14 research outputs found
Optimal Mass Variables for Semivisible Jets
Strongly coupled hidden sector theories predict collider production of
invisible, composite dark matter candidates mixed with regular hadrons in the
form of semivisible jets. Classical mass reconstruction techniques may not be
optimal for these unusual topologies, in which the missing transverse momentum
comes from massive particles and has a nontrivial relationship to the visible
jet momentum. We apply the artificial event variable network, a semisupervised,
interpretable machine learning technique that uses an information bottleneck,
to derive superior mass reconstruction functions for several cases of resonant
semivisible jet production. We demonstrate that the technique can extrapolate
to unknown signal model parameter values. We further demonstrate the viability
of conducting an actual search for new physics using this method, by applying
the learned functions to standard model background events from quantum
chromodynamics.Comment: To be submitted to SciPost Phy
Finding Wombling Boundaries in LHC Data with Voronoi and Delaunay Tessellations
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.Comment: 54 pages. New figure 13 and appendix A added. Conclusions unchanged.
Matches published versio
New Machine Learning Techniques for Simulation-Based Inference: InferoStatic Nets, Kernel Score Estimation, and Kernel Likelihood Ratio Estimation
We propose an intuitive, machine-learning approach to multiparameter
inference, dubbed the InferoStatic Networks (ISN) method, to model the score
and likelihood ratio estimators in cases when the probability density can be
sampled but not computed directly. The ISN uses a backend neural network that
models a scalar function called the inferostatic potential . In
addition, we introduce new strategies, respectively called Kernel Score
Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the
score and the likelihood ratio functions from simulated data. We illustrate the
new techniques with some toy examples and compare to existing approaches in the
literature. We mention en passant some new loss functions that optimally
incorporate latent information from simulations into the training procedure.Comment: 35 pages, 10 figures. Submission to SciPos
Quantum circuit fidelity estimation using machine learning
The computational power of real-world quantum computers is limited by errors.
When using quantum computers to perform algorithms which cannot be efficiently
simulated classically, it is important to quantify the accuracy with which the
computation has been performed. In this work we introduce a
machine-learning-based technique to estimate the fidelity between the state
produced by a noisy quantum circuit and the target state corresponding to ideal
noise-free computation. Our machine learning model is trained in a supervised
manner, using smaller or simpler circuits for which the fidelity can be
estimated using other techniques like direct fidelity estimation and quantum
state tomography. We demonstrate that, for simulated random quantum circuits
with a realistic noise model, the trained model can predict the fidelities of
more complicated circuits for which such methods are infeasible. In particular,
we show the trained model may make predictions for circuits with higher degrees
of entanglement than were available in the training set, and that the model may
make predictions for non-Clifford circuits even when the training set included
only Clifford-reducible circuits. This empirical demonstration suggests
classical machine learning may be useful for making predictions about
beyond-classical quantum circuits for some non-trivial problems.Comment: 27 pages, 6 figure