Does SUSY have friends? A new approach for LHC event analysis

Abstract

We present a novel technique for the analysis of proton-proton collision events from the ATLAS and CMS experiments at the Large Hadron Collider. For a given final state and choice of kinematic variables, we build a graph network in which the individual events appear as weighted nodes, with edges between events defined by their distance in kinematic space. We then show that it is possible to calculate local metrics of the network that serve as event-by-event variables for separating signal and background processes, and we evaluate these for a number of different networks that are derived from different distance metrics. Using a supersymmetric electroweakino and stop production as examples, we construct prototype analyses that take account of the fact that the number of simulated Monte Carlo events used in an LHC analysis may differ from the number of events expected in the LHC dataset, allowing an accurate background estimate for a particle search at the LHC to be derived. For the electroweakino example, we show that the use of network variables outperforms both cut-and-count analyses that use the original variables and a boosted decision tree trained on the original variables. The stop example, deliberately chosen to be difficult to exclude due its kinematic similarity with the top background, demonstrates that network variables are not automatically sensitive to BSM physics. Nevertheless, we identify local network metrics that show promise if their robustness under certain assumptions of node-weighted networks can be confirmed.

What's Anomalous in LHC Jets?

“…Based on these practical successes, ML-methods for anomaly detection at the LHC have generally received a lot of attention in the context of anomalous jets [10][11][12][13][14][15][16][17], anomalous events pointing to physics beyond the Standard Model [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35], or enhancing established search strategies [36][37][38][39][40][41][42]. They include a first ATLAS analysis [43], experimental validation of some of the methods [44,45], quantum machine learning [46], applications to heavy-ion collisions [47], the DarkMachines challenge [48], and the LHC Olympics 2020 community challenge [49,50].…”

Use of a generalized energy Mover’s distance in the search for rare phenomena at colliders

“…the values d(I, J ) represent an adjancy matrix that can be used for network analyses, such as clustering which we will explore below. This is similar to the approach in [8]. For simplicity we still call it a distance throughout the paper.…”

“…In the current paper, we expand on the EMD definition by using global event properties, contributing to a better exploitation of the experimental information used in the search for rare events, which typically have cross-sections several orders of magnitude below the backgrounds affecting their measurement. In such cases, good discrimination between signal and background is a critical aspect to keep the experimental uncertainties under control and new variables contributing to correct classification of events can contribute to this goal [8,9].…”

Better latent spaces for better autoencoders

“…Additional papers studying similar unsupervised LHC problems include Ref. [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40].…”