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View article: Uncertainty Quantification of Machine-Learning-Based Atmospheric Retrievals of Exoplanets fromTransmission Spectra
Uncertainty Quantification of Machine-Learning-Based Atmospheric Retrievals of Exoplanets fromTransmission Spectra Open
Transmission spectroscopy is a common tool for the characterization of transiting exoplanets and their atmospheres. Machine learning (ML) techniques are being increasingly applied to the inverse problem of determining the exoplanet paramet…
View article: Machine Learning in Planetary Science: Lessons from a Cross-Disciplinary Collaboration
Machine Learning in Planetary Science: Lessons from a Cross-Disciplinary Collaboration Open
We share insights into our cross-disciplinary research program at the intersection of planetary science, theoretical physics, and Machine Learning. Specifically, we highlight our work on the characterization of exoplanet atmospheres using …
View article: Detecting unusual chemical signatures using autoencoder-based anomaly detection
Detecting unusual chemical signatures using autoencoder-based anomaly detection Open
The study of exoplanetary atmospheres is influenced by models and assumptions based on Earth-like chemistry, because it is what we know best. This perspective can introduce biases in how we interpret spectroscopic data. With the upcoming A…
View article: Quantum Diffusion Model for Quark and Gluon Jet Generation
Quantum Diffusion Model for Quark and Gluon Jet Generation Open
Diffusion models have demonstrated remarkable success in image generation, but they are computationally intensive and time-consuming to train. In this paper, we introduce a novel diffusion model that benefits from quantum computing techniq…
View article: Lie-Equivariant Quantum Graph Neural Networks
Lie-Equivariant Quantum Graph Neural Networks Open
Discovering new phenomena at the Large Hadron Collider (LHC) involves the identification of rare signals over conventional backgrounds. Thus binary classification tasks are ubiquitous in analyses of the vast amounts of LHC data. We develop…
View article: Quantum Attention for Vision Transformers in High Energy Physics
Quantum Attention for Vision Transformers in High Energy Physics Open
We present a novel hybrid quantum-classical vision transformer architecture incorporating quantum orthogonal neural networks (QONNs) to enhance performance and computational efficiency in high-energy physics applications. Building on advan…
View article: Quantum Vision Transformers for Quark–Gluon Classification
Quantum Vision Transformers for Quark–Gluon Classification Open
We introduce a hybrid quantum-classical vision transformer architecture, notable for its integration of variational quantum circuits within both the attention mechanism and the multi-layer perceptrons. The research addresses the critical c…
View article: Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics
Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics Open
Models based on vision transformer architectures are considered state-of-the-art when it comes to image classification tasks. However, they require extensive computational resources both for training and deployment. The problem is exacerba…
View article: ℤ2 × ℤ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks
ℤ2 × ℤ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks Open
This paper presents a comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNNs) and Quantum Neural Networks (QNNs), juxtaposed against their classical counterparts: Equivariant Neural Networks (ENNs) and Deep N…
View article: Variance reduction via simultaneous importance sampling and control variates techniques using vegas
Variance reduction via simultaneous importance sampling and control variates techniques using vegas Open
Monte Carlo (MC) integration is an important calculational technique in the physical sciences. Practical considerations require that the calculations are performed as accurately as possible for a given set of computational resources. To im…
View article: Codebase release r1.4 for CoVVVR
Codebase release r1.4 for CoVVVR Open
Monte Carlo (MC) integration is an important calculational technique in the physical sciences. Practical considerations require that the calculations are performed as accurately as possible for a given set of computational resources. To im…
View article: A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks
A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks Open
Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with gr…
View article: $M_{TN}$ is all you need: production of multiple semi-invisible resonances at hadron colliders
$M_{TN}$ is all you need: production of multiple semi-invisible resonances at hadron colliders Open
The stransverse mass variable $M_{T2}$ was originally proposed for the study of hadron collider events in which $N=2$ parent particles are produced and then decay semi-invisibly. Here we consider the generalization to the case of $N\ge 3$ …
View article: Exploring the Truth and Beauty of Theory Landscapes with Machine Learning
Exploring the Truth and Beauty of Theory Landscapes with Machine Learning Open
Theoretical physicists describe nature by i) building a theory model and ii) determining the model parameters. The latter step involves the dual aspect of both fitting to the existing experimental data and satisfying abstract criteria like…
View article: Report on 2309.12369v1
Report on 2309.12369v1 Open
Monte Carlo (MC) integration is an important calculational technique in the physical sciences.Practical considerations require that the calculations are performed as accurately as possible for a given set of computational resources.To impr…
View article: $\mathbb{Z}_2\times \mathbb{Z}_2$ Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks
$\mathbb{Z}_2\times \mathbb{Z}_2$ Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks Open
This paper presents a comprehensive comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNN) and Quantum Neural Networks (QNN), juxtaposed against their classical counterparts: Equivariant Neural Networks (ENN)…
View article: A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networks
A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networks Open
Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with gr…
View article: Kinematic variables and feature engineering for particle phenomenology
Kinematic variables and feature engineering for particle phenomenology Open
Kinematic variables are important tools for analyzing collider experiments. This article reviews a variety of such tools, which were designed primarily for the experiments at the Large Hadron Collider, but which have potential uses in othe…
View article: Searching for Novel Chemistry in Exoplanetary Atmospheres Using Machine Learning for Anomaly Detection
Searching for Novel Chemistry in Exoplanetary Atmospheres Using Machine Learning for Anomaly Detection Open
The next generation of telescopes will yield a substantial increase in the availability of high-quality spectroscopic data for thousands of exoplanets. The sheer volume of data and number of planets to be analyzed greatly motivate the deve…
View article: Seeking Truth and Beauty in Flavor Physics with Machine Learning
Seeking Truth and Beauty in Flavor Physics with Machine Learning Open
The discovery process of building new theoretical physics models involves the dual aspect of both fitting to the existing experimental data and satisfying abstract theorists' criteria like beauty, naturalness, etc. We design loss functions…
View article: Accelerated discovery of machine-learned symmetries: Deriving the exceptional Lie groups G2, F4 and E6
Accelerated discovery of machine-learned symmetries: Deriving the exceptional Lie groups G2, F4 and E6 Open
Recent work has applied supervised deep learning to derive continuous symmetry transformations that preserve the data labels and to obtain the corresponding algebras of symmetry generators. This letter introduces two improved algorithms th…
View article: Report on 2309.12369v1
Report on 2309.12369v1 Open
Monte Carlo (MC) integration is an important calculational technique in the physical sciences.Practical considerations require that the calculations are performed as accurately as possible for a given set of computational resources.To impr…
View article: Reproducing Bayesian Posterior Distributions for Exoplanet Atmospheric Parameter Retrievals with a Machine Learning Surrogate Model
Reproducing Bayesian Posterior Distributions for Exoplanet Atmospheric Parameter Retrievals with a Machine Learning Surrogate Model Open
We describe a machine-learning-based surrogate model for reproducing the Bayesian posterior distributions for exoplanet atmospheric parameters derived from transmission spectra of transiting planets with typical retrieval software such as …
View article: Variance Reduction via Simultaneous Importance Sampling and Control Variates Techniques Using Vegas
Variance Reduction via Simultaneous Importance Sampling and Control Variates Techniques Using Vegas Open
Monte Carlo (MC) integration is an important calculational technique in the physical sciences. Practical considerations require that the calculations are performed as accurately as possible for a given set of computational resources. To im…
View article: Identifying the Group-Theoretic Structure of Machine-Learned Symmetries
Identifying the Group-Theoretic Structure of Machine-Learned Symmetries Open
Deep learning was recently successfully used in deriving symmetry transformations that preserve important physics quantities. Being completely agnostic, these techniques postpone the identification of the discovered symmetries to a later s…
View article: Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection
Searching for Novel Chemistry in Exoplanetary Atmospheres using Machine Learning for Anomaly Detection Open
The next generation of telescopes will yield a substantial increase in the availability of high-resolution spectroscopic data for thousands of exoplanets. The sheer volume of data and number of planets to be analyzed greatly motivate the d…
View article: Accelerated Discovery of Machine-Learned Symmetries: Deriving the Exceptional Lie Groups G2, F4 and E6
Accelerated Discovery of Machine-Learned Symmetries: Deriving the Exceptional Lie Groups G2, F4 and E6 Open
Recent work has applied supervised deep learning to derive continuous symmetry transformations that preserve the data labels and to obtain the corresponding algebras of symmetry generators. This letter introduces two improved algorithms th…