21 research outputs found
Secondary Vertex Finding in Jets with Neural Networks
Jet classification is an important ingredient in measurements and searches
for new physics at particle coliders, and secondary vertex reconstruction is a
key intermediate step in building powerful jet classifiers. We use a neural
network to perform vertex finding inside jets in order to improve the
classification performance, with a focus on separation of bottom vs. charm
flavor tagging. We implement a novel, universal set-to-graph model, which takes
into account information from all tracks in a jet to determine if pairs of
tracks originated from a common vertex. We explore different performance
metrics and find our method to outperform traditional approaches in accurate
secondary vertex reconstruction. We also find that improved vertex finding
leads to a significant improvement in jet classification performance
Neural Functional Transformers
The recent success of neural networks as implicit representation of data has
driven growing interest in neural functionals: models that can process other
neural networks as input by operating directly over their weight spaces.
Nevertheless, constructing expressive and efficient neural functional
architectures that can handle high-dimensional weight-space objects remains
challenging. This paper uses the attention mechanism to define a novel set of
permutation equivariant weight-space layers and composes them into deep
equivariant models called neural functional Transformers (NFTs). NFTs respect
weight-space permutation symmetries while incorporating the advantages of
attention, which have exhibited remarkable success across multiple domains. In
experiments processing the weights of feedforward MLPs and CNNs, we find that
NFTs match or exceed the performance of prior weight-space methods. We also
leverage NFTs to develop Inr2Array, a novel method for computing permutation
invariant latent representations from the weights of implicit neural
representations (INRs). Our proposed method improves INR classification
accuracy by up to over existing methods. We provide an implementation
of our layers at https://github.com/AllanYangZhou/nfn
Equivariant Networks for Crystal Structures
Supervised learning with deep models has tremendous potential for
applications in materials science. Recently, graph neural networks have been
used in this context, drawing direct inspiration from models for molecules.
However, materials are typically much more structured than molecules, which is
a feature that these models do not leverage. In this work, we introduce a class
of models that are equivariant with respect to crystalline symmetry groups. We
do this by defining a generalization of the message passing operations that can
be used with more general permutation groups, or that can alternatively be seen
as defining an expressive convolution operation on the crystal graph.
Empirically, these models achieve competitive results with state-of-the-art on
property prediction tasks.Comment: 10 pages, 4 figures + appendi