Inferring Interpretable Models of Fragmentation Functions using Symbolic Regression

• URL: http://arxiv.org/abs/2501.07123v1
• Date: Mon, 13 Jan 2025 08:25:14 GMT
• Title: Inferring Interpretable Models of Fragmentation Functions using Symbolic Regression
• Authors: Nour Makke, Sanjay Chawla,
• Abstract summary: We present the first study that infers, directly from experimental data, a functional form of fragmentation functions. This study represents an approach to follow in such QCD-related phenomenology studies and more generally in sciences.
• Score: 10.091537548478655
• License:
• Abstract: Machine learning is rapidly making its path into natural sciences, including high-energy physics. We present the first study that infers, directly from experimental data, a functional form of fragmentation functions. The latter represent a key ingredient to describe physical observables measured in high-energy physics processes that involve hadron production, and predict their values at different energy. Fragmentation functions can not be calculated in theory and have to be determined instead from data. Traditional approaches rely on global fits of experimental data using a pre-assumed functional form inspired from phenomenological models to learn its parameters. This novel approach uses a ML technique, namely symbolic regression, to learn an analytical model from measured charged hadron multiplicities. The function learned by symbolic regression resembles the Lund string function and describes the data well, thus representing a potential candidate for use in global FFs fits. This study represents an approach to follow in such QCD-related phenomenology studies and more generally in sciences.

Related papers

• An Explainable Pipeline for Machine Learning with Functional Data [0.1874930567916036]
  We consider two applications from high-consequence spaces with objectives of making predictions using functional data inputs. One application aims to classify material types to identify explosive materials given hyperspectral computed tomography scans of the materials. The other application considers the forensics science task of connecting an inkjet printed document to the source printer using color signatures extracted by Raman spectroscopy.
  arXiv  Detail & Related papers  (2025-01-10T23:33:15Z)
• SymbolFit: Automatic Parametric Modeling with Symbolic Regression [1.2662552408022727]
  We introduce SymbolFit, a framework that automates parametric modeling by using symbolic regression to perform a machine-search for functions that fit the data. Our approach is demonstrated in data analysis applications in high-energy physics experiments at the CERN Large Hadron Collider.
  arXiv  Detail & Related papers  (2024-11-15T00:09:37Z)
• Sparse identification of quasipotentials via a combined data-driven method [4.599618895656792]
  We leverage on machine learning via the combination of two data-driven techniques, namely a neural network and a sparse regression algorithm, to obtain symbolic expressions of quasipotential functions. We show that our approach discovers a parsimonious quasipotential equation for an archetypal model with a known exact quasipotential and for the dynamics of a nanomechanical resonator.
  arXiv  Detail & Related papers  (2024-07-06T11:27:52Z)
• Discovering Interpretable Physical Models using Symbolic Regression and Discrete Exterior Calculus [55.2480439325792]
  We propose a framework that combines Symbolic Regression (SR) and Discrete Exterior Calculus (DEC) for the automated discovery of physical models. DEC provides building blocks for the discrete analogue of field theories, which are beyond the state-of-the-art applications of SR to physical problems. We prove the effectiveness of our methodology by re-discovering three models of Continuum Physics from synthetic experimental data.
  arXiv  Detail & Related papers  (2023-10-10T13:23:05Z)
• On the Joint Interaction of Models, Data, and Features [82.60073661644435]
  We introduce a new tool, the interaction tensor, for empirically analyzing the interaction between data and model through features. Based on these observations, we propose a conceptual framework for feature learning. Under this framework, the expected accuracy for a single hypothesis and agreement for a pair of hypotheses can both be derived in closed-form.
  arXiv  Detail & Related papers  (2023-06-07T21:35:26Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• Spherical Poisson Point Process Intensity Function Modeling and Estimation with Measure Transport [0.20305676256390934]
  We present a new approach for modeling non-homogeneous Poisson process intensity functions on the sphere. The central idea of this framework is to build, and estimate, a flexible Bijective map that transforms the underlying intensity function of interest on the sphere into a simpler reference, intensity function, also on the sphere.
  arXiv  Detail & Related papers  (2022-01-24T06:46:22Z)
• Analytical Modelling of Exoplanet Transit Specroscopy with Dimensional Analysis and Symbolic Regression [68.8204255655161]
  The deep learning revolution has opened the door for deriving such analytical results directly with a computer algorithm fitting to the data. We successfully demonstrate the use of symbolic regression on synthetic data for the transit radii of generic hot Jupiter exoplanets. As a preprocessing step, we use dimensional analysis to identify the relevant dimensionless combinations of variables.
  arXiv  Detail & Related papers  (2021-12-22T00:52:56Z)
• Leveraging Global Parameters for Flow-based Neural Posterior Estimation [90.21090932619695]
  Inferring the parameters of a model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations. We present a method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters.
  arXiv  Detail & Related papers  (2021-02-12T12:23:13Z)
• Random Sampling High Dimensional Model Representation Gaussian Process Regression (RS-HDMR-GPR) for representing multidimensional functions with machine-learned lower-dimensional terms allowing insight with a general method [0.0]
  Python implementation for RS-HDMR-GPR (Random Sampling High Dimensional Model Representation Gaussian Process Regression) Code allows for imputation of missing values of the variables and for a significant pruning of the useful number of HDMR terms. The capabilities of this regression tool are demonstrated on test cases involving synthetic analytic functions, the potential energy surface of the water molecule, kinetic energy densities of materials, and financial market data.
  arXiv  Detail & Related papers  (2020-11-24T00:12:05Z)
• UNIPoint: Universally Approximating Point Processes Intensities [125.08205865536577]
  We provide a proof that a class of learnable functions can universally approximate any valid intensity function. We implement UNIPoint, a novel neural point process model, using recurrent neural networks to parameterise sums of basis function upon each event.
  arXiv  Detail & Related papers  (2020-07-28T09:31:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.