Edge Detecting New Physics the Voronoi Way

Debnath, Dipsikha; Gainer, James S.; Kim, Doojin; Matchev, K.

doi:10.48550/arxiv.1506.04141

Cited by 5 publications

(38 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the purpose of quantifying the correlations, we introduce an Ising spin lattice with suitably defined nearestneighbor spin-spin interactions (alternative approaches rely on neural-networks [34,35] or wavelet transforms 1 There are alternative approaches which try to avoid (to varying degrees) the reliance on a background prediction from Monte Carlo. These include traditional bump-hunting methods, edge detection techniques [16,17] and recent machine-learning based approaches [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. However, given the spectacular success of the SM in describing current data, its theoretical prediction of the background is well under control and should not be ignored.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A quantum algorithm for model independent searches for new physics

Matchev,

Shyamsundar,

Smolinsky

2020

Preprint

Self Cite

View full text Add to dashboard Cite

We propose a novel quantum computing based technique to search for unmodelled anomalies in multi-dimensional binned collider data. We propose to associate an Ising lattice spin site with each bin, with the Ising Hamiltonian suitably constructed from the observed data and a corresponding theoretical expectation. In order to capture spatially correlated anomalies in the data, we introduce spin-spin interactions between neighboring sites, as well as self-interactions. The ground state energy of the resulting Ising Hamiltonian can be used as a new test statistic, which can be computed via adiabatic quantum optimization as implemented, e.g., in D-wave. We demonstrate that our test statistic outperforms some of the most commonly used goodness-of-fit tests. The new approach greatly reduces the look-elsewhere effect by exploiting the typical differences between statistical noise and genuine new physics signals.

show abstract

mentioning

confidence: 99%

“…3 we take n to be relatively low, n = 4. This allows us to find the minimum energy H min by brute force, i.e., by inspecting each of the 2 N = 2 16 comparing the corresponding energies. Then in Fig.…”

mentioning

confidence: 99%

A quantum algorithm for model independent searches for new physics

Matchev,

Shyamsundar,

Smolinsky

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Here, instead of looking for an excess in the signal region P SR , we shall follow up on the idea of Refs. [16,17] to target directly the boundary ∂P N P of the NP phase space region P N P , by using the fact that the combined distribution (1.2) is non-differentiable anywhere on ∂P N P where f N P is non-vanishing. As it turns out, the latter is a very safe assumption -if anything, f N P is not only non-vanishing, but often enhanced and even singular on the boundary ∂P N P [18][19][20][21][22][23][24][25][26][27].…”

mentioning

confidence: 99%

“…As it turns out, the latter is a very safe assumption -if anything, f N P is not only non-vanishing, but often enhanced and even singular on the boundary ∂P N P [18][19][20][21][22][23][24][25][26][27]. Since the background distribution f SM is a smooth function across ∂P N P , the presence of f N P creates a discontinuous "jump" in the combined event density (1.2), precisely at the location of the boundary ∂P N P [16,17]. We can thus reformulate the original problem of finding evidence of NP in the collider data as follows:…”

mentioning

confidence: 99%

See 1 more Smart Citation

Finding Wombling Boundaries in LHC Data with Voronoi and Delaunay Tessellations

Matchev,

Roman,

Shyamsundar

2020

Preprint

Self Cite

View full text Add to dashboard Cite

We address the problem of finding a wombling boundary in point data generated by a general Poisson point process, a specific example of which is an LHC event sample distributed in the phase space of a final state signature, with the wombling boundary created by some new physics. We discuss the use of Voronoi and Delaunay tessellations of the point data for estimating the local gradients and investigate methods for sharpening the boundaries by reducing the statistical noise. The outcome from traditional wombling algorithms is a set of boundary cell candidates with relatively large gradients, whose spatial properties must then be scrutinized in order to construct the boundary and evaluate its significance. Here we propose an alternative approach where we simultaneously form and evaluate the significance of all possible boundaries in terms of the total gradient flux. We illustrate our method with several toy examples of both straight and curved boundaries with varying amounts of signal present in the data.

show abstract

Singularity variables for missing energy event kinematics

Matchev

Shyamsundar

2020

J. High Energ. Phys.

Self Cite

View full text Add to dashboard Cite

We discuss singularity variables which are properly suited for analyzing the kinematics of events with missing transverse energy at the LHC. We consider six of the simplest event topologies encountered in studies of leptonic W -bosons and top quarks, as well as in SUSY-like searches for new physics with dark matter particles. In each case, we illustrate the general prescription for finding the relevant singularity variable, which in turn helps delineate the visible parameter subspace on which the singularities are located. Our results can be used in two different ways -first, as a guide for targeting the signal-rich regions of parameter space during the stage of discovery, and second, as a sensitive focus point method for measuring the particle mass spectrum after the initial discovery. 4 The only exception being an invisibly decaying Z-boson, or a leptonically decaying W -boson where the lepton is lost [26].5 For reviews of the large variety of mass measurement methods proposed for SUSY-like events with MET, see [27,28] and references therein.

show abstract

Edge Detecting New Physics the Voronoi Way

Cited by 5 publications

References 0 publications

A quantum algorithm for model independent searches for new physics

A quantum algorithm for model independent searches for new physics

Finding Wombling Boundaries in LHC Data with Voronoi and Delaunay Tessellations

Singularity variables for missing energy event kinematics

Contact Info

Product

Resources

About