117 research outputs found
QCD Analysis of the Scale-Invariance of Jets
Studying the substructure of jets has become a powerful tool for event
discrimination and for studying QCD. Typically, jet substructure studies rely
on Monte Carlo simulation for vetting their usefulness; however, when possible,
it is also important to compute observables with analytic methods. Here, we
present a global next-to-leading-log resummation of the angular correlation
function which measures the contribution to the mass of a jet from constituents
that are within an angle R with respect to one another. For a scale-invariant
jet, the angular correlation function should scale as a power of R. Deviations
from this behavior can be traced to the breaking of scale invariance in QCD. To
do the resummation, we use soft-collinear effective theory relying on the
recent proof of factorization of jet observables at e+ e- colliders.
Non-trivial requirements of factorization of the angular correlation function
are discussed. The calculation is compared to Monte Carlo parton shower and
next-to-leading order results. The different calculations are important in
distinct phase space regions and exhibit that jets in QCD are, to very good
approximation, scale invariant over a wide dynamical range.Comment: Updated to PRD version, added discussion of relative importance of
NLL vs. NLO contribution
Conformal Invariance of the Subleading Soft Theorem in Gauge Theory
In this note, I show that the recently proposed subleading soft factor in
massless gauge theory uniquely follows from conformal symmetry of tree-level
gauge theory amplitudes in four dimensions.Comment: v1: 6 pages, no figures, JHEP style; v2: 7 pages, added some
discussion and references; v3: 5 pages, PRD accepted version, minor wording
change
Unsafe but Calculable: Ratios of Angularities in Perturbative QCD
Infrared- and collinear-safe (IRC-safe) observables have finite cross
sections to each fixed-order in perturbative QCD. Generically, ratios of
IRC-safe observables are themselves not IRC safe and do not have a valid
fixed-order expansion. Nevertheless, in this paper we present an explicit
method to calculate the cross section for a ratio observable in perturbative
QCD with the help of resummation. We take the IRC-safe jet angularities as an
example and consider the ratio formed from two angularities with different
angular exponents. While the ratio observable is not IRC safe, it is "Sudakov
safe", meaning that the perturbative Sudakov factor exponentially suppresses
the singular region of phase space. At leading logarithmic (LL) order, the
distribution is finite but has a peculiar expansion in the square root of the
strong coupling constant, a consequence of IRC unsafety. The accuracy of the LL
distribution can be further improved with higher-order resummation and
fixed-order matching. Non-perturbative effects can sometimes give rise to order
one changes in the distribution, but at sufficiently high energies Q, Sudakov
safety leads to non-perturbative corrections that scale like a (fractional)
power of 1/Q, as is familiar for IRC-safe observables. We demonstrate that
Monte Carlo parton showers give reliable predictions for the ratio observable,
and we discuss the prospects for computing other ratio observables using our
method.Comment: 41 pages, 14 figures, 1 table, small changes in v.
How Much Information is in a Jet?
Machine learning techniques are increasingly being applied toward data
analyses at the Large Hadron Collider, especially with applications for
discrimination of jets with different originating particles. Previous studies
of the power of machine learning to jet physics has typically employed image
recognition, natural language processing, or other algorithms that have been
extensively developed in computer science. While these studies have
demonstrated impressive discrimination power, often exceeding that of
widely-used observables, they have been formulated in a non-constructive manner
and it is not clear what additional information the machines are learning. In
this paper, we study machine learning for jet physics constructively,
expressing all of the information in a jet onto sets of observables that
completely and minimally span N-body phase space. For concreteness, we study
the application of machine learning for discrimination of boosted, hadronic
decays of Z bosons from jets initiated by QCD processes. Our results
demonstrate that the information in a jet that is useful for discrimination
power of QCD jets from Z bosons is saturated by only considering observables
that are sensitive to 4-body (8 dimensional) phase space.Comment: 14 pages + appendices, 10 figures; v2: JHEP version, updated neural
network, included deeper network and boosted decision tree result
Aspects of Jets at 100 TeV
We present three case studies at a 100 TeV proton collider for how jet
analyses can be improved using new jet (sub)structure techniques. First, we use
the winner-take-all recombination scheme to define a recoil-free jet axis that
is robust against pileup. Second, we show that soft drop declustering is an
effective jet grooming procedure that respects the approximate scale invariance
of QCD. Finally, we highlight a potential standard candle for jet calibration
using the soft-dropped energy loss. This latter observable is remarkably
insensitive to the scale and flavor of the jet, a feature that arises because
it is infrared/collinear unsafe, but Sudakov safe.Comment: 9 pages, double column, 7 figures, based on a talk by A.L. at the
"Workshop on Physics at a 100 TeV Collider" at SLAC from April 23-25, 2014;
v.2: PRD versio
Automating the Construction of Jet Observables with Machine Learning
Machine-learning assisted jet substructure tagging techniques have the
potential to significantly improve searches for new particles and Standard
Model measurements in hadronic final states. Techniques with simple analytic
forms are particularly useful for establishing robustness and gaining physical
insight. We introduce a procedure to automate the construction of a large class
of observables that are chosen to completely specify -body phase space. The
procedure is validated on the task of distinguishing
from , where and previous brute-force approaches
to construct an optimal product observable for the -body phase space have
established the baseline performance. We then use the new method to design
tailored observables for the boosted search, where and brute-force
methods are intractable. The new classifiers outperform standard -prong
tagging observables, illustrating the power of the new optimization method for
improving searches and measurement at the LHC and beyond.Comment: 15 pages, 8 tables, 12 figure
Constructing Amplitudes from Their Soft Limits
The existence of universal soft limits for gauge-theory and gravity
amplitudes has been known for a long time. The properties of the soft limits
have been exploited in numerous ways; in particular for relating an n-point
amplitude to an (n-1)-point amplitude by removing a soft particle. Recently, a
procedure called inverse soft was developed by which "soft" particles can be
systematically added to an amplitude to construct a higher-point amplitude for
generic kinematics. We review this procedure and relate it to
Britto-Cachazo-Feng-Witten recursion. We show that all tree-level amplitudes in
gauge theory and gravity up through seven points can be constructed in this
way, as well as certain classes of NMHV gauge-theory amplitudes with any number
of external legs. This provides us with a systematic procedure for constructing
amplitudes solely from their soft limits.Comment: minor change
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