New Limits on Spontaneous Wave Function Collapse Models with the XENONnT
Data
- URL: http://arxiv.org/abs/2209.15082v2
- Date: Tue, 3 Jan 2023 19:44:11 GMT
- Title: New Limits on Spontaneous Wave Function Collapse Models with the XENONnT
Data
- Authors: Inwook Kim
- Abstract summary: We have analyzed recently published XENONnT data for the spontaneous X-ray emission signature predicted by the objective wave function collapse model of quantum mechanics.
With extremely low background and large exposure, XENONnT data can be used to completely exclude the theoretically predicted collapse parameters of continuous spontaneous localization(CSL) model suggested by Ghirardi, Rhimini and Weber.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We have analyzed recently published XENONnT data for the spontaneous X-ray
emission signature predicted by the objective wave function collapse model of
quantum mechanics. With extremely low background and large exposure, XENONnT
data can be used to completely exclude the theoretically predicted collapse
parameters of continuous spontaneous localization~(CSL) model suggested by
Ghirardi, Rhimini and Weber. Our result strongly suggests that the simplest
version of the CSL model with the white-noise assumption is unlikely to provide
answers to the long-standing measurement problem of quantum mechanics and
motivates pursuits of more complex versions of the theory. If the result is
interpreted with the Di\'{o}si-Penrose gravitational wave function collapse
model, our limit improves the previous limit by a factor of 5.7. Detailed
analysis using more precise background modelling can further improve the
limits.
Related papers
- Causal Deciphering and Inpainting in Spatio-Temporal Dynamics via Diffusion Model [45.45700202300292]
CaPaint aims to identify causal regions in data and endow model with causal reasoning ability in a two-stage process.
By using a fine-tuned unconditional Diffusion Probabilistic Model (DDPM) as the generative prior, we in-fill the masks defined as environmental parts.
Experiments conducted on five real-world ST benchmarks demonstrate that integrating the CaPaint concept allows models to achieve improvements ranging from 4.3% to 77.3%.
arXiv Detail & Related papers (2024-09-29T08:18:50Z) - Non-asymptotic bounds for forward processes in denoising diffusions: Ornstein-Uhlenbeck is hard to beat [49.1574468325115]
This paper presents explicit non-asymptotic bounds on the forward diffusion error in total variation (TV)
We parametrise multi-modal data distributions in terms of the distance $R$ to their furthest modes and consider forward diffusions with additive and multiplicative noise.
arXiv Detail & Related papers (2024-08-25T10:28:31Z) - Is Temperature Sample Efficient for Softmax Gaussian Mixture of Experts? [27.924615931679757]
We explore the impacts of a dense-to-sparse gating mixture of experts (MoE) on the maximum likelihood estimation under the MoE.
We propose using a novel activation dense-to-sparse gate, which routes the output of a linear layer to an activation function before delivering them to the softmax function.
arXiv Detail & Related papers (2024-01-25T01:09:09Z) - Extrapolation of polaron properties to low phonon frequencies by
Bayesian machine learning [0.0]
Feasibility of accurate quantum calculations is often restricted by the dimensionality of the truncated Hilbert space required for numerical computations.
The present work demonstrates Bayesian machine learning (ML) models that use quantum properties in an effectively lower-dimensional Hilbert space.
arXiv Detail & Related papers (2023-12-15T18:04:41Z) - Structured Low-Rank Tensors for Generalized Linear Models [15.717917936953718]
This work investigates a new low-rank tensor model, called Low Separation Rank (LSR), in Generalized Linear Model (GLM) problems.
The LSR model generalizes the well-known Tucker and CANDECOMP/PARAFAC (CP) models.
Experiments on synthetic datasets demonstrate the efficacy of the proposed LSR tensor model for three regression types.
arXiv Detail & Related papers (2023-08-05T17:20:41Z) - 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) - A Neural PDE Solver with Temporal Stencil Modeling [44.97241931708181]
Recent Machine Learning (ML) models have shown new promises in capturing important dynamics in high-resolution signals.
This study shows that significant information is often lost in the low-resolution down-sampled features.
We propose a new approach, which combines the strengths of advanced time-series sequence modeling and state-of-the-art neural PDE solvers.
arXiv Detail & Related papers (2023-02-16T06:13:01Z) - New insights on the quantum-classical division in light of Collapse
Models [63.942632088208505]
We argue that the division between quantum and classical behaviors is analogous to the division of thermodynamic phases.
A specific relationship between the collapse parameter $(lambda)$ and the collapse length scale ($r_C$) plays the role of the coexistence curve in usual thermodynamic phase diagrams.
arXiv Detail & Related papers (2022-10-19T14:51:21Z) - Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty
Quantification [44.598503284186336]
Conditional-Flow NeRF (CF-NeRF) is a novel probabilistic framework to incorporate uncertainty quantification into NeRF-based approaches.
CF-NeRF learns a distribution over all possible radiance fields modelling which is used to quantify the uncertainty associated with the modelled scene.
arXiv Detail & Related papers (2022-03-18T23:26:20Z) - Gravity-related collapse of the wave function and spontaneous heating:
revisiting the experimental bounds [0.0]
We argue that the the parameter-free version of the DP model is close to be ruled out by standard heat leak measurements at ultralow temperature.
This result would strengthen a recent claim of exclusion inferred by spontaneous x-ray emission experiments.
arXiv Detail & Related papers (2021-09-30T10:21:42Z) - A Sparse Model of Quantum Holography [0.0]
We study a sparse version of the Sachdev-Ye-Kitaev (SYK) model defined on random hypergraphs constructed either by a random pruning procedure or by randomly sampling regular hypergraphs.
We argue that this sparse SYK model recovers the interesting global physics of ordinary SYK even when $k$ is of order unity.
Our argument proceeds by constructing a path integral for the sparse model which reproduces the conventional SYK path integral plus gapped fluctuations.
arXiv Detail & Related papers (2020-08-05T18:21:42Z)
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.