Simulation-based inference methods for particle physics
- URL: http://arxiv.org/abs/2010.06439v2
- Date: Mon, 2 Nov 2020 14:31:40 GMT
- Title: Simulation-based inference methods for particle physics
- Authors: Johann Brehmer and Kyle Cranmer
- Abstract summary: We explain why the likelihood function of high-dimensional LHC data cannot be explicitly evaluated, why this matters for data analysis, and reframe what the field has traditionally done to circumvent this problem.
We then review new simulation-based inference methods that let us directly analyze high-dimensional data by combining machine learning techniques and information from the simulator.
- Score: 12.451050883955071
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Our predictions for particle physics processes are realized in a chain of
complex simulators. They allow us to generate high-fidelity simulated data, but
they are not well-suited for inference on the theory parameters with observed
data. We explain why the likelihood function of high-dimensional LHC data
cannot be explicitly evaluated, why this matters for data analysis, and reframe
what the field has traditionally done to circumvent this problem. We then
review new simulation-based inference methods that let us directly analyze
high-dimensional data by combining machine learning techniques and information
from the simulator. Initial studies indicate that these techniques have the
potential to substantially improve the precision of LHC measurements. Finally,
we discuss probabilistic programming, an emerging paradigm that lets us extend
inference to the latent process of the simulator.
Related papers
- MaD-Scientist: AI-based Scientist solving Convection-Diffusion-Reaction Equations Using Massive PINN-Based Prior Data [22.262191225577244]
We explore whether a similar approach can be applied to scientific foundation models (SFMs)
We collect low-cost physics-informed neural network (PINN)-based approximated prior data in the form of solutions to partial differential equations (PDEs) constructed through an arbitrary linear combination of mathematical dictionaries.
We provide experimental evidence on the one-dimensional convection-diffusion-reaction equation, which demonstrate that pre-training remains robust even with approximated prior data.
arXiv Detail & Related papers (2024-10-09T00:52:00Z) - Embed and Emulate: Contrastive representations for simulation-based inference [11.543221890134399]
This paper introduces Embed and Emulate (E&E), a new simulation-based inference ( SBI) method based on contrastive learning.
E&E learns a low-dimensional latent embedding of the data and a corresponding fast emulator in the latent space.
We demonstrate superior performance over existing methods in a realistic, non-identifiable parameter estimation task.
arXiv Detail & Related papers (2024-09-27T02:37:01Z) - Diffusion posterior sampling for simulation-based inference in tall data settings [53.17563688225137]
Simulation-based inference ( SBI) is capable of approximating the posterior distribution that relates input parameters to a given observation.
In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model.
We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost.
arXiv Detail & Related papers (2024-04-11T09:23:36Z) - 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) - Data Compression and Inference in Cosmology with Self-Supervised Machine
Learning [0.86325068644655]
We introduce a method that leverages the paradigm of self-supervised machine learning in a novel manner to construct representative summaries of massive datasets.
Deploying the method on hydrodynamical cosmological simulations, we show that it can deliver highly informative summaries.
Results indicate that self-supervised machine learning techniques offer a promising new approach for compression of cosmological data as well its analysis.
arXiv Detail & Related papers (2023-08-18T18:00:22Z) - Simulation-based inference using surjective sequential neural likelihood
estimation [50.24983453990065]
Surjective Sequential Neural Likelihood estimation is a novel method for simulation-based inference.
By embedding the data in a low-dimensional space, SSNL solves several issues previous likelihood-based methods had when applied to high-dimensional data sets.
arXiv Detail & Related papers (2023-08-02T10:02:38Z) - Addressing computational challenges in physical system simulations with
machine learning [0.0]
We present a machine learning-based data generator framework tailored to aid researchers who utilize simulations to examine various physical systems or processes.
Our approach involves a two-step process: first, we train a supervised predictive model using a limited simulated dataset to predict simulation outcomes.
Subsequently, a reinforcement learning agent is trained to generate accurate, simulation-like data by leveraging the supervised model.
arXiv Detail & Related papers (2023-05-16T17:31:50Z) - 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) - Mixed Effects Neural ODE: A Variational Approximation for Analyzing the
Dynamics of Panel Data [50.23363975709122]
We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data.
We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem.
We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
arXiv Detail & Related papers (2022-02-18T22:41:51Z) - Likelihood-Free Inference in State-Space Models with Unknown Dynamics [71.94716503075645]
We introduce a method for inferring and predicting latent states in state-space models where observations can only be simulated, and transition dynamics are unknown.
We propose a way of doing likelihood-free inference (LFI) of states and state prediction with a limited number of simulations.
arXiv Detail & Related papers (2021-11-02T12:33:42Z)
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.