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

Europe/Zurich
Online

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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:

Registration
Registration
Participants
• Abu Ubaidah Amir Bin Ab Maalek
• Afiq Azraei Bin Rishinsa
• Afiq Azraei Bin Rishinsa
• Alba Soto Ontoso
• Alejandro Gomez Espinosa
• Alicia Wongel
• Anabel Romero
• Anastasia Kotsokechagia
• Andrea Piccinelli
• Andreas Hinzmann
• Andrew Larkoski
• Andrzej Siodmok
• Anna Albrecht
• Anne Marie Sickles
• Antonio Giannini
• Aparajita Dattagupta
• Bibhuti Parida
• Bin Yan
• Bryan Ostdiek
• Cari Cesarotti
• Cecilia Tosciri
• Chang Wu
• Charanjit K. Khosa
• Chris Malena Delitzsch
• Christopher Matthies
• Christopher Young
• Clemencia Mora Herrera
• Clemens Lange
• Cristina Ana Mantilla Suarez
• Danish Farooq Meer
• DARIUS FAROUGHY
• Davide Melini
• Davide Napoletano
• Debarati Roy
• Deepak Kar
• Dengfeng Zhang
• Dennis Schwarz
• Dilia Maria Portillo Quintero
• Dingyu Shao
• Doojin Kim
• Edson Carquin Lopez
• Eimear Isobel Conroy
• Eric Ballabene
• Erik Buhmann
• Fabio Iemmi
• Fang-Ying Tsai
• Gabriel Palacino
• Gabriele Benelli
• Gavin Salam
• Giordon Holtsberg Stark
• Giovanni Stagnitto
• Giuseppe Callea
• Gregory Soyez
• Guilherme Milhano
• Hamed Abdolmaleki
• Helena Santos
• Henning Kirschenmann
• Henrik Jabusch
• Holly Ann Pacey
• HuaXing Zhu
• Ian Moult
• Ines Ochoa
• Irene Zoi
• Jack Helliwell
• Jack Holguin
• James Mulligan
• Jannik Geisen
• Jean-Roch Vlimant
• Jennifer Kathryn Roloff
• Jesse Liu
• Jesse Thaler
• Joey Huston
• Johannes Hamre Isaksen
• Jonathan Butterworth
• Judita Mamuzic
• Julia Lynne Gonski
• Julie Hogan
• Julio Lozano Bahilo
• Karla Pena
• Katherine Fraser
• Kaustuv Datta
• Kimmo Kallonen
• Ksenia de Leo
• Kuan-Yu Lin
• Lisa Benato
• Louise Skinnari
• Loukas Gouskos
• Marcel Vos
• Marcelo Gameiro Munhoz
• Maria Vittoria Garzelli
• Mario Campanelli
• Marta Verweij
• Martin Murin
• Martin Rybar
• Matt LeBlanc
• Maximilian J Swiatlowski
• Michael James Fenton
• Milos Dordevic
• Muge Karagoz
• Nils Faltermann
• Ning Wang
• Nurfikri Norjoharuddeen
• Olaf Nackenhorst
• Ovidiu Miu
• Oz Amram
• Pantelis Kontaxakis
• Pedro Cal
• Pedro Candido da Silva
• Pekka Sinervo
• Peter Loch
• Petr Jacka
• Prasanth Shyamsundar
• QAMARUL HASSAN
• Rachik Soualah
• Raghav Kunnawalkam Elayavalli
• Raif Rafideen Bin Norisam
• Ricardo Barrué
• Roman Kogler
• Roy Lemmon
• Saehanseul Oh
• Sahibjeet Singh
• Santeri Laurila
• Sascha Daniel Diefenbacher
• Saunak Dutta
• Sebastien Rettie
• Shubham Bansal
• Silvia Auricchio
• Simone Caletti
• Simone Marzani
• Sinjini Chandra
• Soumya Dansana
• Steffen Albrecht
• Steven Schramm
• Sukanya Sinha
• Suman Chatterjee
• Sung Hak Lim
• Swagata Ghosh
• Syed Mohamed Syakir Syed Omar
• Tania Robens
• Tasnuva Chowdhury
• Tatjana Lenz
• Teng Jian Khoo
• Todd Huffman
• Toshi Sumida
• Wasikul Islam
• Wenkai Zou
• Wouter Waalewijn
• Xingguo Li
• Yajun He
• Yang-Ting Chien
• Yi-Lun Chung
• Yoav Afik
• Yuya Mino
Contact
• Monday, 20 July
• 15:00 17:00
Session 1: New techniques
• 15:00
Welcome 5m
Speakers: Andreas Hinzmann (Hamburg University (DE)), Roman Kogler (Hamburg University (DE))
• 15:10
Advances in jet substructure techniques (including ML) 40m
Speaker: Reina Coromoto Camacho Toro (Centre National de la Recherche Scientifique (FR))
• 16:10
Theoretical advances in jet substructure (including ML) 40m
Speaker: Dr Simone Marzani (Università di Genova and INFN Genova)
• 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.

• 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.

• Tuesday, 21 July
• 15:00 17:00
Session 2: Measurements and Calculations
• 15:00
Substructure measurements 40m
Speaker: Christine Angela McLean (SUNY Buffalo)
• 16:00
Substructure calculations and modelling 40m
Speaker: Ian James Moult
• 17:00 18:00
Session 6: Discussion session
• 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
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
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.

• Wednesday, 22 July
• 15:00 17:00
Session 3: Heavy Ions
• 15:00
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.

• Thursday, 23 July
• 15:00 17:00
Session 4: Taggers
• 15:00
Performance of jet substructure taggers in ATLAS 40m
Speaker: Steven Schramm (Universite de Geneve (CH))
• 16:00
Performance of jet substructure taggers in CMS 40m
Speaker: Loukas Gouskos (CERN)
• 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
Hadronic W boson and top quark tagging at ATLAS 9m

The reconstruction and identification of boosted hadronic final states is a key part for beyond the Standard Model (BSM) physics searches and precision measurements of Standard Model processes at ATLAS. Identification algorithms designed to identify boosted hadronically decaying W bosons and top quarks, known as taggers, have been updated and optimized from previous efforts to include the data collected between 2015 and 2017, corresponding to 80 inverse femtobarns of integrated luminosity. The data are used to derive scale factors to correct the relative difference in the tagging efficiency between data and MC simulation for these taggers. This poster describes the latest tagger developments and derivation procedure of the signal efficiency scale factors using lepton+jets events with a ttbar topology for a cut-based tagger using hadronic jet properties optimized to identify jets containing the full decay of a W boson and two Deep Neural Network top taggers that use jet substructure moments as inputs; one optimized to identify jets containing all the energy from a hadronically decaying top quark, and the other optimized to identify jets containing part of the hadronic top quark decay regardless of containment.

• 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.

• 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.

• Friday, 24 July
• 14:00 18:00
Session 5: Future
• 14:00
Future colliders theory 30m
Speaker: Matthew Philip Mccullough (CERN)
• 16:00
Discussion of BOOST contribution to Snowmass 1h 45m