No Arabic abstract
We investigate Bayesian and frequentist approaches to resonance searches using a toy model based on an ATLAS search for the Higgs boson in the diphoton channel. We draw pseudo-data from the background only model and background plus signal model at multiple luminosities, from $10^{-3}$/fb to $10^7$/fb. We chart the change in the Bayesian posterior of the background only model and the global p-value. We find that, as anticipated, the posterior converges to certainty about the model as luminosity increases. The p-value, on the other hand, randomly walks between 0 and 1 if the background only model is true, and otherwise converges to 0. After briefly commenting on the frequentist properties of the posterior, we make a direct comparison of the significances obtained in Bayesian and frequentist frameworks. We find that the well-known look-elsewhere effect reduces local significances by about 1$sigma$. We furthermore find that significances from our Bayesian framework are typically about 1 to 2$sigma$ smaller than the global significances, though the reduction depends on the prior, global significance and integrated luminosity. This suggests that even global significances could significantly overstate the evidence against the background only model. We checked that this effect --- the Bayes effect --- was robust with respect to fourteen choices of prior and investigated the Jeffreys-Lindley paradox for three of them.
We propose a new scientific application of unsupervised learning techniques to boost our ability to search for new phenomena in data, by detecting discrepancies between two datasets. These could be, for example, a simulated standard-model background, and an observed dataset containing a potential hidden signal of New Physics. We build a statistical test upon a test statistic which measures deviations between two samples, using a Nearest Neighbors approach to estimate the local ratio of the density of points. The test is model-independent and non-parametric, requiring no knowledge of the shape of the underlying distributions, and it does not bin the data, thus retaining full information from the multidimensional feature space. As a proof-of-concept, we apply our method to synthetic Gaussian data, and to a simulated dark matter signal at the Large Hadron Collider. Even in the case where the background can not be simulated accurately enough to claim discovery, the technique is a powerful tool to identify regions of interest for further study.
The search for di-Higgs final states is typically limited at the LHC to the dominant gluon fusion channels, with weak boson fusion only assuming a spectator role. In this work, we demonstrate that when it comes to searches for resonant structures that arise from iso-singlet mixing in the Higgs sector, the weak boson fusion sideline can indeed contribute to winning the discovery game. Extending existing experimental resonance searches by including both contributions is therefore crucial.
In experiments searching for axionic dark matter, the use of the standard threshold-based data analysis discards valuable information. We present a Bayesian analysis framework that builds on an existing processing protocol to extract more information from the data of coherent axion detectors such as operating haloscopes. The analysis avoids logical subtleties that accompany the standard analysis framework and enables greater experimental flexibility on future data runs. Performing this analysis on the existing data from the HAYSTAC experiment, we find improved constraints on the axion-photon coupling $g_gamma$ while also identifying the most promising regions of parameter space within the $23.15$--$24.0$ $mu$eV mass range. A comparison with the standard threshold analysis suggests a $36%$ improvement in scan rate from our analysis, demonstrating the utility of this framework for future axion haloscope analyses.
We consider the Jeffreys-Lindley paradox from an objective Bayesian perspective by attempting to find priors representing complete indifference to sample size in the problem. This means that we ensure that the prior for the unknown mean and the prior predictive for the $t$-statistic are independent of the sample size. If successful, this would lead to Bayesian model comparison that was independent of sample size and ameliorate the paradox. Unfortunately, it leads to an improper scale-invariant prior for the unknown mean. We show, however, that a truncated scale-invariant prior delays the dependence on sample size, which could be practically significant. Lastly, we shed light on the paradox by relating it to the fact that the scale-invariant prior is improper.
The extension of the Standard Model by right-handed neutrinos can not only explain the active neutrino masses via the seesaw mechanism, it is also able solve a number of long standing problems in cosmology. Especially, masses below the TeV scale are of particular interest as they can lead to a plethora of signatures in experimental searches. We present the first full frequentist analysis of the extension of the Standard Model by three right-handed neutrinos, with masses between 60 MeV and 500 GeV, using the Global and Modular BSM (beyond the Standard Model) Inference Tool GAMBIT. Our analysis is based on the Casas-Ibarra parametrisation and includes a large range of experimental constraints: active neutrino mixing, indirect constraints from, e.g., electroweak precision observables and lepton universality, and numerous direct searches for right-handed neutrinos. To study their overall effect, we derive combined profile likelihood results for the phenomenologically most relevant parameter projections. Furthermore, we discuss the role of (marginally) statistically preferred regions in the parameter space. Finally, we explore the flavour mixing pattern of the three right-handed neutrinos for different values of the lightest neutrino mass. Our results comprise the most comprehensive assessment of the model with three right-handed neutrinos model below the TeV scale so far, and provide a robust ground for exploring the impact of future constraints or detections.