arXiv subjects

Oliver Friedrich

Publications and source records attributed to Oliver Friedrich.

At least 19 recordsLinked to original sources

arXiv2026

Causality from the spectrum: Emergence of causal order from process-matrix mereology

In Hamiltonian systems, the only basis-independent quantity is the spectrum. Given a spectrum, quantum mereology seeks preferred tensor-product decompositions into local subsystems, leading to the emergence of locality. Causal order, however, remains fixed by the Hamiltonian time evolution. In contrast, higher-order quantum theory permits more general processes that need not possess a definite global causal order. In the process-matrix framework, specifying a process requires a choice of subsystems corresponding to the input and output Hilbert spaces of each agent. A change of basis redefines both the agents and the corresponding decompositions into subsystems, while leaving the spectrum of the process matrix invariant. Here, we study how causality arises from this spectrum. First, we derive spectral constraints on processes compatible with definite causal order. Second, we show that, in the thermodynamic limit, generic quantum processes admit a preferred decomposition with a definite causal order. This suggests a mechanism for the emergence of classical causality only from algebraic ingredients.

quant-ph↗

arXiv2026

Wave packets from the spectrum

The freedom to change Fock basis seems to ensure a minimum amount of locality in lattice theories in the following sense: If $\lbrace (\hat a_i^\dagger\,,\,\hat a_i)\rbrace$ for $i=1,\dots,n$ is a lattice of creation and annihilation operators and if a given Hamiltonian $\hat H$ induces highly non-local dynamics on that lattice, then it will usually be possible to change to a new set of operators $\lbrace (\hat b_i^\dagger\,,\,\hat b_i)\rbrace$ in terms of which the dynamics appear less non-local. We demonstrate this by turning a highly non-local random matrix model into a local, 1D lattice theory where particles can propagate in localized wave packets. More generally, we show that any Hamiltonian can be made to look like such a theory, with the lattice dispersion relation and the non-integrability of the theory depending on the spectrum of $\hat H$. We argue that our results are a step towards quantum mereology for fields.

quant-ph↗

arXiv2026

Area Scaling of Dynamical Degrees of Freedom in Regularised Scalar Field Theory

How many canonical degrees of freedom does a quantum field theory actually use during its Hamiltonian evolution? For a UV/IR-regularised classical scalar field, we address this question directly at the level of phase-space dynamics by identifying the minimal symplectic dimension required to reproduce a single trajectory by an autonomous Hamiltonian system. Using symplectic model order reduction as a structure-preserving diagnostic, we show that for the free scalar field this minimal dimension is controlled not by the volume-extensive number of discretised field variables, but by the much smaller number of distinct normal-mode frequencies below the ultraviolet cutoff. In flat space, this leads to an area-type scaling with the size of the region, up to slowly varying corrections. On geodesic balls in maximally symmetric curved spaces, positive curvature induces mild super-area growth, while negative curvature suppresses the scaling, with the flat result recovered smoothly in the small-curvature limit. Numerical experiments further indicate that this behaviour persists in weakly interacting $\lambda\phi^4$ theory over quasi-integrable time scales. Beyond counting, the reduced dynamics exhibits a distinctive internal structure: it decomposes into independent oscillator blocks, while linear combinations of these blocks generate a larger family of apparent field modes whose Poisson brackets are governed by a projector rather than the identity. This reveals a purely classical and dynamical mechanism by which overlapping degrees of freedom arise, without modifying canonical structures by hand. Our results provide a controlled field-theoretic setting in which area-type scaling and overlap phenomena can be studied prior to quantisation, helping to identify which aspects of such structures--often discussed in holographic contexts--can already arise from classical Hamiltonian dynamics.

hep-th↗

arXiv2025

Emergence of Gravitational Potential and Time Dilation from Non-interacting Systems Coupled to a Global Quantum Clock

We study gravitational back-reaction within the Page-Wootters formulation of quantum mechanics by treating time as a quantum degree of freedom. Our model introduces a distinction between global coordinate time, represented as a relational quantum observable, and proper time, measured by internal quantum degrees of freedom of physical systems. By coupling mass-energy with coordinate time through a Wheeler-DeWitt-like constraint, we demonstrate the natural emergence of gravitational time dilation. In the presence of a massive object this agrees with time dilation in a Schwarzchild metric at leading order if the interaction strength is taken to be representative of the gravitational coupling $G$. Additionally, when two particles independently couple to the time coordinate, a Newtonian gravitational interaction arises in the low-energy limit, showing how gravitational potential can emerge from non-interacting quantum systems. Our approach also reveals renormalization features, potentially softening high-energy divergences and suggesting that particles in superposition might introduce quantum corrections to gravitational time dilation.

gr-qc↗

arXiv2025

Bye binormal: analysing the joint PDF of galaxy density and weak lensing convergence

At any given scale, 3$\times$2-point statistics extract only three numbers from the joint distribution of the cosmic matter density and galaxy density fluctuations: their variances and their covariance. It is well known that the full shape of the PDF of those fluctuations contains significantly more information than can be accessed through these three numbers. But the study of the PDF of cosmic density fluctuations in real observational data is still in its infancy. Here we present \verb|CosMomentum|, a public software toolkit for calculating theoretical predictions for the full shape of the joint distribution of a line-of-sight projected tracer density and the gravitational lensing convergence. We demonstrate that an analysis of this full shape of the PDF can indeed disentangle complicated tracer bias and stochasticity relations from signatures of cosmic structure growth. Our paper also provides back-drop for an upcoming follow-up study, which prepares PDF analyses for application to observational data by incorporating the impact of realistic weak lensing systematics.

astro-ph.CO↗

arXiv2025

Detecting immune cells with label-free two-photon autofluorescence and deep learning

Label-free imaging has gained broad interest because of its potential to omit elaborate staining procedures which is especially relevant for in vivo use. Label-free multiphoton microscopy (MPM), for instance, exploits two-photon excitation of natural autofluorescence (AF) from native, metabolic proteins, making it ideal for in vivo endomicroscopy. Deep learning (DL) models have been widely used in other optical imaging technologies to predict specific target annotations and thereby digitally augment the specificity of these label-free images. However, this computational specificity has only rarely been implemented for MPM. In this work, we used a data set of label-free MPM images from a series of different immune cell types (5,075 individual cells for binary classification in mixed samples and 3,424 cells for a multi-class classification task) and trained a convolutional neural network (CNN) to classify cell types based on this label-free AF as input. A low-complexity squeezeNet architecture was able to achieve reliable immune cell classification results (0.89 ROC-AUC, 0.95 PR-AUC, for binary classification in mixed samples; 0.689 F1 score, 0.697 precision, 0.748 recall, and 0.683 MCC for six-class classification in isolated samples). Perturbation tests confirmed that the model is not confused by extracellular environment and that both input AF channels (NADH and FAD) are about equally important to the classification. In the future, such predictive DL models could directly detect specific immune cells in unstained images and thus, computationally improve the specificity of label-free MPM which would have great potential for in vivo endomicroscopy.

cs.LG↗

arXiv2025

Informed total-error-minimizing priors: Interpretable cosmological parameter constraints despite complex nuisance effects

While Bayesian inference techniques are standard in cosmological analyses, it is common to interpret resulting parameter constraints with a frequentist intuition. This intuition can fail, for example, when marginalizing high-dimensional parameter spaces onto subsets of parameters, because of what has come to be known as projection effects or prior volume effects. We present the method of informed total-error-minimizing (ITEM) priors to address this problem. An ITEM prior is a prior distribution on a set of nuisance parameters, such as those describing astrophysical or calibration systematics, intended to enforce the validity of a frequentist interpretation of the posterior constraints derived for a set of target parameters (e.g., cosmological parameters). Our method works as follows. For a set of plausible nuisance realizations, we generate target parameter posteriors using several different candidate priors for the nuisance parameters. We reject candidate priors that do not accomplish the minimum requirements of bias (of point estimates) and coverage (of confidence regions among a set of noisy realizations of the data) for the target parameters on one or more of the plausible nuisance realizations. Of the priors that survive this cut, we select the ITEM prior as the one that minimizes the total error of the marginalized posteriors of the target parameters. As a proof of concept, we applied our method to the density split statistics measured in Dark Energy Survey Year 1 data. We demonstrate that the ITEM priors substantially reduce prior volume effects that otherwise arise and that they allow for sharpened yet robust constraints on the parameters of interest.

astro-ph.CO↗

arXiv2025

Cosmology on point: modelling spectroscopic tracer one-point statistics

The 1-point matter density probability distribution function (PDF) captures some of the non-Gaussian information lost in standard 2-point statistics. The matter PDF can be well predicted at mildly non-linear scales using large deviations theory. This work extends those predictions to biased tracers like dark matter halos and the galaxies they host. We model the conditional PDF of tracer counts given matter density using a tracer bias and stochasticity model previously used for photometric data. We find accurate parametrisations for tracer bias with a smoothing scale-independent 2-parameter Gaussian Lagrangian bias model and a quadratic shot noise. We relate those bias and stochasticity parameters to the one for the power spectrum and tracer-matter covariances. We validate the model against the Quijote suite of N-body simulations and find excellent agreement for both halo and galaxy density PDFs and their cosmology dependence. We demonstrate the constraining power of the tracer PDFs and their complementarity to power spectra through a Fisher forecast. We focus on the cosmological parameters $σ_8$ and $Ω_m$ as well as linear bias parameters, finding that the strength of the tracer PDF lies in disentangling tracer bias from cosmology. Our results show promise for applications to spectroscopic clustering data when augmented with a redshift space distortion model

astro-ph.CO↗

arXiv2024

Simulation-based inference has its own Dodelson-Schneider effect (but it knows that it does)

Making inferences about physical properties of the Universe requires knowledge of the data likelihood. A Gaussian distribution is commonly assumed for the uncertainties with a covariance matrix estimated from a set of simulations. The noise in such covariance estimates causes two problems: it distorts the width of the parameter contours, and it adds scatter to the location of those contours which is not captured by the widths themselves. For non-Gaussian likelihoods, an approximation may be derived via Simulation-Based Inference (SBI). It is often implicitly assumed that parameter constraints from SBI analyses, which do not use covariance matrices, are not affected by the same problems as parameter estimation with a covariance matrix estimated from simulations. We investigate whether SBI suffers from effects similar to those of covariance estimation in Gaussian likelihoods. We use Neural Posterior and Likelihood Estimation with continuous and masked autoregressive normalizing flows for density estimation. We fit our approximate posterior models to simulations drawn from a Gaussian linear model, so that the SBI result can be compared to the true posterior. We test linear and neural network based compression, demonstrating that neither methods circumvent the issues of covariance estimation. SBI suffers an inflation of posterior variance that is equal or greater than the analytical result in covariance estimation for Gaussian likelihoods for the same number of simulations. The assumption that SBI requires a smaller number of simulations than covariance estimation for a Gaussian likelihood analysis is inaccurate. The limitations of traditional likelihood analysis with simulation-based covariance remain for SBI with a finite simulation budget. Despite these issues, we show that SBI correctly draws the true posterior contour given enough simulations.

astro-ph.CO↗

arXiv2024

Remote Analysis of Femoroacetabular Impingement by a triade of label-free optical spectroscopy techniques

This paper introduces the combination of Laser-induced breakdown spectroscopy (LIBS), Raman spectroscopy (RS) and diffuse reflectance spectroscopy (DRS) in the field of biomedical research. Thereby, the results from RS and LIBS are combined with previous DRS results. These noninvasive optical methods, used together, offer thorough analysis of the absorptive and scattering behaviour, elemental composition, and molecular bindings of tissue, without resulting in considerable harm or changes to the sample or the requirement of markers. The study centres on applying this triad of techniques to tissues affected by Femoroacetabular Impingement (FAI), a condition characterised by reduced hip mobility due to developmental deformities of the hip joint. The research results in a biochemical pathway model of the condition causing the red staining caused by FAI which origin was unknown until now. This proves that this approach may have significant implications for numerous medical applications and support in exploring complex chemical and biochemical pathways.

physics.optics↗

arXiv2024

Holographic phenomenology via overlapping degrees of freedom

The holographic principle suggests that regions of space contain fewer physical degrees of freedom than would be implied by conventional quantum field theory. Meanwhile, in Hilbert spaces of large dimension $2^n$, it is possible to define $N \gg n$ Pauli algebras that are nearly anti-commuting (but not quite) and which can be thought of as "overlapping degrees of freedom". We propose to model the phenomenology of holographic theories by allowing field-theory modes to be overlapping, and derive potential observational consequences. In particular, we build a Fermionic quantum field whose effective degrees of freedom approximately obey area scaling and satisfy a cosmic Bekenstein bound, and compare predictions of that model to cosmic neutrino observations. Our implementation of holography implies a finite lifetime of plane waves, which depends on the overall UV cutoff of the theory. To allow for neutrino flux from blazar TXS 0506+056 to be observable, our model needs to have a cutoff $k_{\mathrm{UV}} \lesssim 500\, k_{\mathrm{LHC}}\,$. This is broadly consistent with current bounds on the energy spectrum of cosmic neutrinos from IceCube, but high energy neutrinos are a potential challenge for our model of holography. We motivate our construction via quantum mereology, i.e. using the idea that EFT degrees of freedom should emerge from an abstract theory of quantum gravity by finding quasi-classical Hilbert space decompositions. We also discuss how to extend the framework to Bosons. Finally, using results from random matrix theory we derive an analytical understanding of the energy spectrum of our theory. The numerical tools used in this work are publicly available within the GPUniverse package, https://github.com/OliverFHD/GPUniverse .

hep-th↗

arXiv2024

Bounds on galaxy stochasticity from halo occupation distribution modeling

The joint probability distribution of matter overdensity and galaxy counts in cells is a powerful probe of cosmology, and the extent to which variance in galaxy counts at fixed matter density deviates from Poisson shot noise is not fully understood. The lack of informed bounds on this stochasticity is currently the limiting factor in constraining cosmology with the galaxy-matter PDF. We investigate stochasticity in the conditional distribution of galaxy counts at fixed matter density and present a halo occupation distribution (HOD)-based approach for obtaining plausible ranges for stochasticity parameters. To probe the high-dimensional space of possible galaxy-matter connections, we derive HODs which conserve linear galaxy bias and number density to produce redMaGiC-like galaxy catalogs within the AbacusSummit suite of N-body simulations. We study the impact of individual HOD parameters and cosmology on stochasticity and perform a Monte Carlo search in HOD parameter space, subject to the constraints on bias and density. In mock catalogs generated by the selected HODs, shot noise in galaxy counts spans both sub-Poisson and super-Poisson values, ranging from 80% to 133% of Poisson variance at mean matter density. Nearly all derived HODs show a positive relationship between local matter density and stochasticity. For galaxy catalogs with higher stochasticity, quadratic galaxy bias is required for an accurate description of the conditional PDF of galaxy counts at fixed matter density. The presence of galaxy assembly bias also substantially extends the range of stochasticity in the super-Poisson direction. This HOD-based approach leverages degrees of freedom in the galaxy-halo connection to obtain informed bounds on model nuisance parameters and can be adapted to other parametrizations of stochasticity, in particular to motivate prior ranges for cosmological analyses.

astro-ph.CO↗

arXiv2023

Cosmology from the integrated shear 3-point correlation function: simulated likelihood analyses with machine-learning emulators

The integrated shear 3-point correlation function $ζ_{\pm}$ measures the correlation between the local shear 2-point function $ξ_{\pm}$ and the 1-point shear aperture mass in patches of the sky. Unlike other higher-order statistics, $ζ_{\pm}$ can be efficiently measured from cosmic shear data, and it admits accurate theory predictions on a wide range of scales as a function of cosmological and baryonic feedback parameters. Here, we develop and test a likelihood analysis pipeline for cosmological constraints using $ζ_{\pm}$. We incorporate treatment of systematic effects from photometric redshift uncertainties, shear calibration bias and galaxy intrinsic alignments. We also develop an accurate neural-network emulator for fast theory predictions in MCMC parameter inference analyses. We test our pipeline using realistic cosmic shear maps based on $N$-body simulations with a DES Y3-like footprint, mask and source tomographic bins, finding unbiased parameter constraints. Relative to $ξ_{\pm}$-only, adding $ζ_{\pm}$ can lead to $\approx 10-25\%$ improvements on the constraints of parameters like $A_s$ (or $σ_8$) and $w_0$. We find no evidence in $ξ_{\pm} + ζ_{\pm}$ constraints of a significant mitigation of the impact of systematics. We also investigate the impact of the size of the apertures where $ζ_{\pm}$ is measured, and of the strategy to estimate the covariance matrix ($N$-body vs. lognormal). Our analysis solidifies the strong potential of the $ζ_{\pm}$ statistic and puts forward a pipeline that can be readily used to improve cosmological constraints using real cosmic shear data.

astro-ph.CO↗

arXiv2023

The cumulant generating function as a novel observable to cumulate weak lensing information

Key non-Gaussian properties of cosmological fields can be captured by their one-point statistics, providing a complement to two-point statistical measurements from power spectra or correlation functions. Large deviation theory can robustly predict the one-point statistics of cosmological density fields on mildly non-linear scales from first principles. It provides a direct prediction for the cumulant generating function (CGF) of such fields, from which a prediction for the more commonly used probability density function (PDF) is extracted through an inverse Laplace transform. For joint one-point statistics of multiple fields, the inverse Laplace transform rapidly becomes more cumbersome and computationally expensive. In this work, we demonstrate for the first time that the weak lensing CGF itself can be used as an observable that captures an equal amount of cosmological information to the PDF. While we use the weak-lensing convergence field as a simplistic and instructive example, this work is intended as a first step towards a cosmological analysis based on large deviation theory in the context of a nulling framework, which excludes contributions from small scales to facilitate highly accurate theoretical predictions. In this context, the method should be generally applicable for a multi-scale tomographic analysis of weak lensing and galaxy clustering.

astro-ph.CO↗

arXiv2023

Beyond 3$\times$2-point cosmology: the integrated shear and galaxy 3-point correlation functions

We present the integrated 3-point correlation functions (3PCF) involving both the cosmic shear and the galaxy density fields. These are a set of higher-order statistics that describe the modulation of local 2-point correlation functions (2PCF) by large-scale features in the fields, and which are easy to measure from galaxy imaging surveys. Based on previous works on the shear-only integrated 3PCF, we develop the theoretical framework for modelling 5 new statistics involving the galaxy field and its cross-correlations with cosmic shear. Using realistic galaxy and cosmic shear mocks from simulations, we determine the regime of validity of our models based on leading-order standard perturbation theory with an MCMC analysis that recovers unbiased constraints of the amplitude of fluctuations parameter $A_s$ and the linear and quadratic galaxy bias parameters $b_1$ and $b_2$. Using Fisher matrix forecasts for a DES-Y3-like survey, relative to baseline analyses with conventional 3$\times$2PCFs, we find that the addition of the shear-only integrated 3PCF can improve cosmological parameter constraints by $20-40\%$. The subsequent addition of the new statistics introduced in this paper can lead to further improvements of $10-20\%$, even when utilizing only conservatively large scales where the tree-level models are valid. Our results motivate future work on the galaxy and shear integrated 3PCFs, which offer a practical way to extend standard analyses based on 3$\times$2PCFs to systematically probe the non-Gaussian information content of cosmic density fields.

astro-ph.CO↗

arXiv2023

Tensorial tomographic Fourier Ptychography with applications to muscle tissue imaging

We report Tensorial tomographic Fourier Ptychography (ToFu), a new non-scanning label-free tomographic microscopy method for simultaneous imaging of quantitative phase and anisotropic specimen information in 3D. Built upon Fourier Ptychography, a quantitative phase imaging technique, ToFu additionally highlights the vectorial nature of light. The imaging setup consists of a standard microscope equipped with an LED matrix, a polarization generator, and a polarization-sensitive camera. Permittivity tensors of anisotropic samples are computationally recovered from polarized intensity measurements across three dimensions. We demonstrate ToFu's efficiency through volumetric reconstructions of refractive index, birefringence, and orientation for various validation samples, as well as tissue samples from muscle fibers and diseased heart tissue. Our reconstructions of muscle fibers resolve their 3D fine-filament structure and yield consistent morphological measurements compared to gold-standard second harmonic generation scanning confocal microscope images found in the literature. Additionally, we demonstrate reconstructions of a heart tissue sample that carries important polarization information for detecting cardiac amyloidosis.

physics.optics↗

arXiv2023

Digital staining in optical microscopy using deep learning -- a review

Until recently, conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics, fundamental research and biotechnology. Despite this role as gold-standard, staining protocols face several challenges, such as a need for extensive, manual processing of samples, substantial time delays, altered tissue homeostasis, limited choice of contrast agents for a given sample, 2D imaging instead of 3D tomography and many more. Label-free optical technologies, on the other hand, do not rely on exogenous and artificial markers, by exploiting intrinsic optical contrast mechanisms, where the specificity is typically less obvious to the human observer. Over the past few years, digital staining has emerged as a promising concept to use modern deep learning for the translation from optical contrast to established biochemical contrast of actual stainings. In this review article, we provide an in-depth analysis of the current state-of-the-art in this field, suggest methods of good practice, identify pitfalls and challenges and postulate promising advances towards potential future implementations and applications.

eess.IV↗

arXiv2023

KiDS-1000 Cosmology: Constraints from density split statistics

Context. Weak lensing and clustering statistics beyond two-point functions can capture non-Gaussian information about the matter density field, thereby improving the constraints on cosmological parameters relative to the mainstream methods based on correlation functions and power spectra. Aims. This paper presents a cosmological analysis of the fourth data release of the Kilo Degree Survey based on the density split statistics, which measures the mean shear profiles around regions classified according to foreground densities. The latter is constructed from a bright galaxy sample, which we further split into red and blue samples, allowing us to probe their respective connection to the underlying dark matter density. Methods. We use the state-of-the-art model of the density splitting statistics and validate its robustness against mock data infused with known systematic effects such as intrinsic galaxy alignment and baryonic feedback. Results. After marginalising over the photometric redshift uncertainty and the residual shear calibration bias, we measure for the full KiDS-bright sample a structure growth parameter of $S_8 = σ_8 \sqrt{Ω_\mathrm{m}/0.3} = 0.74^{+0.03}_{-0.02}$ that is competitive to and consistent with two-point cosmic shear results, a matter density of $Ω_\mathrm{m} = 0.28 \pm 0.02$, and a constant galaxy bias of $b = 1.32^{+0.12}_{-0.10}$.

astro-ph.CO↗