Related papers: Efficiently Exploring Multi-Dimensional Parameter …
We systematically study the allowed parameter space for the flavor composition of astrophysical neutrinos measured at Earth, including beyond the Standard Model theories at production, during propagation, and at detection. One motivation is…
Computational models have become a powerful tool in the quantitative sciences to understand the behaviour of complex systems that evolve in time. However, they often contain a potentially large number of free parameters whose values cannot…
In this work, we propose using the mixture density network (MDN) to estimate cosmological parameters. We test the MDN method by constraining parameters of the $\Lambda$CDM and $w$CDM models using Type Ia supernovae and the power spectra of…
Constraints can be interpreted in a broad sense as any kind of explicit restriction over the parameters. While some constraints are defined directly on the parameter space, when they are instead defined by known behaviour on the model,…
Neutrino experiments study the least understood of the Standard Model particles by observing their direct interactions with matter or searching for ultra-rare signals. The study of neutrinos typically requires overcoming large backgrounds,…
Models of stochastic processes are widely used in almost all fields of science. Theory validation, parameter estimation, and prediction all require model calibration and statistical inference using data. However, data are almost always…
The abundance of models of complex networks and the current insufficient validation standards make it difficult to judge which models are strongly supported by data and which are not. We focus here on likelihood maximization methods for…
We present a Markov Chain Monte Carlo global analysis of neutrino parameters using both cosmological and experimental data. Results are presented for the combination of all presently available data from oscillation experiments, cosmology,…
Our knowledge on the active 3$\nu$ mixing angles ($\theta_{12}$, $\theta_{13}$, and $\theta_{23}$) and the CP phase $\delta_{\mathrm{CP}}$ is becoming accurate day-by-day enabling us to test the unitarity of the leptonic mixing matrix with…
Precise modeling of detector energy response is crucial for next-generation neutrino experiments which present computational challenges due to lack of analytical likelihoods. We propose a solution using neural likelihood estimation within…
Monte Carlo is famous for accepting model extensions and model refinements up to infinite dimension. However, this powerful incremental design is based on a premise which has severely limited its application so far: a state-variable can…
Monte Carlo methods to evaluate and maximize the likelihood function enable the construction of confidence intervals and hypothesis tests, facilitating scientific investigation using models for which the likelihood function is intractable.…
We show that density models describing multiple observables with (i) hard boundaries and (ii) dependence on external parameters may be created using an auto-regressive Gaussian mixture model. The model is designed to capture how observable…
Fine-tuning in physics and cosmology is often used as evidence that a theory is incomplete. For example, the parameters of the standard model of particle physics are "unnaturally" small (in various technical senses), which has driven much…
High-dimensional predictive models, those with more measurements than observations, require regularization to be well defined, perform well empirically, and possess theoretical guarantees. The amount of regularization, often determined by…
Dark photons, hypothetical feebly interacting massive vector bosons, appear in many extensions of the Standard Model. This study investigates their production and subsequent decay during supernova explosions. We demonstrate that the decay…
The development of mathematically complete and consistent models solving the so-called "measurement problem", strongly renewed the interest of the scientific community for the foundations of quantum mechanics, among these the Dynamical…
In this thesis, we assess the sensitivity of next-generation long-baseline neutrino oscillation experiments, DUNE, T2HK, and T2HKK, to three popular beyond the Standard Model (BSM) scenarios. Within the three BSM studies, we examine: (i)…
By considering the effect of varying the target radii and detector aperture width on the scattering angle in experimental Compton scattering, mathematical models were developed and subsequently incorporated into Monte Carlo simulations. By…
We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any…