paint-brush
Derivation of Marginal Likelihood with Stochastic Field Amplitude by@phenomenology

Derivation of Marginal Likelihood with Stochastic Field Amplitude

by Phenomenology TechnologyOctober 27th, 2024
Read on Terminal Reader
Read this story w/o Javascript
tldt arrow

Too Long; Didn't Read

This article details the derivation of marginal likelihood for stochastic field amplitude, using Bessel and Gamma functions to analytically integrate random variables. The resulting normalized likelihood is split into three components: two for sum/difference peaks and one for the Compton peak, with the forms of the first two being equivalent.
featured image - Derivation of Marginal Likelihood with Stochastic Field Amplitude
Phenomenology Technology HackerNoon profile picture

Authors:

(1) Dorian W. P. Amaral, Department of Physics and Astronomy, Rice University and These authors contributed approximately equally to this work;

(2) Mudit Jain, Department of Physics and Astronomy, Rice University, Theoretical Particle Physics and Cosmology, King’s College London and These authors contributed approximately equally to this work;

(3) Mustafa A. Amin, Department of Physics and Astronomy, Rice University;

(4) Christopher Tunnell, Department of Physics and Astronomy, Rice University.

Abstract and 1 Introduction

2 Calculating the Stochastic Wave Vector Dark Matter Signal

2.1 The Dark Photon Field

2.2 The Detector Signal

3 Statistical Analysis and 3.1 Signal Likelihood

3.2 Projected Exclusions

4 Application to Accelerometer Studies

4.1 Recasting Generalised Limits onto B − L Dark Matter

5 Future Directions

6 Conclusions, Acknowledgments, and References


A Equipartition between Longitudinal and Transverse Modes

B Derivation of Marginal Likelihood with Stochastic Field Amplitude

C Covariance Matrix

D The Case of the Gradient of a Scalar

B Derivation of Marginal Likelihood with Stochastic Field Amplitude

The full signal in time space is given by



Using the series representation of the Bessel function, together with Gamma function identities, the 5 random variables can be integrated out analytically. We arrive at the following marginalized (and normalized) likelihood:



where



This likelihood can be split into three individual likelihoods for the sum/difference peaks and the Compton peak, as given in Eq. (3.2). The form of the likelihoods for the sum and difference peaks is equivalent.


This paper is under CC BY 4.0 DEED license.


바카라사이트 바카라사이트 온라인바카라