BOOST2016 is the eighth of a series of successful joint theory/experiment workshops that bring together the world's leading experts from theory and LHC experiments to discuss the latest progress and develop new approaches on the reconstruction of and use of boosted decay topologies in order to search for new physics. This year, the workshop is jointly hosted by the University of Zurich and ETH Zurich.
40' + 10'
40' + 10'
In experimental studies which use boosted taggers, groomers are typically used to reduce sensitivity to wide angle soft radiation. It is therefore important to understand the behavior of these groomers to all orders in QCD. In this talk, I will discuss the factorization of groomed two prong substructure observables, focusing in particular on the $D_2$ observable. I will show that for a particular groomer, soft drop, this observable can be factorized to all orders in perturbation theory. I will discuss theoretical and experimental advantages and disadvantages of soft dropped $D_2$ as a tagger, as well as present numerical results. This analysis sheds considerable light into the behavior of groomed substructure observables and their calculability.
Jet shapes are commonly used as discriminative variables to tag boosted objects. In this talk, I will present a method to compute jet shapes for boosted objects which retains the dominant contributions coming either from the large boost or, when appropriate, from the smallness of the shape itself. I will mostly focus on the case of 2subjettiness but will also show that the method can be applied to other observables like Nsubjettiness with grooming or EnergyCorrelation functions.
I will discuss recent advances in precision jet substructure calculations. The soft drop groomed mass has been calculated to nexttonexttoleading logarithmic accuracy and matched to relative $\alpha_s^2$ fixedorder corrections for jets in $pp\to Z+j$ events. The normalized soft drop mass distribution is insensitive to underlying event and pileup, depends only on collinear physics, and only requires determination of the relative quark and gluon jet fractions from fixedorder calculations. This is the first jet substructure calculation to this accuracy and opens the door to precision theory and data comparisons.

Jet properties
Top differential cross section and charge asymmetry

New massive resonances with boosted top signatures


Identification of b jets in boosted event topologies
X> bbbar tagging ATLAS
Many of the most exciting searches for new physics beyond the Standard Model,
as well as further studies of the Standard Model itself, benefit from being
able to identify highenergy jets containing $b$ quarks (``$b$jets'').
Examples include Higgs pair production and decay via
$HH\rightarrow b\overline{b}b\overline{b}$, sensitive to Higgs trilinear
couplings~\cite{Behr:2015oqq}; graviton and radion decays to heavy fermions
and bosons in warped extra dimension models~\cite{Gouzevitch:2013qca}; thirdgeneration
superpartners in supersymmetry~\cite{Alwall:2008ag}; and indeed any new
physics with preferential couplings to heavy Standard Model particles or
thirdgeneration fermions in particular.
One of the most distinctive features of a $b$jet is the relatively long life
(on the order of 1.5~ps) of the $B$ hadron, resulting in charged particle
tracks displaced from the primary interaction vertex. For this reason, almost
all modern colliderbased particle physics experiments deploy several layers of
highgranularity silicon detectors near the interaction point, and algorithms
for distinguishing $b$jets from jets originating from lighter quarks rely on
the ability to reconstruct highresolution tracks in these finely grained
subsystems.
However, with increasingly stringent limits placed on the energy scale for new physics,
distinguishing displaced tracks within increasingly energetic jets
becomes simultaneously more important and more challenging. Two effects in
particular make $b$tagging in TeVscale jets difficult: First,
more tracks are collimated into a small angle, resulting in a higher hit
density and a more ambiguous association of hits with tracks.
A single misassignment can steer a track offcourse and produce an
erroneous impact parameter. Second, at extreme energies, an increasing
fraction of $B$ hadrons will decay after crossing the innermost layers of the
silicon detector: in the best case scenario, this situation merely reduces the
number of hits available for reconstruction and thus degrades the impact
parameter resolution of the track. A worse scenario is that the track picks up
a spurious hit in the densely populated inner layer.
Results on conventional $b$tagger efficiencies from the LHC experiments typically
are limited to momenta transverse to the beam ($p_T$) below roughly
500 GeV. Early simulation
results indicated a falling tagging efficiency beyond approximately 150 GeV.
Even with considerable optimization, results remain
consistent with a falling efficiency at high energies, though obscured somewhat
by the restricted momentum range published.
This article investigates a new method which, by relying only on Si detector hits rather
than the reconstructed tracks, better maintains its efficiency at extreme energies,
by which we mean energies of at least 300 GeV, above which conventional
$b$tagging performance degrades rapidly.
We explore the scaledependence and correlations of jet substructure observables to improve upon existing techniques in the identification of highly Lorentzboosted objects. Modified observables are designed to remove correlations from existing theoretically wellunderstood observables, providing practical advantages for experimental measurements and searches for new phenomena. We study such observables in W jet tagging and provide recommendations for observables based on considerations beyond signal and background efficiencies.
Boosted W/Ztagging

The LHC is starting to study the regime where topquark pairs are produced with energies much larger than the top mass. In this "boosted regime", large QCD corrections can arise both from softgluon emissions and from emissions collinear to the energetic top quarks, which become singular in the boosted limit. In this talk I discuss a theoretical framework which can be used to resum both types of potentially large corrections in the boosted regime, and compare some of its numerical predictions for differential cross sections with LHC data.
The top quark mass is one of the most important standard model parameters. The most precise method for top mass extraction comes from kinematic extraction. However, there's an O(1) GeV theory uncertainty associated with the fact these methods rely on Monte Carlo simulations which do not have a fully specified field theoretic mass scheme definition. I will describe our proposal for using a 2jettiness variable with a boosted top sample to extract the top mass at the LHC. This variable obeys a factorization theorem which allow the associated cross section to be calculated with a well defined top mass scheme, and has the same strong sensitivity as the currently used template method.

Boosted toptagging
We study the detector performance with an emphasis on jet substructure variables for extremely boosted objects at very high energy proton colliders using Geant4 simulation. We focus on the calorimeter performance and study hadronicallydecaying W bosons with transverse momentum in the multiTeV range (520 TeV). The calorimeter segmentation is benchmarked in order to understand the impact of granularity and resolution on boosted boson discrimination.
Abstract: The linear collider experiments require excellent performance of jet clustering algorithms in highenergy electronpositron with nonnegligible gamma gamma > hadrons background. The ILC and CLIC detector concepts have studied the performance of several algorithms under realistic conditions and with a detailed model of the detector response. Results on jet energy and substructure response are presented for several key benchmark processes. The identification of boosted objects in TeV electronpositron collisions is also discussed.

In this talk I will introduce our recent work about factorization and resummation for jet processes. From a detailed analysis of StermanWeinberg conejet cross sections in effective field theory, we obtain novel factorization theorems which separate the physics associated with different energy scales present in such processes. The relevant lowenergy physics is encoded in Wilson lines along the directions of the energetic particles inside the jets. This multiWilsonline structure is present even for narrowcone jets due to the relevance of smallangle soft radiation. We discuss the renormalizationgroup equations satisfied by these operators. Their solution resums all logarithmically enhanced contributions to such processes, including nonglobal logarithms. Such logarithms arise in many observables, in particular whenever hard phasespace constraints are imposed, and are not captured with standard resummation techniques. Our formalism provides the basis for higherorder logarithmic resummations of jet and other nonglobal observables. As a nontrivial consistency check, we use it to obtain explicit twoloop results for all logarithmically enhanced terms in conejet cross sections and verify those against numerical fixedorder computations. This talk is based on arXiv:1508.06645, arXiv:1605.02737 and some recent progress about numerical results.
Pileup mitigation techniques
Measuring inclusive quantities, both global (missing and sum transverse energy) and local (jet mass and substructure), after the high luminosity LHC upgrade will be extremely challenging, and will require new pileup mitigation techniques that correct more than local jet energies. To this end, one can use the fact that pileup has no angular structure while hard processes are characterised by smallangle emissions and are therefore highly sparse in the frequency domain. Using wavelet functions, intermediates between a standard pixel basis and a Fourier basis, which are localised in position ($y  \phi$) as well as frequency (angular) space, we can naturally and efficiently perform an eventwide classification of signal and pileup particles by filtering in the frequency domain. In this talk, we will motivate the use of wavelets in high energy physics, describe the procedure behind a wavelet analysis, and present a few concrete methods and results. In particular, using a generatorlevel overlay of signal and pileup events, we demonstrate that, using wavelet techniques, a significant improvement in e.g.~missing transverse energy reconstruction may be possible even up to $\langle\mu\rangle$ of 300 or beyond.



Jet mass response ATLAS
SUSY using boosted techniques

New resonances decaying to boosted Higgs signatures

New resonances decaying to boosted diboson signatures (no abstract received)
Building on the jetimage based representation of high energy jets, we develop computer vision based techniques for jettagging through the use of Deep Neural Networks. Jetimages enabled the connection between jet substructure and tagging with the fields of computer vision and image processing. We show how applying such techniques using Deep Neural Networks can improve the performance to identify highly boosted W bosons with respect to stateoftheart substructure methods. In addition, we explore new ways to extract and visualize the discriminating features of different classes of jets, adding a new capability to understand the physics within jets and to design more powerful jet tagging methods.
Deducing whether the substructure of an observed jet is due to a lowmass single particle or due to multiple decay objects of a massive particle is an important problem in the analysis of collider data. Traditional approaches have relied on expert features designed to detect energy deposition patterns in the calorimeter, but the complexity of the data make this task an excellent candidate for the application of machine learning tools. The data collected by the detector can be treated as a twodimensional image, lending itself to the natural application of image classification techniques. In this work, we apply deep neural networks with a mixture of locallyconnected and fullyconnected nodes. Our experiments demonstrate that without the aid of expert features, such networks match or modestly outperform the current stateoftheart approach for discriminating between jets from single hadronic particles and overlapping jets from pairs of collimated hadronic particles, and that such performance gains persist in the presence of pileup interactions.
In addition, we will present initial studies on using deep networks to perform btagging inside boosted objects.
We present a new approach for efficiently and selectively identifying highmomentum, hadronically decaying top quarks, Higgs bosons, and W and Z bosons, distinguishing them from jets from light quarks and gluons in protonproton collisions at the LHC or future colliders. This technique yields variables that can be combined with those from current approaches to boosted particle tagging in multivariate classifiers such as deep neural networks or boosted decision trees to yield estimators which can be used in a broad range of analyses in which such highly boosted particles play a role. The technique is capable of identifying subjets which overlap strongly in the tracking and calorimetry systems, allowing good performance even in the multiTeV regime. The performance of the method is studied in various scenarios and shows promise for actual use at the LHC experiments.
We present a new algorithm developed for the identification of boosted heavy particles at the LHC, the Heavy Object Tagger with Variable R (HOTVR). The algorithm is based on jet clustering with a variable distance parameter $R$ combined with a mass jump condition. The variable $R$ approach adapts the jet size to the transverse momentum $p_T$, resulting in smaller jets for increasing values of $p_T$, making the jet mass less susceptible to radiation. Two and three prong decays are identified using subjets, formed by the mass jump condition. The resulting algorithm combines the jet clustering, subjet finding and rejection of soft clusters in one step, making it robust and simple. We present performance tests for the identification of boosted top quarks, which show that the HOTVR algorithm has similar or better performance over a large range in $p_T$ compared to other algorithms commonly used at the LHC.

Jet performance in Run 2

It has recently been shown that the Ysplitter method with trimming is a very effective method for tagging boosted electroweak bosons, outperforming several standard taggers at high pt. Here we analytically investigate this observation and explain the performance of Ysplitter with a range of grooming techniques from first principles of QCD. We also suggest modifications that considerably simplify the analytical results, thereby increasing robustness, and make the results largely independent of the details of grooming.
Heavy Ion zfragmentation measurement in pp and PbPb
Massive vectorlike quarks using boosted particle reconstructions
