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20142023
most citedFast inference of deep neural networks in FPGAs for particle physics

438 citations · 3.2k across the 52 of their papers we have counts for

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18 papers · 1 filter

hep-ph2022

Report of the Topical Group on Physics Beyond the Standard Model at Energy Frontier for Snowmass 2021

Tulika Bose, Antonio Boveia, Caterina Doglioni +318

This is the Snowmass2021 Energy Frontier (EF) Beyond the Standard Model (BSM) report. It combines the EF topical group reports of EF08 (Model-specific explorations), EF09 (More gen…

hep-ph2022★ 13 cited

Dark Sector Physics at High-Intensity Experiments

Stefania Gori, Mike Williams, Phil Ilten +10

Is Dark Matter part of a Dark Sector? The possibility of a dark sector neutral under Standard Model (SM) forces furnishes an attractive explanation for the existence of Dark Matter…

hep-ph2022★ 17 cited

Neural Embedding: Learning the Embedding of the Manifold of Physics Data

Sang Eon Park, Philip Harris, Bryan Ostdiek

In this paper, we present a method of embedding physics data manifolds with metric structure into lower dimensional spaces with simpler metrics, such as Euclidean and Hyperbolic sp…

hep-ph2022★ 3 cited

Snowmass White Paper: New flavors and rich structures in dark sectors

Philip Harris, Philip Schuster, Jure Zupan

Dark matter can be part of a dark sector with non-minimal couplings to the Standard Model. Compared to many (minimal) benchmark models, such scenarios can result in significant mod…

hep-ph2022★ 4 cited

Summarizing experimental sensitivities of collider experiments to dark matter models and comparison to other experiments

Antonio Boveia, Caterina Doglioni, Boyu Gao +19

Comparisons of the coverage of current and proposed dark matter searches can help us to understand the context in which a discovery of particle dark matter would be made. In some s…

hep-ph2022★ 4 cited

Displaying dark matter constraints from colliders with varying simplified model parameters

Andreas Albert, Antonio Boveia, Oleg Brandt +17

The search for dark matter is one of the main science drivers of the particle and astroparticle physics communities. Determining the nature of dark matter will require a broad appr…

hep-ph2021★ 56 cited

Unveiling Hidden Physics at the LHC

Oliver Fischer, Bruce Mellado, Stefan Antusch +30

The field of particle physics is at the crossroads. The discovery of a Higgs-like boson completed the Standard Model (SM), but the lacking observation of convincing resonances Beyo…

hep-ph2021★ 42 cited

The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics

Gregor Kasieczka, Benjamin Nachman, David Shih +44

A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. I…

hep-ph2020★ 53 cited

Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge

Sang Eon Park, Dylan Rankin, Silviu-Marian Udrescu +2

Discoveries of new phenomena often involve a dedicated search for a hypothetical physics signature. Recently, novel deep learning techniques have emerged for anomaly detection in t…

hep-ph2019★ 1 cited

An approach to constraining the Higgs width at the LHC and HL-LHC

Philip Coleman Harris, Dylan Sheldon Rankin, Cristina Mantilla Suarez

Despite the discovery of the Higgs boson decay in five separate channels many parameters of the Higgs boson remain largely unconstrained. In this paper, we present a new approach t…

hep-ph2018

Novel tools and observables for jet physics in heavy-ion collisions

Harry Arthur Andrews, Liliana Apolinario, Redmer Alexander Bertens +25

Studies of fully-reconstructed jets in heavy-ion collisions aim at extracting thermodynamical and transport properties of hot and dense QCD matter. Recently, a plethora of new jet…

hep-ph2018★ 73 cited

Machine Learning Uncertainties with Adversarial Neural Networks

Christoph Englert, Peter Galler, Philip Harris +1

Machine Learning is a powerful tool to reveal and exploit correlations in a multi-dimensional parameter space. Making predictions from such correlations is a highly non-trivial tas…

hep-ph2017★ 17 cited

Fractal based observables to probe jet substructure of quarks and gluons

Joe Davighi, Philip Harris

New jet observables are defined which characterize both fractal and scale-dependent contributions to the distribution of hadrons in a jet. These infrared safe observables, named Ex…

hep-ph2015★ 28 cited

Closing up on Dark Sectors at Colliders: from 14 to 100 TeV

Philip Harris, Valentin V. Khoze, Michael Spannowsky +1

We investigate the reach of the LHC Run 2 and that of a future circular hadron collider with up to 100 TeV centre of mass energy for the exploration of potential Dark Matter sector…

hep-ph2015★ 11 cited

Towards an Understanding of the Correlations in Jet Substructure

D. Adams, A. Arce, L. Asquith +83

Over the past decade, a large number of jet substructure observables have been proposed in the literature, and explored at the LHC experiments. Such observables attempt to utilize…

hep-ph2014★ 140 cited

Constraining Dark Sectors at Colliders: Beyond the Effective Theory Approach

Philip Harris, Valentin V. Khoze, Michael Spannowsky +1

We outline and investigate a set of benchmark simplified models with the aim of providing a minimal simple framework for an interpretation of the existing and forthcoming searches…

hep-ph2014★ 385 cited

Pileup Per Particle Identification

Daniele Bertolini, Philip Harris, Matthew Low +1

We propose a new method for pileup mitigation by implementing "pileup per particle identification" (PUPPI). For each particle we first define a local shape which probes the col…

hep-ph2014★ 79 cited

Constraining CP-violating Higgs Sectors at the LHC using gluon fusion

Matthew J. Dolan, Philip Harris, Martin Jankowiak +1

We investigate the constraints that the LHC can set on a 126 GeV Higgs boson that is an admixture of CP eigenstates. Traditional analyses rely on Higgs couplings to massive vector…