128 research outputs found
FAIR Principles for data and AI models in high energy physics research and education
In recent years, digital object management practices to support findability,
accessibility, interoperability, and reusability (FAIR) have begun to be
adopted across a number of data-intensive scientific disciplines. These digital
objects include datasets, AI models, software, notebooks, workflows,
documentation, etc. With the collective dataset at the Large Hadron Collider
scheduled to reach the zettabyte scale by the end of 2032, the experimental
particle physics community is looking at unprecedented data management
challenges. It is expected that these grand challenges may be addressed by
creating end-to-end AI frameworks that combine FAIR and AI-ready datasets,
advances in AI, modern computing environments, and scientific data
infrastructure. In this work, the FAIR4HEP collaboration explores the
interpretation of FAIR principles in the context of data and AI models for
experimental high energy physics research. We investigate metrics to quantify
the FAIRness of experimental datasets and AI models, and provide open source
notebooks to guide new users on the use of FAIR principles in practice.Comment: Contribution to the Proceedings of 41st International Conference on
High Energy Physics - ICHEP2022, Presented on behalf of the FAIR4HEP
collaboratio
Oscillating Shells and Oscillating Balls in AdS
It has recently been reported that certain thin timelike shells undergo
oscillatory motion in AdS. In this paper, we compute two-point function of a
probe field in the geodesic approximation in such an oscillating shell
background. We confirm that the two-point function exhibits an oscillatory
behaviour following the motion of the shell. We show that similar oscillatory
dynamics is possible when the perfect fluid on the shell has a polytropic
equation of state. Moreover, we show that certain ball like configurations in
AdS also exhibit oscillatory motion and comment on how such a solution can be
smoothly matched to an appropriate exterior solution. We also demonstrate that
the weak energy condition is satisfied for these oscillatory configurations.Comment: 23 pages, 5 figures; v2: refs added; v3: JHEP versio
A Detailed Study of Interpretability of Deep Neural Network based Top Taggers
Recent developments in the methods of explainable AI (xAI) methods allow us
to explore the inner workings of deep neural networks (DNNs), revealing crucial
information about input-output relationships and realizing how data connects
with machine learning models. In this paper we explore interpretability of DNN
models designed for identifying jets coming from top quark decay in the high
energy proton-proton collisions at the Large Hadron Collider (LHC). We review a
subset of existing such top tagger models and explore different quantitative
methods to identify which features play the most important roles in identifying
the top jets. We also investigate how and why feature importance varies across
different xAI metrics, how feature correlations impact their explainability,
and how latent space representations encode information as well as correlate
with physically meaningful quantities. Our studies uncover some major pitfalls
of existing xAI methods and illustrate how they can be overcome to obtain
consistent and meaningful interpretation of these models. We additionally
illustrate the activity of hidden layers as Neural Activation Pattern (NAP)
diagrams and demonstrate how they can be used to understand how DNNs relay
information across the layers and how this understanding can help us to make
such models significantly simpler by allowing effective model reoptimization
and hyperparameter tuning. While the primary focus of this work remains a
detailed study of interpretability of DNN-based top tagger models, it also
features state-of-the art performance obtained from modified implementation of
existing networks.Comment: Repository: https://github.com/FAIR4HEP/xAI4toptagge
Comments on Information Erasure in Black Hole
We analyze the Kim, Lee & Lee model of information erasure by black holes and
find contradictions with standard physical laws. We demonstrate that the
erasure model leads to arbitrarily fast information erasure; the proposed
physical interpretation of information freezing at the event horizon as
observed by an asymptotic observer is problematic; and information erasure,
whatever the process may be, near the black hole horizon leads to
contradictions with quantum mechanics if Landauer's principle is assumed. The
later part of the work demonstrates the significance of the "erasure entropy."
We show that the erasure entropy is the mutual information between two
subsystems.Comment: 13 pages, clarified some issues in detai
Deep Learning for the Matrix Element Method
Extracting scientific results from high-energy collider data involves the
comparison of data collected from the experiments with synthetic data produced
from computationally-intensive simulations. Comparisons of experimental data
and predictions from simulations increasingly utilize machine learning (ML)
methods to try to overcome these computational challenges and enhance the data
analysis. There is increasing awareness about challenges surrounding
interpretability of ML models applied to data to explain these models and
validate scientific conclusions based upon them. The matrix element (ME) method
is a powerful technique for analysis of particle collider data that utilizes an
\textit{ab initio} calculation of the approximate probability density function
for a collision event to be due to a physics process of interest. The ME method
has several unique and desirable features, including (1) not requiring training
data since it is an \textit{ab initio} calculation of event probabilities, (2)
incorporating all available kinematic information of a hypothesized process,
including correlations, without the need for feature engineering and (3) a
clear physical interpretation in terms of transition probabilities within the
framework of quantum field theory. These proceedings briefly describe an
application of deep learning that dramatically speeds-up ME method calculations
and novel cyberinfrastructure developed to execute ME-based analyses on
heterogeneous computing platforms.Comment: 6 pages, 3 figures. Contribution to the Proceedings of the ICHEP 2022
Conferenc
Robust Learning of Physics Informed Neural Networks
Physics-informed Neural Networks (PINNs) have been shown to be effective in
solving partial differential equations by capturing the physics induced
constraints as a part of the training loss function. This paper shows that a
PINN can be sensitive to errors in training data and overfit itself in
dynamically propagating these errors over the domain of the solution of the
PDE. It also shows how physical regularizations based on continuity criteria
and conservation laws fail to address this issue and rather introduce problems
of their own causing the deep network to converge to a physics-obeying local
minimum instead of the global minimum. We introduce Gaussian Process (GP) based
smoothing that recovers the performance of a PINN and promises a robust
architecture against noise/errors in measurements. Additionally, we illustrate
an inexpensive method of quantifying the evolution of uncertainty based on the
variance estimation of GPs on boundary data. Robust PINN performance is also
shown to be achievable by choice of sparse sets of inducing points based on
sparsely induced GPs. We demonstrate the performance of our proposed methods
and compare the results from existing benchmark models in literature for
time-dependent Schr\"odinger and Burgers' equations
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