We introduce a new nonparametric density estimator inspired by Markov Chains, and generalizing the well-known Kernel Density Estimator (KDE). Our estimator presents several benefits with respect to the usual ones and can be used straightforwardly as
a foundation in all density-based algorithms. We prove the consistency of our estimator and we find it typically outperforms KDE in situations of large sample size and high dimensionality. We also employ our density estimator to build a local outlier detector, showing very promising results when applied to some realistic datasets.
One of the key indicators used in tracking the evolution of an infectious disease isthe reproduction number. This quantity is usually computed using the reportednumber of cases, but ignoring that many more individuals may be infected (e.g.asymptomati
cs). We propose a statistical procedure to quantify the impact of un-detected infectious cases on the determination of the effective reproduction number. Our approach is stochastic, data-driven and not relying on any compartmentalmodel. It is applied to the COVID-19 case in eight different countries and all Italianregions, showing that the effect of undetected cases leads to estimates of the effective reproduction numbers larger than those obtained only with the reported cases by factors ranging from two to ten. Our findings urge caution about deciding when and how to relax containment measures based on the value of the reproduction number.
We study the non-minimal composite Higgs model with global symmetry $text{SO}(7)$ broken to $text{SO}(5)times text{SO}(2)$. The model results in a composite Two-Higgs doublet model (2HDM) equipped with two extra singlets, the lightest of which can be
a viable dark matter candidate. The model is able to reproduce the correct dark matter relic density both via the usual thermal freeze-out and through late time decay of the heavier singlet. In the case of thermal freeze-out, it is possible to evade current experimental constraints even with the minimum fine tuning allowed by electroweak precision tests.
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.
Colored dark sectors where the dark matter particle is accompanied by colored partners have recently attracted theoretical and phenomenological interest. We explore the possibility that the dark sector consists of the dark matter particle and a color
-octet partner, where the interaction with the Standard Model is governed by an effective operator involving gluons. The resulting interactions resemble the color analogues of electric and magnetic dipole moments. Although many phenomenological features of this kind of model only depend on the group representation of the partner under SU(3)$_c$, we point out that interesting collider signatures such as $R$-hadrons are indeed controlled by the interaction operator between the dark and visible sector. We perform a study of the current constraints and future reach of LHC searches, where the complementarity between different possible signals is highlighted and exploited.
We investigate constraints on scalar dark matter (DM) by analyzing the Lyman-alpha forest, which probes structure formation at medium and small scales, and also by studying its cosmological consequences at high and low redshift. For scalar DM that co
nstitutes more than 30% of the total DM density, we obtain a lower limit m >~ 10^{-21} eV for the mass of scalar DM. This implies an upper limit on the initial field displacement (or the decay constant for an axion-like field) of phi <~ 10^{16} GeV. We also derive limits on the energy scale of cosmic inflation and establish an upper bound on the tensor-to-scalar ratio of r < 10^{-3} in the presence of scalar DM. Furthermore, we show that there is very little room for ultralight scalar DM to solve the small-scale crisis of cold DM without spoiling the Lyman-alpha forest results. The constraints presented in this paper can be used for testing generic theories that contain light scalar fields.
Displaced vertices are relatively unusual signatures for dark matter searches at the LHC. We revisit the model of pseudo-Dirac dark matter (pDDM), which can accommodate the correct relic density, evade direct detection constraints, and generically pr
ovide observable collider signatures in the form of displaced vertices. We use this model as a benchmark to illustrate the general techniques involved in the analysis, the complementarity between monojet and displaced vertex searches, and provide a comprehensive study of the current bounds and prospective reach.
Dark matter with momentum- or velocity-dependent interactions with nuclei has shown significant promise for explaining the so-called Solar Abundance Problem, a longstanding discrepancy between solar spectroscopy and helioseismology. The best-fit mode
ls are all rather light, typically with masses in the range of 3-5 GeV. This is exactly the mass range where dark matter evaporation from the Sun can be important, but to date no detailed calculation of the evaporation of such models has been performed. Here we carry out this calculation, for the first time including arbitrary velocity- and momentum-dependent interactions, thermal effects, and a completely general treatment valid from the optically thin limit all the way through to the optically thick regime. We find that depending on the dark matter mass, interaction strength and type, the mass below which evaporation is relevant can vary from 1 to 4 GeV. This has the effect of weakening some of the better-fitting solutions to the Solar Abundance Problem, but also improving a number of others. As a by-product, we also provide an improved derivation of the capture rate that takes into account thermal and optical depth effects, allowing the standard result to be smoothly matched to the well-known saturation limit.
We generalize in several directions our recent analysis of the limitations to the use of the effective field theory approach to study dark matter at the LHC. Firstly, we study the full list of operators connecting fermion DM to quarks and gluons, cor
responding to integrating out a heavy mediator in the $s$-channel; secondly, we provide analytical results for the validity of the EFT description for both $sqrt{s}=8$ {rm TeV} and $14$ {rm TeV}; thirdly, we make use of a MonteCarlo event generator approach to assess the validity of our analytical conclusions. We apply our results to revisit the current collider bounds on the ultraviolet cut-off scale of the effective field theory and show that these bounds are weakened once the validity conditions of the effective field theory are imposed.
We discuss the limitations to the use of the effective field theory approach to study dark matter at the LHC. We introduce and study a few quantities, some of them independent of the ultraviolet completion of the dark matter theory, which quantify th
e error made when using effective operators to describe processes with very high momentum transfer. Our criteria indicate up to what cutoff energy scale, and with what precision, the effective description is valid, depending on the dark matter mass and couplings.
