arXiv subjects

Daniel Whiteson

Publications and source records attributed to Daniel Whiteson.

At least 73 records · Page 4Linked to original sources

arXiv2020

Resonance Searches with Machine Learned Likelihood Ratios

We demonstrate the power of machine-learned likelihood ratios for resonance searches in a benchmark model featuring a heavy Z' boson. The likelihood ratio is expressed as a function of multivariate detector level observables, but rather than being calculated explicitly as in matrix-element-based approaches, it is learned from a joint likelihood ratio which depends on latent information from simulated samples. We show that bounds drawn using the machine learned likelihood ratio are tighter than those drawn using a likelihood ratio calculated from histograms.

hep-ph↗

arXiv2019

The motivation and status of two-body resonance decays after the LHC Run 2 and beyond

Searching for two-body resonance decays is a central component of the high energy physics energy frontier research program. While many of the possibilities are covered when the two bodies are Standard Model (SM) particles, there are still significant gaps. If one or both of the bodies are themselves non-SM particles, there is very little coverage from existing searches. We review the status of two-body searches and motivate the need to search for the missing combinations. It is likely that the search program of the future will be able to cover all possibilities with a combination of dedicated and model agnostic search approaches.

hep-ph↗

arXiv2018

Deep Learning and its Application to LHC Physics

Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex problems than previously feasible. This review is aimed at the reader who is familiar with high energy physics but not machine learning. The connections between machine learning and high energy physics data analysis are explored, followed by an introduction to the core concepts of neural networks, examples of the key results demonstrating the power of deep learning for analysis of LHC data, and discussion of future prospects and concerns.

hep-ex↗

arXiv2017

Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes

We describe a procedure for constructing a model of a smooth data spectrum using Gaussian processes rather than the historical parametric description. This approach considers a fuller space of possible functions, is robust at increasing luminosity, and allows us to incorporate our understanding of the underlying physics. We demonstrate the application of this approach to modeling the background to searches for dijet resonances at the Large Hadron Collider and describe how the approach can be used in the search for generic localized signals.

physics.data-an↗

arXiv2017

Decorrelated Jet Substructure Tagging using Adversarial Neural Networks

We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass. This reduces the impact of systematic uncertainties in background modeling while enhancing signal purity, resulting in improved discovery significance relative to existing taggers. The network is trained using an adversarial strategy, resulting in a tagger that learns to balance classification accuracy with decorrelation. As a benchmark scenario, we consider the case where large-radius jets originating from a boosted resonance decay are discriminated from a background of nonresonant quark and gluon jets. We show that in the presence of systematic uncertainties on the background rate, our adversarially-trained, decorrelated tagger considerably outperforms a conventionally trained neural network, despite having a slightly worse signal-background separation power. We generalize the adversarial training technique to include a parametric dependence on the signal hypothesis, training a single network that provides optimized, interpolatable decorrelated jet tagging across a continuous range of hypothetical resonance masses, after training on discrete choices of the signal mass.

hep-ex↗

arXiv2016

Searching for spin-3/2 leptons

A model of spin-3/2 vector-like leptons is introduced, which serve as an alternative probe of possible lepton compositeness. Possible signatures at the Large Hadron Collider are explored, and current data are used to constrain the masses of the hypothetical new leptons.

hep-ph↗

arXiv2016

The unexplored landscape of two-body resonances

We propose a strategy for searching for theoretically-unanticipated new physics which avoids a large trials factor by focusing on experimental strengths. Searches for resonances decaying into pairs of visible particles are experimentally very powerful due to the localized mass peaks and have a rich history of discovery. Yet, due to a focus on subsets of theoretically-motivated models, the landscape of such resonances is far from thoroughly explored. We survey the existing set of searches, identify untapped experimental opportunities and discuss the theoretical constraints on models which would generate such resonances.

hep-ph↗

arXiv2016

Jet Flavor Classification in High-Energy Physics with Deep Neural Networks

Classification of jets as originating from light-flavor or heavy-flavor quarks is an important task for inferring the nature of particles produced in high-energy collisions. The large and variable dimensionality of the data provided by the tracking detectors makes this task difficult. The current state-of-the-art tools require expert data-reduction to convert the data into a fixed low-dimensional form that can be effectively managed by shallow classifiers. We study the application of deep networks to this task, attempting classification at several levels of data, starting from a raw list of tracks. We find that the highest-level lowest-dimensionality expert information sacrifices information needed for classification, that the performance of current state-of-the-art taggers can be matched or slightly exceeded by deep-network-based taggers using only track and vertex information, that classification using only lowest-level highest-dimensionality tracking information remains a difficult task for deep networks, and that adding lower-level track and vertex information to the classifiers provides a significant boost in performance compared to the state-of-the-art.

hep-ex↗

arXiv2016

Jet Substructure Classification in High-Energy Physics with Deep Neural Networks

At the extreme energies of the Large Hadron Collider, massive particles can be produced at such high velocities that their hadronic decays are collimated and the resulting jets overlap. Deducing whether the substructure of an observed jet is due to a low-mass 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 two-dimensional image, lending itself to the natural application of image classification techniques. In this work, we apply deep neural networks with a mixture of locally-connected and fully-connected nodes. Our experiments demonstrate that without the aid of expert features, such networks match or modestly outperform the current state-of-the-art 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.

hep-ex↗

arXiv2016

Boosting low-mass hadronic resonances

Searches for new hadronic resonances typically focus on high-mass spectra, due to overwhelming QCD backgrounds and detector trigger rates. We present a study of searches for relatively low-mass hadronic resonances at the LHC in the case that the resonance is boosted by recoiling against a well-measured high-$p_{\textrm{T}}$ probe such as a muon, photon or jet. The hadronic decay of the resonance is then reconstructed either as a single large-radius jet or as a resolved pair of standard narrow-radius jets, balanced in transverse momentum to the probe. We show that the existing 2015 LHC dataset of $pp$ collisions with $\int\mathcal{L}dt = 4\ \mathrm{fb}^{-1}$ should already have powerful sensitivity to a generic $Z'$ model which couples only to quarks, for $Z'$ masses ranging from 20-500 GeV/c$^2$.

hep-ph↗

arXiv2016

Parameterized Machine Learning for High-Energy Physics

We investigate a new structure for machine learning classifiers applied to problems in high-energy physics by expanding the inputs to include not only measured features but also physics parameters. The physics parameters represent a smoothly varying learning task, and the resulting parameterized classifier can smoothly interpolate between them and replace sets of classifiers trained at individual values. This simplifies the training process and gives improved performance at intermediate values, even for complex problems requiring deep learning. Applications include tools parameterized in terms of theoretical model parameters, such as the mass of a particle, which allow for a single network to provide improved discrimination across a range of masses. This concept is simple to implement and allows for optimized interpolatable results.

hep-ex↗

arXiv2015

Observing Ultra-High Energy Cosmic Rays with Smartphones

We propose a novel approach for observing cosmic rays at ultra-high energy ($>10^{18}$~eV) by repurposing the existing network of smartphones as a ground detector array. Extensive air showers generated by cosmic rays produce muons and high-energy photons, which can be detected by the CMOS sensors of smartphone cameras. The small size and low efficiency of each sensor is compensated by the large number of active phones. We show that if user adoption targets are met, such a network will have significant observing power at the highest energies.

astro-ph.IM↗

arXiv2015

Erratum: Bounds on Invisible Higgs boson Decays from $t\bar{t}H$ Production

Bounds on invisible decays of the Higgs boson from $t\bar{t}H$ production were inferred from a CMS search for stop quarks decaying to $t\bar{t}$ and missing transverse momentum. Limits on the production of $t\bar{t}H$ relied on the efficiency of the CMS selection for $t\bar{t}H$, as measured in a simulated sample. An error in the generation of the simulated sample lead to a significant overestimate of the selection efficiency. Corrected results are presented.

hep-ph↗

arXiv2015

Mono-Z': searches for dark matter in events with a resonance and missing transverse energy

We analyze the potential dark matter implications of LHC events with missing transverse momentum and a resonance, such as a Z', decaying to a pair of jets or leptons. This final state contains significant discovery potential, but has not yet been examined in detail by the LHC experiments. We introduce models of Z' production in association with dark matter particles, propose reconstruction and selection strategies, and estimate the sensitivity of the current LHC dataset.

hep-ph↗

arXiv2014

Enhanced Higgs to $τ^+τ^-$ Searches with Deep Learning

The Higgs boson is thought to provide the interaction that imparts mass to the fundamental fermions, but while measurements at the Large Hadron Collider (LHC) are consistent with this hypothesis, current analysis techniques lack the statistical power to cross the traditional 5$σ$ significance barrier without more data. \emph{Deep learning} techniques have the potential to increase the statistical power of this analysis by \emph{automatically} learning complex, high-level data representations. In this work, deep neural networks are used to detect the decay of the Higgs to a pair of tau leptons. A Bayesian optimization algorithm is used to tune the network architecture and training algorithm hyperparameters, resulting in a deep network of eight non-linear processing layers that improves upon the performance of shallow classifiers even without the use of features specifically engineered by physicists for this application. The improvement in discovery significance is equivalent to an increase in the accumulated dataset of 25\%.

hep-ph↗

arXiv2014

Mono-Higgs: a new collider probe of dark matter

We explore the LHC phenomenology of dark matter (DM) pair production in association with a 125 GeV Higgs boson. This signature, dubbed `mono-Higgs,' appears as a single Higgs boson plus missing energy from DM particles escaping the detector. We perform an LHC background study for mono-Higgs signals at $\sqrt{s} = 8$ and $14$ TeV for four Higgs boson decay channels: $γγ$, $b \bar b$, and $ZZ^* \to 4\ell$, $\ell\ell j j$. We estimate the LHC sensitivities to a variety of new physics scenarios within the frameworks of both effective operators and simplified models. For all these scenarios, the $γγ$ channel provides the best sensitivity, whereas the $b\bar b$ channel suffers from a large $t \bar t$ background. Mono-Higgs is unlike other mono-$X$ searches ($X$=jet, photon, etc.), since the Higgs boson is unlikely to be radiated as initial state radiation, and therefore probes the underlying DM vertex directly.

hep-ph↗

arXiv2014

Searching for Exotic Particles in High-Energy Physics with Deep Learning

Collisions at high-energy particle colliders are a traditionally fruitful source of exotic particle discoveries. Finding these rare particles requires solving difficult signal-versus-background classification problems, hence machine learning approaches are often used. Standard approaches have relied on `shallow' machine learning models that have a limited capacity to learn complex non-linear functions of the inputs, and rely on a pain-staking search through manually constructed non-linear features. Progress on this problem has slowed, as a variety of techniques have shown equivalent performance. Recent advances in the field of deep learning make it possible to learn more complex functions and better discriminate between signal and background classes. Using benchmark datasets, we show that deep learning methods need no manually constructed inputs and yet improve the classification metric by as much as 8\% over the best current approaches. This demonstrates that deep learning approaches can improve the power of collider searches for exotic particles.

hep-ph↗

arXiv2014

Collider Bounds on Indirect Dark Matter Searches: The $WW$ Final State

We describe an effective theory of interaction between pairs of dark matter particles (denoted $χ$) and pairs of $W$ bosons. Such an interaction could accommodate $χ\barχ\rightarrow WW$ processes, which are a major focus of indirect dark matter experiments, as well as $pp \rightarrow W\rightarrow Wχ\barχ$ processes, which would predict excesses at the LHC in the $W$+MET final-state. We reinterpret an ATLAS $W$+MET analysis in the hadronic mode and translate the bounds to the space of indirect detection signals. We also reinterpret the $W$+MET analysis in terms of graviton theory through the processes $W\rightarrow WG$ and $Z\rightarrow ZG$ in which $G$ is invisible. Finally, the final state is interpreted in terms of a $W'$ model where $W'\rightarrow WZ$, where $W$ decays hadronically and $Z$ decays to neutrinos.

hep-ph↗