相关论文: Introducing Model Benchmark Analysis for dark matt…
We study neutrino mass generation and dark matter in a left-right symmetric model. The model is based on an $SU(3)_c\times SU(2)_L \times SU(2)_R \times U(1)_{B-L}$ gauge theory with a softly broken parity symmetry. Masses of the charged…
We introduce a model designed to describe charged particles as stable topological solitons of a field with values on the internal space S^3. These solitons behave like particles with relativistic properties like Lorentz contraction and…
To properly solve the coincidence problem ($\Omega_\mathrm{DM} \simeq 5\Omega_\mathrm{VM}$) in a model of asymmetric dark matter, one cannot simply relate the number densities of visible and dark matter without also relating their particle…
The sensitivity of direct searches for heavy neutral leptons (HNLs) in accelerator-based experiments depends strongly on the particles properties. Commonly used benchmark scenarios are important to ensure comparability and consistency…
In this work we study a class of leptophilic dark matter models, where the dark matter interacts with the standard model particles via the $U(1)_{L_i-L_j}$ gauge boson, to explain the $e^{\pm}$ excess in cosmic rays observed by ATIC and…
We study renormalisable models with minimal field content that can provide a viable Dark Matter candidate through the standard freeze-out paradigm and, simultaneously, accommodate the observed anomalies in semileptonic $B$-meson decays at…
Minimal atomic dark matter with its distinctive cooling mechanisms offers an instructive framework for understanding the potential impact of dark matter on small-scale structure formation and early cosmology. The model consists of two…
The proposed DarkQuest beam dump experiment, a modest upgrade to the existing SeaQuest/SpinQuest experiment, has great potential for uncovering new physics within a dark sector. We explore both the near-term and long-term prospects for…
The parameters of a linear compartment model are usually estimated from experimental input-output data. A problem arises when infinitely many parameter values can yield the same result; such a model is called unidentifiable. In this case,…
Persistent homology analysis provides means to capture the connectivity structure of data sets in various dimensions. On the mathematical level, by defining a metric between the objects that persistence attaches to data sets, we can…
We study a model with $U(1)$ gauged lepton number symmetry in which the active neutrino masses are generated at two-loop level through the spontaneous breaking of the lepton number symmetry. To cancel gauge anomalies some exotic leptons are…
We use the framework of dark matter effective field theories to study the complementarity of bounds for a dark matter particle with mass in the MeV range. Taking properly into account the mixing between operators induced by the…
We propose some scenarios to pursue dark matter searches at the LHC in a fairly model-independent way. The first benchmark case is dark matter co-annihilations with coloured particles (gluinos or squarks being special examples). We…
The rapid proliferation of benchmarks for evaluating large language models (LLMs) has created an urgent need for systematic methods to assess benchmark quality itself. We propose Benchmark^2, a comprehensive framework comprising three…
We stress the importance of the circa 20 parameters in the Standard Model, which are not fixed by the model but only determined experimentally, as a window to the physics beyond the Standard Model. However, it is a tiny window in as far as…
The simple 3-3-1 model that contains the minimal lepton and minimal scalar contents is detailedly studied. The impact of the inert scalars (i.e., the extra fundamental fields that provide realistic dark matter candidates) on the model is…
We study freeze-in dark matter production in models that rely on the Clockwork mechanism to suppress the dark matter couplings to the visible sector. We construct viable scalar and fermionic dark matter models within this Clockwork FIMP…
We study the dark matter phenomenology of scotogenic frameworks through a rather illustrative model extending the Standard Model by scalar and fermionic singlets and doublets. Such a setup is phenomenologically attractive since it provides…
Complex prediction models such as deep learning are the output from fitting machine learning, neural networks, or AI models to a set of training data. These are now standard tools in science. A key challenge with the current generation of…
Neural networks encode inputs as high-dimensional vectors, known as representations, that capture how models process data by encoding task-relevant structure and semantics. Representation alignment refers to the degree to which different…