Xingguo Li
  • Yajun He
  • Yang-Ting Chien
  • Yi-Lun Chung
  • Yoav Afik
  • Yu-Heng Chen
  • Yuya Mino
  • Zuhal Seyma Demiroglu
    • 15:00 17:00
      Session 1: New techniques
      Conveners: Andreas Hinzmann (Hamburg University (DE)) , Roman Kogler (Hamburg University (DE))
    • 17:00 18:00
      Session 6: Discussion session
      • 17:00
        Anomaly Awareness for new physics searches 9m

        In this talk we will present a new algorithm to search for new physics called Anomaly Awareness. By making our algorithm 'aware' of the presence of a range of different anomalies, we improve its capability to detect anomalous events even when it hasn't been exposed to them in the past. As an example, we apply this method to boosted jets and use it to uncover new resonances or EFT effects.

      • 17:00
        Disentangling Boosted Higgs Boson Production Modes with Machine Learning 9m

        Higgs Bosons produced via gluon-gluon fusion (ggF) with large transverse momentum ($p_T$) are sensitive probes of physics Beyond the Standard Model. However, high $p_T$ Higgs Boson production is contaminated by a diversity of production modes other than ggF: vector boson fusion, production of a Higgs boson in association with a vector boson and with a top-quark pair. Combining jet substructure and event information with modern machine learning, we demonstrate the ability to focus on particular production modes. These tools hold great discovery potential for boosted Higgs bosons produced via ggF and may also provide additional information about the Higgs Boson sector of the Standard Model in extreme phase space regions for other production modes as well.

      • 17:00
        ML approach to VBF event topology classification: Recurrent Neural Network based on jets information 9m

        A new approach for the identification of VBF topology is presented. A Recurrent Neural Network (RNN) approach based on the 4-momentum of the small-R jets in the event has been developed in the context of the search of high mass resonances decaying into diboson semi-leptonic final states (X —> VV —> vv/lv/ll + qq). The class of RNN networks shows high performances and opportunity to deal with variable length input set as the 4-momentum of jets in an event. The analysis is performing the classification of VBF vs ggF/DY events based on the score of the RNN before the full analysis flow. This method shows higher classification performances and an higher signal
        efficiency respect to usual approaches based on the tagging of the VBF-like jets. The simple 4-momentum (low-level variables) of the small-R jets in the event are used instead of other variables built starting from them (high-level variables). Furthermore, this approach based on jets information of the event is independent by the lepton channel of the diboson decay and it has been used for different spin hypothesis.

        Speaker: Antonio Giannini (Universita e sezione INFN di Napoli (IT))
      • 17:00
        Quantum information and entanglement with top quarks at the LHC 9m

        Entanglement is a key subject in quantum information theory. Due to its genuine relativistic and fundamental nature, high-energy colliders are attractive systems for the experimental study of quantum information theory. We propose the detection of entanglement between the spins of top-antitop quark pairs at the LHC, representing the first proposal of entanglement detection in a pair of quarks, and also the entanglement observation at the highest energy scale so far. We show that entanglement can be observed by direct measurement of the angular separation between the leptons arising from the decay of the top-antitop pair. The detection can be achieved with more than 5 statistical deviations, using the current data recorded at the LHC. In addition, we develop a simple protocol to implement the quantum tomography of the top-antitop pair, providing a new experimental tool to test theoretical predictions for the quantum state of the top-antitop pair. Our work explicitly implements canonical experimental techniques of the quantum information field, paving the way to use high-energy colliders to study quantum information theory.

    • 15:00 17:00
      Session 2: Measurements and Calculations
      Convener: Andrew Larkoski (Reed Collge)
    • 17:00 18:00
      Session 6: Discussion session
      • 17:00
        A Robust Measure of Event Isotropy at Colliders 9m

        We introduce a new event shape observable -- event isotropy -- that quantifies how close the radiation pattern of a collider event is to a uniform distribution. This observable is based on a normalized version of the energy mover's distance, which is the minimum "work" needed to rearrange one radiation pattern into another of equal energy. We investigate the utility of event isotropy both at electron-positron colliders, where events are compared to a perfectly spherical radiation pattern, as well as at proton-proton colliders, where the natural comparison is to either cylindrical or ring-like patterns. Compared to traditional event shape observables like sphericity and thrust, event isotropy exhibits a larger dynamic range for high-multiplicity events. This enables event isotropy to not only distinguish between dijet and multijet processes but also separate uniform N-body phase space configurations for different values of N. As a key application of this new observable, we study its performance to characterize strongly-coupled new physics scenarios with isotropic collider signatures.

      • 17:00
        Calculation for Non-global Logarithms with Neural Networks 9m

        High-precision all-order calculations can only be performed for a narrow class of observables, which are sensitive to radiation over the entire final state phase-space. When phase-space boundaries are introduced, the resummation is affected by so-called non-global logarithms, which have an intricate all-order structure. In this talk, we present a first-principle calculation for the non-global logarithms, and some improvements for higher-order calculation and resummation are proposed with artificial neural networks, which can dramatically speed up needed theory calculations.

      • 17:00
        Groomed jet mass as a direct probe of collinear parton dynamics 9m

        We study the link between parton dynamics in the collinear limit and the logarithmically enhanced terms of the groomed jet mass distribution, for jets groomed with the modified mass-drop tagger (mMDT). While the leading logarithmic structure is linked to collinear evolution with leading-order splitting kernels, here we derive the NLL structure directly from triple-collinear splitting functions. The calculation we present is a fixed-order calculation in the triple-collinear limit, independent of resummation ingredients and methods. It therefore constitutes a powerful cross-check of the NLL results derived using the SCET formalism and provides much of the insight needed for resummation within the traditional QCD approach.

      • 17:00
        Measurement of boosted top quark pair production 9m

        A measurement of the production cross section for high transverse momentum top quark pairs is reported. The data set was collected during 2016 with the CMS detector at the LHC from pp collisions at 13 TeV, and corresponds to an integrated luminosity of 35.9 fb-1. The measurement uses events where either both top quark candidates decay hadronically and are reconstructed as large-radius jets with pt>400 GeV, or where one top quark decays hadronically and is identified as a single large-radius jet with pt>400 GeV and the other top quark decays leptonically to a b jet, an electron or a muon, and a neutrino. The cross section is extracted differentially as a function of kinematic variables of the top quark or the top quark pair system. The results are presented at the particle level, within a region of phase space close to that of the experimental acceptance, and at the parton level, and are compared to various theoretical models. The measured differential cross sections are significantly lower in both decay channels in the phase space of interest, compared to the theory predictions, while the normalized differential cross sections are consistent between data and theory.

      • 17:00
        Multi-Differential and Unbinned Measurements of Hadronic Event Shapes in e+e- Collisions at sqrt(s)=91 GeV from ALEPH Open Data 9m

        First results are presented on the use of a new machine-learning based unfolding technique, OmniFold, applied to archival hadronic e+e- collisions using 730 pb^-1 of data collected at 91 GeV with the ALEPH detector at LEP. With the archived data and unfolding procedure, multiple classic hadronic event-shape variables are measured in a fully unbinned and multi-differential manner. Of particular interest, the differential distribution of log one minus thrust is presented and is expected to be helpful for extracting alpha_s via a fit to precision QCD calculations. The analysis is accompanied by a public release of the archived data set and the unfolding results, so that users may make their own versions of plots, either with different binning or with different combinations of observables in a multi-differential distribution.

      • 17:00
        Search for Boosted Higgs decaying into bottom quark pairs in CMS 9m

        I will present the search for boosted Higgs boson with transverse momentum greater than 450 GeV decaying into bottom quark pairs using LHC full run 2 dataset collected by the CMS experiment.
        In this search, we employed the latest jet substructure variables and b-tagging techniques based on a deep neural network to reduce the overwhelming QCD backgrounds.
        An excess of events above background is observed with a local significance of 2.5 standard deviation, while the expectation is 0.7.
        The measured Higgs production cross sections is also presented as a function of transverse momentum of the Higgs boson and compared with the latest gluon-gluon fusion prediction with finite top-mass corrections.

      • 17:00
        Towards Machine Learning Analytics for Jet Substructure 9m

        The past few years have seen a rapid development of machine-learning algorithms. While surely augmenting performance, these complex tools are often treated as black-boxes and may impair our understanding of the physical processes under study. Moving a first step into the direction of applying expert-knowledge in particle physics, we test whether the optimal decision function is achieved by standard training. In particular, we consider the binary classification problem of discriminating quark-initiated jets from gluon-initiated ones. We construct a new version of the widely used N-subjettiness variable, which features a simpler theoretical behaviour than the original one, while maintaining, if not exceeding, the discrimination power. We input these new observables to the simplest possible neural network, the one made by a single neuron (perceptron) and we analytically study the network behaviour at leading logarithmic accuracy. We are able to determine under which circumstances the perceptron achieves optimal performance. We also compare our analytic findings to an actual implementation of a perceptron and to a more realistic neural network and find very good agreement.

    • 15:00 17:00
      Session 3: Heavy Ions
      Convener: Philip Coleman Harris (Massachusetts Inst. of Technology (US))
      • 15:00
        CANCELED (Theoretical advances in jet substructure for heavy ion physics) 40m
        Speaker: Dr Guilherme Milhano (LIP-Lisbon & CERN TH)
      • 16:00
        Experimental advances in jet substructure for heavy ion physics 40m
        Speaker: Marta Verweij (Nikhef National institute for subatomic physics (NL))
    • 17:00 18:00
      Session 6: Discussion session
      • 17:00
        Measurement of suppression of large-radius jets and its dependence on substructure in Pb+Pb with ATLAS 9m

        Measurements of the jet substructure in Pb+Pb collisions provide information about the mechanism of jet quenching in the hot and dense QCD medium created in these collisions, over a wide range of energy scales. This poster presents the ATLAS measurement of the suppression of yields of large-radius jets and its dependence on the jet substructure, characterized by the presence of sub-jets and their angular correlations. This measurement is performed using the large Pb+Pb data sample at the center-of-mass energy of 5.02 TeV recorded in 2018 and compared to the result from 2017 $pp$ collisions at the same collision energy. This study of the suppression of inclusive yields of large-$R$ jets brings new information about the evolution of the parton shower in the medium and tests the sensitivity of the jet quenching to the color coherence effects.

        Speaker: Martin Krivos (Charles University (CZ))
    • 15:00 17:00
      Session 4: Taggers
      Convener: Peter Loch (University of Arizona (US))
    • 17:00 18:00
      Session 6: Discussion session
      • 17:00
        Explainable AI for ML jet taggers using expert variables and layerwise relevance propagation 9m

        A method is presented to extract salient information from a deep neural network classifier of jet substructure tagging techniques, using expert variables that augment the inputs, using layerwise relevance propagation. The results show that these eXpert AUGmented (XAUG) variables can be used to easily interpret the behavior of the classifier, and in some cases can capture the behavior of the classifier completely. This can be used both to understand the behavior of complicated classifiers, and also to utilize them to guide development of expert variables that can encapsulate the physics the classifier is learning.

      • 17:00
        Machine Learning for Pion Identification and Energy Calibration with the ATLAS Detector 9m

        Separating charged and neutral pions as well as calibrating the pion energy response is a core component of reconstruction in the ATLAS calorimeter. This poster presents an investigation of deep learning techniques for these tasks, representing the signal in the ATLAS calorimeter layers as pixelated images. Machine learning approaches outperform the classification applied in the baseline local hadronic calibration and are able to improve the energy resolution for a wide range in particle momenta, especially for low energy pions. This work demonstrates the potential of machine-learning-based low-level hadronic calibrations to significantly improve the quality of particle reconstruction in the ATLAS calorimeter.

        Speaker: Mr Dewen Zhong (Univ. Illinois at Urbana Champaign (US))
      • 17:00
        Pareto optimization for decorrelated taggers 9m

        Jet taggers that are decorrelated from certain observables, such as mass, are of increasing interest for experimental measurements.
        Several methods have been proposed to design taggers that balance discrimination power against correlation.
        As a fundamentally multi-objective optimization problem, there is an infinite set of Pareto-efficient solutions, known as the Pareto frontier.
        We demonstrate that while most existing methods can generally converge to some solution near this frontier, there is often limited control over the exact trade-off point achieved, even when the surrogate objective includes a tunable hyperparameter.
        We also demonstrate some qualitative features of this Pareto frontier using a toy model with an analytic likelihood, allowing us to probe the exact points at which optimal discrimination and decorrelation occur.
        Lastly, we discuss the use of these qualitative features as a map for locating optimal working points for real-world taggers, for which no tractable likelihood is available.