Related papers: Approximate Differentiable Likelihoods for Astropa…
In this letter we point out the possibility to study new physics in the neutrino sector using dark matter detectors based on liquid xenon. These are characterized by very good spatial resolution and extremely low thresholds for electron…
With the large progress in searches for dark matter (DM) particles with indirect and direct methods, we develop a numerical tool that enables fast calculations of the likelihoods of specified DM particle models given a number of…
The XENON1T experiment searches for dark matter particles through their scattering off xenon atoms in a 2 tonne liquid xenon target. The detector is a dual-phase time projection chamber, which measures simultaneously the scintillation and…
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics processes, we extract extra information from the simulator.…
We present a detector apparatus, DireXeno (DIRectinal Xenon), designed to measure the spatial and temporal properties of scintillation in liquid xenon to very high accuracy. The properties of scintillation are of primary importance for dark…
Calibrated estimates of uncertainty are critical for many real-world computer vision applications of deep learning. While there are several widely-used uncertainty estimation methods, dropout inference stands out for its simplicity and…
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…
Multi-dimensional parameter spaces are commonly encountered in physics theories that go beyond the Standard Model. However, they often possess complicated posterior geometries that are expensive to traverse using techniques traditional to…
Inferring models, predicting the future, and estimating the entropy rate of discrete-time, discrete-event processes is well-worn ground. However, a much broader class of discrete-event processes operates in continuous-time. Here, we provide…
Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty. Probability-estimation models are trained on observed outcomes (e.g. whether it has rained or not, or…
Monte Carlo simulations are a crucial tool for the analysis and prediction of various background components in liquid xenon (LXe) detectors. With improving shielding in new experiments, the simulation of external backgrounds, such as…
Position sensitive detectors based on gaseous scintillation proportional counters with Anger-type readout are being used in several research areas such as neutron detection, search for dark matter and neutrinoless double beta decay. Design…
The search for dark matter is one of today's most exciting fields. As bigger detectors are being built to increase their sensitivity, background reduction is an ever more challenging issue. To this end, a new type of dark matter detector is…
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…
To accurately reproduce measurements from the real world, simulators need to have an adequate model of the physical system and require the parameters of the model be identified. We address the latter problem of estimating parameters through…
Parameter inference for stochastic differential equation mixed effects models (SDEMEMs) is a challenging problem. Analytical solutions for these models are rarely available, which means that the likelihood is also intractable. In this case,…
When a posterior peaks in unexpected regions of parameter space, new physics has either been discovered, or a bias has not been identified yet. To tell these two cases apart is of paramount importance. We therefore present a method to…
Correcting for detector effects in experimental data, particularly through unfolding, is critical for enabling precision measurements in high-energy physics. However, traditional unfolding methods face challenges in scalability,…
The ratio between two probability density functions is an important component of various tasks, including selection bias correction, novelty detection and classification. Recently, several estimators of this ratio have been proposed. Most…
In the domain of physics experiments, data fitting is a pivotal technique for extracting insights from both experimental and simulated datasets. This article presents an approximation method designed to estimate the systematic errors…