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Recently there have been increasing interests in learning and inference with implicit distributions (i.e., distributions without tractable densities). To this end, we develop a gradient estimator for implicit distributions based on Stein's…
Aerosol injectors applied in single-particle diffractive imaging experiments demonstrated their potential in efficiently delivering nanoparticles with high density. Continuous optimization of injector design is crucial for achieving…
This thesis explores experimental and theoretical approaches to dark matter detection, from gas-based detectors to quantum sensors, tackling the challenge of identifying dark matter, which makes up 27% of the Universe's energy. It reviews…
With the availability of thousands of type Ia supernovae in the near future the magnitude scatter induced by lensing will become a major issue as it affects parameter estimation. Current N-body simulations are too time consuming to be…
Multi-sensor state space models underpin fusion applications in networks of sensors. Estimation of latent parameters in these models has the potential to provide highly desirable capabilities such as network self-calibration. Conventional…
We extend and correct a recently proposed maximum-likelihood halo-independent method to analyze unbinned direct dark matter detection data. Instead of the recoil energy as independent variable we use the minimum speed a dark matter particle…
We compute the distribution of likelihoods from the non-parametric iterative smoothing method over a set of mock Pantheon-like type Ia supernova datasets. We use this likelihood distribution to test whether typical dark energy models are…
Hydrodynamical simulations are the most accurate way to model structure formation in the universe, but they often involve a large number of astrophysical parameters modeling subgrid physics, in addition to cosmological parameters. This…
The authors propose a robust semi-parametric empirical likelihood method to integrate all available information from multiple samples with a common center of measurements. Two different sets of estimating equations are used to improve the…
Bayesian methods offer a coherent and efficient framework for implementing uncertainties into induction problems. In this article, we review how this approach applies to the analysis of dark matter direct detection experiments. In…
The possibility of observing supernova (SN) neutrinos through the process of coherent elastic neutrino-nucleus scattering (CENNS) in future ton scale detectors designed primarily for direct detection of dark matter is investigated. In…
Direct detection of light dark matter (DM), below the GeV scale, through electron recoil can be efficient if DM has a velocity well above the virial value of $v\sim 10^{-3}$. We point out that if there is a long range attractive force…
Sensitive dark matter (DM) experiments can be well exploited beyond their designated targets, allowing to explore a breadth of physics topics. As we discuss, future large direct DM detection experiments constitute impressive telescopes,…
Direct searches for light dark matter particles (mass $<10$ GeV) are especially challenging because of the low energies transferred in elastic scattering to typical heavy nuclear targets. We investigate the possibility of using liquid…
A variety of detectors has been proposed for dark matter direct detection, but most of them -- by the fact -- are still at R&D stage. In many cases, it is claimed that the lack of an adequate detectors' radio-purity might be compensated…
A simple extension of the Standard Model consists of a scalar field that can potentially constitute the dark matter (DM). Significant attention has been devoted to probing light $\mathcal{O}(\lesssim 10~\rm{eV})$ scalar DM, with a multitude…
Photon detection is important for liquid argon detectors for direct dark matter searches or neutrino property measurements. Precise simulation of photon transport is widely used to understand the probability of photon detection in liquid…
The XENON experiment aims at the direct detection of dark matter in the form of WIMPs (Weakly Interacting Massive Particles) via their elastic scattering off Xe nuclei. A fiducial mass of 1000 kg, distributed in ten independent liquid xenon…
We propose a novel approach to parameter estimation for simulator-based statistical models with intractable likelihood. Our proposed method involves recursive application of kernel ABC and kernel herding to the same observed data. We…
We present a real-time anomaly detection framework for liquid argon time projection chambers (LArTPCs), targeting applications in particle physics experiments such as the Short Baseline Near Detector or the future Deep Underground Neutrino…