52 research outputs found
(Machine) Learning to Do More with Less
Determining the best method for training a machine learning algorithm is
critical to maximizing its ability to classify data. In this paper, we compare
the standard "fully supervised" approach (that relies on knowledge of
event-by-event truth-level labels) with a recent proposal that instead utilizes
class ratios as the only discriminating information provided during training.
This so-called "weakly supervised" technique has access to less information
than the fully supervised method and yet is still able to yield impressive
discriminating power. In addition, weak supervision seems particularly well
suited to particle physics since quantum mechanics is incompatible with the
notion of mapping an individual event onto any single Feynman diagram. We
examine the technique in detail -- both analytically and numerically -- with a
focus on the robustness to issues of mischaracterizing the training samples.
Weakly supervised networks turn out to be remarkably insensitive to systematic
mismodeling. Furthermore, we demonstrate that the event level outputs for
weakly versus fully supervised networks are probing different kinematics, even
though the numerical quality metrics are essentially identical. This implies
that it should be possible to improve the overall classification ability by
combining the output from the two types of networks. For concreteness, we apply
this technology to a signature of beyond the Standard Model physics to
demonstrate that all these impressive features continue to hold in a scenario
of relevance to the LHC.Comment: 32 pages, 12 figures. Example code is provided at
https://github.com/bostdiek/PublicWeaklySupervised . v3: Version published in
JHEP, discussion adde
Comment on measuring the t-tbar forward-backward asymmetry at ATLAS and CMS
We suggest a new possibility for ATLAS and CMS to explore the t-tbar
forward-backward asymmetry measured at the Tevatron, by attempting to
reconstruct t-tbar events, with one of the tops decaying semileptonically in
the central region (|\eta| < 2.5) and the other decaying hadronically in the
forward region (|\eta| > 2.5). For several models which give comparable
Tevatron signals, we study the charge asymmetry at the LHC as a function of
cuts on |\eta| and on the t-tbar invariant mass, m_{t-tbar}. We show that there
is an interesting complementarity between cuts on |\eta| and m_{t-tbar} to
suppress the dominant and symmetric gg -> t-tbar rate, and different
combinations of cuts enhance the distinguishing power between models. This
complementarity is likely to hold in other new physics scenarios as well, which
affect the t-tbar cross section, so it motivates extending t-tbar
reconstruction to higher |\eta|.Comment: 6 pages, 3 figures, 3 tables, v2: to match version appearing in PRD,
resolution in figures improve
Gamma-rays from Dark Showers with Twin Higgs Models
We consider a twin WIMP scenario whose twin sector contains a full dark copy
of the SM hadrons, where the lightest twin particles are twin pions. By analogy
to the standard WIMP paradigm, the dark matter (DM) freezes out through twin
electroweak interactions, and annihilates into a dark shower of light twin
hadrons. These are either stable or decay predominantly to standard model (SM)
photons. We show that this 'hadrosymmetric' scenario can be consistent with all
applicable astrophysical, cosmological and collider constraints. In order to
decay the twin hadrons before the big-bang nucleosynthesis epoch, an additional
portal between the SM and twin sector is required. In most cases we find this
additional mediator is within reach of either the LHC or future intensity
frontier experiments. Furthermore, we conduct simulations of the dark shower
and consequent photon spectra. We find that fits of these spectra to the
claimed galactic center gamma-ray excess seen by Fermi-LAT non-trivially
coincide with regions of parameter space that both successfully generate the
observed DM abundance and exhibit minimal fine-tuning.Comment: 45 pages, 11 figures, v2: journal version, extended discussions in
Secs. III-V, references adde
Simulating collider physics on quantum computers using effective field theories
Simulating the full dynamics of a quantum field theory over a wide range of
energies requires exceptionally large quantum computing resources. Yet for many
observables in particle physics, perturbative techniques are sufficient to
accurately model all but a constrained range of energies within the validity of
the theory. We demonstrate that effective field theories (EFTs) provide an
efficient mechanism to separate the high energy dynamics that is easily
calculated by traditional perturbation theory from the dynamics at low energy
and show how quantum algorithms can be used to simulate the dynamics of the low
energy EFT from first principles. As an explicit example we calculate the
expectation values of vacuum-to-vacuum and vacuum-to-one-particle transitions
in the presence of a time-ordered product of two Wilson lines in scalar field
theory, an object closely related to those arising in EFTs of the Standard
Model of particle physics. Calculations are performed using simulations of a
quantum computer as well as measurements using the IBMQ Manhattan machine.Comment: 5 pages, plus 11 pages of Supplemental Material
Boosting with Machine Learning
High Higgs production at hadron colliders provides a direct probe of
the internal structure of the loop with the decay
offering the most statistics due to the large branching ratio. Despite the
overwhelming QCD background, recent advances in jet substructure have put the
observation of the channel at the LHC within the realm
of possibility. In order to enhance the sensitivity to this process, we develop
a two stream convolutional neural network, with one stream acting on jet
information and one using global event properties. The neural network
significantly increases the discovery potential of a Higgs signal, both for
high Standard Model production as well for possible beyond the Standard
Model contributions. Unlike most studies for boosted hadronically decaying
massive particles, the boosted Higgs search is unique because double
-tagging rejects nearly all background processes that do not have two hard
prongs. In this context --- which goes beyond state-of-the-art two-prong
tagging --- the network is studied to identify the origin of the additional
information leading to the increased significance. The procedures described
here are also applicable to related final states where they can be used to
identify additional sources of discrimination power that are not being
exploited by current techniques.Comment: 26 pages, 12 figures. v3: Updated to journal versio
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