Search

In this work, we present a novel approach to transform supervised classifiers into effective unsupervised anomaly detectors. The method we have developed, termed Discriminatory Detection of Distortions (DDD), enhances anomaly detection by training a discriminator model on both original and artificially modified datasets. We conducted a comprehensive evaluation of our models on the Dark Machines Anomaly Score Challenge channels and a search for 4-top quark events, demonstrating the effectiveness of our approach across various final states and beyond the Standard Model scenarios. We compare the performance of the DDD method with the Deep Robust One-Class Classification method (DROCC), which incorporates signals in the training process, and the Deep Support Vector Data Description (DeepSVDD) method, a well established and well performing method for anomaly detection. Results show that the effectiveness of each model varies by signal and channel, with DDD proving to be a very effective anomaly detector. We recommend the combined use of DeepSVDD and DDD for purely unsupervised applications, with the addition of flow models for improved performance when resources allow. Findings suggest that network architectures that excel in supervised contexts, such as the particle transformer with standard model interactions, also perform well as unsupervised anomaly detectors. We also show that with these methods, it is likely possible to recognize 4-top quark production as an anomaly without prior knowledge of the process. We argue that the Large Hadron Collider community can transform supervised classifiers into anomaly detectors to uncover potential new physical phenomena in each search.

The $U(1)_{L_\mu-L_\tau}$ extended Standard Model (SM) is anomaly free, and contains a massive $Z^\prime$ boson. The associated Higgs, which generates the $Z'$'s mass via spontaneous symmetry breaking (SSB) can mix with the SM Higgs. The new parameters relating to extra Higgs cannot be probed at the LHC with final states containing no more than $4$ leptons. Therefore, we use signatures with at least $6$ leptons to probe the parameter space through LHC experiments. Since SM predicts a lower cross section for a final state with at least $6$ leptons than is currently visible at the LHC, the background is negligible, thereby making this channel an extremely sensitive probe. We find that in a limited region of the parameter space this channel can strongly constrain the associated $U(1)_{L_\mu-L_\tau}$ coupling constant, even more so than for a $4$ lepton or fewer final state., Comment: 5 pages, 3 figures. Minor changes based on comments received

Neutral particles capable of travelling cosmic distances from a source to detectors on Earth are limited to photons and neutrinos. Examination of the Dark Matter annihilation/decay spectra for these particles reveals the presence of continuum spectra (e.g. due to fragmentation and W or Z decay) and peaks (due to direct annihilations/decays). However, when one explores extensions of the Standard Model (BSM), unexplored spectra emerge that differ significantly from those of the Standard Model (SM) for both neutrinos and photons. In this paper, we argue for the inclusion of important spectra that include peaks as well as previously largely unexplored entities such as boxes and combinations of box, peak and continuum decay spectra., Comment: 7 pages, 2 figures

A major task in particle physics is the measurement of rare signal processes. These measurements are highly dependent on the classification accuracy of these events in relation to the huge background of other Standard Model processes. Reducing the background by a few tens of percent with the same signal efficiency can already increase the sensitivity considerably. This work demonstrates the importance of incorporating physical information into deep learning-based event selection. The paper includes this information into different methods for classifying events, in particular Boosted Decision Trees, Transformer Architectures (Particle Transformer) and Graph Neural Networks (Particle Net). In addition to the physical information previously proposed for jet tagging, we add particle measures for energy-dependent particle-particle interaction strengths as predicted by the leading order interactions of the Standard Model (SM). We find that the integration of physical information into the attention matrix (transformers) or edges (graphs) notably improves background rejection by $10\%$ to $40\%$ over baseline models (a graph network), with about $10\%$ of this improvement directly attributable to what we call the SM interaction matrix. In a simplified statistical analysis, we find that such architectures can improve the significance of signals by a significant factor compared to a graph network (our base model)., Comment: Version 2 of this paper supersedes arXiv:2211.05143v1. 26 pages, 5 figures

Model-independent search strategies have been increasingly proposed in recent years because on the one hand there has been no clear signal for new physics and on the other hand there is a lack of a highly probable and parameter-free extension of the standard model. For these reasons, there is no simple search target so far. In this work, we try to take a new direction and ask the question: bearing in mind that we have a large number of new physics theories that go beyond the Standard Model and may contain a grain of truth, can we improve our search strategy for unknown signals by using them "in combination"? In particular, we show that a signal hypothesis based on a large, intermingled set of many different theoretical signal models can be a superior approach to find an unknown BSM signal. Applied to a recent data challenge, we show that "mixture-of-theories training" outperforms strategies that optimize signal regions with a single BSM model as well as most unsupervised strategies. Applications of this work include anomaly detection and the definition of signal regions in the search for signals of new physics., Comment: 40 pages, 10 figures

Neutrino telescope experiments are rapidly becoming more competitive in indirect detection searches for dark matter. Neutrino signals arising from dark matter annihilations are typically assumed to originate from the hadronisation and decay of Standard Model particles. Here we showcase a supersymmetric model, the BLSSMIS, that can simultaneously obey current experimental limits while still providing a potentially observable non-standard neutrino spectrum from dark matter annihilation., Comment: 10 pages, 6 figures

Extending the Standard Model (SM) by a $U(1)_{L_\mu-L_\tau}$ group gives potentially significant new contributions to $g_\mu-2$, allows the construction of realistic neutrino mass matrices, incorporates lepton universality violation, and offers an anomaly-free mediator for a Dark Matter (DM) sector. In a recent analysis we showed that published LHC searches are not very sensitive to this model. Here we apply several Machine Learning (ML) algorithms in order to distinguish this model from the SM using simulated LHC data. In particular, we optimize the $3\mu$-signal, which has a considerably larger cross section than the $4\mu$-signal. Furthermore, since the $2$-muon plus missing $E_T$ final state gets contributions from diagrams involving DM particles, we optimize it as well. We find greatly improved sensitivity, which already for $36$ fb$^{-1}$ of data exceeds the combination of published LHC and non-LHC results. We also emphasize the usefulness of Boosted Decision Trees which, unlike Neural Networks, easily allow to extract additional information from the data which directly connect to the theoretical model through feature importance. The same scheme could be used to analyze other models., Comment: 39 pages, 10 figures

We apply LHC data to constrain a simplified extension of the Standard Model containing a new spin-1 mediator $R$, which does not couple to first generation quarks, and a spinor dark matter particle $\chi$. We recast ATLAS and CMS searches for final states containing one or more jet(s) + missing $E_T$, with or without $b$ tags, as well as searches for di-jet resonances with $b$ or $t$ tagging. We find that LHC constraints on the axial vector couplings of the mediator are always stronger than the unitarity bound, which scales like $m_R/m_t$. If $R$ has a sizable invisible branching ratio, the strongest LHC bound on both vector couplings and axial vector coupling comes from a di-jet + missing $E_T$ search with or without double $b$ tag. These bounds are quite strong for $m_R < 1$ TeV, even though we have switched off all couplings to valence quarks. Searches for a di-jet resonance with double $b$ tag lead to comparable bounds with the previous results even if $R \rightarrow \chi \bar \chi$ decays are allowed; these are the only sensitive LHC searches if the invisible branching ratio of $R$ is very small or zero., Comment: 13 pages, 2 figures

In this study, we apply LHC data to constrain the extension of the Standard Model by an anomaly-free $U(1)_{L_\mu-L_\tau}$ gauge group; this model contains a new gauge boson ($Z^\prime$) and a scalar dark matter particle ($\phi_{\rm DM}$). We recast a large number of LHC analyses from ATLAS and CMS of multi-lepton final states. We find that for $10$ GeV $< m_{Z^\prime} < 60$ GeV the strongest constraint comes from a dedicated $Z^\prime$ search in the $4\mu$ final state by the CMS collaboration; for larger $Z^\prime$ masses, searches for final states with three leptons plus missing $E_T$ are more sensitive. Searches for final states with two leptons and missing $E_T$, which are sensitive to $Z^\prime$ decays into dark matter particles, can only probe regions of parameter space that are excluded by searches in the $3$ and $4$ lepton channels. The combination of LHC data excludes values of $Z^\prime$ mass and coupling constant that can explain the deficit in $g_\mu-2$ for $4$ GeV $\leq m_{Z^\prime} \leq 500$ GeV. However, for much of this range the LHC bound is weaker than the bound that can be derived from searches for trident events in neutrino-nucleus scattering., Comment: 15 pages, 4 figures

We apply data taken at the $e^+e^-$ collider LEP in the 1990's at center-of-mass energy up to 209 GeV to constrain Dark Matter models with a light leptophobic spin-1 mediator $R$. We assume that the dark sector particle (DSP) is a spin-1/2 fermion $\chi$. This scenario is well studied in the context of LHC searches for mediator mass from 100 GeV to several TeV. Emission of the mediator off a quark or antiquark at LEP gives rise to di-jet plus missing energy and 4-jet signatures, which we use to limit the relevant couplings. We focus on scenarios with $2 m_\chi > m_R$, which are poorly constrained by LHC data. We recast published searches by the ALEPH collaboration. For $m_\chi < 20$ GeV the best bounds result from an analysis at $\sqrt{s} \simeq M_Z$ of di-jet plus missing energy events. For heavier DSP but $m_R < 70$ GeV meaningful bounds can be derived from a four jet analysis at $\sqrt{s} = 183$ GeV. Unfortunately published searches using four jet final states at $\sqrt{s} \simeq M_Z$ use only a small fraction of the total data sample. Moreover, all published searches for di-jet plus missing energy final states at $\sqrt{s} \geq 130$ GeV have poor efficiency for our model; we therefore design new cuts that combine good background rejection with higher efficiency. Re-analyzing the higher energy data using our new cuts, and an analysis of the complete four jet data sample taken at $\sqrt{s}\simeq M_Z$, can explore new regions of parameter space., Comment: 28 pages, 16 figures