This paper explores the sensitivity of the Cherenkov Telescope Array Observatory to dark matter annihilation in the Galactic Center, within the frameworks of Effective Field Theory and Simplified Models. We present sensitivity forecasts, utilizing an up-to-date instrument configuration and incorporating the latest models for Galactic Diffuse Emission. A key aspect of our work is the inclusion of updated dark matter density profiles, J-factors, and velocity dispersion distributions derived from the FIRE-2 cosmological hydrodynamical simulations, which significantly impact the expected indirect detection signals. Furthermore, we update the constraints from direct detection experiments (Xenon1T and LZ) taking into account the astrophysical uncertainties informed by the FIRE-2 simulations, and also investigate limits coming from collider searches (ATLAS and CMS). Our analysis reveals improved constraints on the effective suppression scale ($M_*$) in the Effective Field Theory framework and on the mediator mass ($M_{med}$) in Simplified Models compared to previous studies, highlighting the complementarity of the Cherenkov Telescope Array Observatory with direct and collider searches in probing a wide range of dark matter scenarios. We discuss the implications of these results for various dark matter interaction types, including scalar, pseudoscalar, vector, and axial-vector mediators, and emphasize the importance of considering realistic astrophysical inputs in interpreting dark matter search results across different experimental fronts.

Applying Transformers to irregular time-series typically requires specializations to their baseline architecture, which can result in additional computational overhead and increased method complexity. We present the Rotary Masked Autoencoder (RoMAE), which utilizes the popular Rotary Positional Embedding (RoPE) method for continuous positions. RoMAE is an extension to the Masked Autoencoder (MAE) that enables interpolation and representation learning with multidimensional continuous positional information while avoiding any time-series-specific architectural specializations. We showcase RoMAE's performance on a variety of modalities including irregular and multivariate time-series, images, and audio, demonstrating that RoMAE surpasses specialized time-series architectures on difficult datasets such as the DESC ELAsTiCC Challenge while maintaining MAE's usual performance across other modalities. In addition, we investigate RoMAE's ability to reconstruct the embedded continuous positions, demonstrating that including learned embeddings in the input sequence breaks RoPE's relative position property.

Detecting localized differences between two samples is a central task in scientific data analysis, required for the identification of signal events, regime changes, or model mismatch. We introduce EagleEye, a method that pinpoints local over- and under-densities in multivariate feature spaces. EagleEye assigns each point an anomaly score by encoding its ordered k-nearest-neighbour list as a binary membership sequence and testing whether the cumulative number of successes in this sequence is consistent with a binomial (coin-flipping) null model. In the presence of a genuine local anomaly, neighbours will preferentially belong to one of the two datasts, yielding an excess of “successes” relative to the binomial null model. These local, pointwise detections are consolidated into interpretable anomaly sets through a deterministic refinement procedure that can also estimate the irreducible background and local density anomaly purity. We demonstrate EagleEye's efficacy in three scenarios. We first consider an artificial data example with known localized over- and under-densities. Second, we demonstrate how EagleEye may be used for new physics searches at particle collider experiments in the presence of systematic background modelling differences. Finally, we conduct a climate analysis study that reveals localized changes in spatiotemporal temperature-pattern recurrence.

We propose a machine-learning-based technique to determine the number density of radio sources as a function of their flux density, for use in next-generation radio surveys. The method uses a convolutional neural network trained on simulations of the radio sky to predict the number of sources in several flux bins. To train the network, we adopt a supervised approach wherein we simulate training data stemming from a large domain of possible number count models going down to fluxes a factor of 100 below the threshold for source detection. We test the model reconstruction capabilities as well as benchmark the expected uncertainties in the model predictions, observing good performance for fluxes down to a factor of ten below the threshold. This work demonstrates that the capabilities of simple deep learning models for radio astronomy can be useful tools for future surveys.

We consider a dark sector containing a pair of almost degenerate states coupled to the Standard Model through a dark photon mediator. This set-up constitutes a simple realization of the inelastic dark matter scenario. The heaviest dark state is long-lived, in the limit of a small kinetic mixing among the dark photon and the Standard Model hypercharge gauge boson, and/or of a small mass splitting among the dark states. We study the prospects for detection of this scenario at proposed LHC experiments dedicated to search for long-lived particles, namely FASER, MATHUSLA, CODEX-b, AL3X, MAPP, ANUBIS and FACET. We consider both the cases of fermionic and scalar inelastic dark matter. We show that these experimental facilities can probe unexplored regions of the parameter space of this model, and we highlight their complementary roles.

Physical theories that depend on many parameters or are tested against data from many different experiments pose unique challenges to statistical inference. Many models in particle physics, astrophysics and cosmology fall into one or both of these categories. These issues are often sidestepped with statistically unsound ad hoc methods, involving intersection of parameter intervals estimated by multiple experiments, and random or grid sampling of model parameters. Whilst these methods are easy to apply, they exhibit pathologies even in low-dimensional parameter spaces, and quickly become problematic to use and interpret in higher dimensions. In this article we give clear guidance for going beyond these procedures, suggesting where possible simple methods for performing statistically sound inference, and recommendations of readily-available software tools and standards that can assist in doing so. Our aim is to provide any physicists lacking comprehensive statistical training with recommendations for reaching correct scientific conclusions, with only a modest increase in analysis burden. Our examples can be reproduced with the code publicly available at Zenodo.

The dark matter sector remains completely unknown. It is therefore crucial to keep an open mind regarding its nature and possible interactions. Focusing on the case of Weakly Interacting Massive Particles, in this work we make this general philosophy more concrete by applying modern machine learning techniques to dark matter direct detection. We do this by encoding and decoding the graphical representation of background events in the XENONnT experiment with a convolutional variational autoencoder. We describe a methodology that utilizes the `anomaly score' derived from the reconstruction loss of the convolutional variational autoencoder as well as a pre-trained standard convolutional neural network, in a semi-supervised fashion. Indeed, we observe that optimum results are obtained only when both unsupervised and supervised anomaly scores are considered together. A data set that has a higher proportion of anomaly score is deemed anomalous and deserves further investigation. Contrary to classical analyses, in principle all information about the events is used, preventing unnecessary information loss. Lastly, we demonstrate the reach of learning-focused anomaly detection in this context by comparing results with classical inference, observing that, if tuned properly, these techniques have the potential to outperform likelihood-based methods.

Juan Herrero-Garcia, ∗ Riley Patrick, † and Andre Scaffidi ‡ Departamento de F́ısica Teórica and IFIC, Universidad de Valencia-CSIC, C/ Catedrático José Beltrán, 2, E-46980 Paterna, Spain ARC Centre of Excellence for Dark Matter Particle Physics, Department of Physics, University of Adelaide, Adelaide, South Australia 5005, Australia Istituto Nazionale di Fisica Nucleare, Sezione di Torino, via P. Giuria 1, I–10125 Torino, Italy

In high energy physics, the standard convention for expressing physical quantities is natural units. The standard paradigm sets $c = \hbar = \epsilon_0 = 1$ and hence implicitly rescales all physical quantities that depend on unit derivatives of these quantities. We introduce $\texttt{NatPy}$, a simple Python module that defines user-friendly unit objects that can be used and converted within any predefined system of natural units. In this note, we will first introduce, then overview, the algebraic methods utilised by the $\texttt{NatPy}$ module.

The excess of electron recoil events seen by the XENON1T experiment has been interpreted as a potential signal of axion-like particles (ALPs), either produced in the Sun, or constituting part of the dark matter halo of the Milky Way. It has also been explained as a consequence of trace amounts of tritium in the experiment. We consider the evidence for the solar and dark-matter ALP hypotheses from the combination of XENON1T data and multiple astrophysical probes, including horizontal branch stars, red giants, and white dwarfs. We briefly address the influence of ALP decays and supernova cooling. While the different datasets are in clear tension for the case of solar ALPs, all measurements can be simultaneously accommodated for the case of a sub-dominant fraction of dark-matter ALPs. Nevertheless, this solution requires the tuning of several a priori unknown parameters, such that for our choices of priors a Bayesian analysis shows no strong preference for the ALP interpretation of the XENON1T excess over the background hypothesis.

In this paper, we explore beyond the Standard Model top-Higgs Yukawa couplings as a function of a CP-mixing parameter xi(t) at the 14 TeV HL-LHC in the process pp -> thj. We observe that angular variables of the decay products of the top are nontrivially sensitive to xi(t) . This fact is exploited in a full detector level analysis that employs machine learning techniques to optimize signal sensitivity on a suite of variables, including lepton azimuthal angle. The key result of this study is an improved projected exclusion limit on xi(t )even when including the realistic effects of detector smearing and a conservative estimate of systematic error.

We study the case of multi-component dark matter, in particular how direct detection signals are modified in the presence of several stable weakly-interacting-massive particles. Assuming a positive signal in a future direct detection experiment, stemming from two dark matter components, we study the region in parameter space where it is possible to distinguish a one from a two-component dark matter spectrum. We leave as free parameters the two dark matter masses and show that the two hypotheses can be significantly discriminated for a range of dark matter masses with their splitting being the critical factor. For full details see the first of a series of two publications [1].

We perform a global fit within the pseudo-Nambu-Goldstone Dark Matter (DM) model emerging from an additional complex scalar singlet with a softly broken global U (1) symmetry. Leading to a momentum-suppressed DM-nucleon cross section at tree level, the model provides a natural explanation for the null results from direct detection experiments. Our global fit combines constraints from perturbative unitarity, DM relic abundance, Higgs invisible decay, electroweak precision observables and latest Higgs searches at colliders. The results are presented in both frequentist and Bayesian statisical frameworks. Furthermore, post-processing our samples, we include the likelihood from gamma-ray observations of Fermi -LAT dwarf spheroidal galaxies and compute the one-loop DM-nucleon cross section. We find two favoured regions characterised by their dominant annihilation channel: the Higgs funnel and annihilation into Higgs pairs. Both are compatible with current Fermi -LAT observations, and furthermore, can fit the slight excess observed in four dwarfs in a mass range between about 30–300 GeV. While the former region is hard to probe experimentally, the latter can partly be tested by current observations of cosmic-ray antiprotons as well as future gamma-ray observations.

Recently we studied the direct detection of multi-component dark matter with arbitrary local energy densities. Although the generation of the dark matter relic abundance is model-dependent, and in principle could be only indirectly related to direct detection, it is interesting to consider the implications of the former on the latter. In this work we conduct an extended analysis to include constraints from two natural scenarios of dark matter genesis: asymmetric dark matter and thermal freeze-out. In the first (second) case, the dark matter number (energy) densities of the different components are expected to be similar. In the case of thermal freeze-out, we assume that the global energy density scales with the local one. In our numerical analysis we analyse the median sensitivity of direct detection experiments to discriminate a two-component scenario from a one-component one, and also the precision with which dark matter parameters can be extracted. We analyse these generic scenarios for both light and heavy mediators. We find that most scenarios have a relatively suppressed maximum median sensitivity compared to the previously studied general cases. We also find that the asymmetric scenario is more promising than the thermal freeze-out one.

Recently the DAMA/LIBRA collaboration released the long-waited phase-2 results with a lower energy threshold. It was recently argued that a one-component dark matter explanation of the observed annual modulation is strongly disfavoured or excluded unless isospin-violating couplings are invoked. In this short letter, we make the observation that dark matter comprising both a light and a heavy state can reproduce the observed spectrum without the need to invoke fine-tuned isospin-violating couplings. We perform a fit to scenarios with two-component dark matter and find that they are indeed in excellent agreement with the data. Our results show that two qualitatively different solutions are possible in the simplest case of isospin-conserving couplings with equal cross sections (or energy densities) of the two components. The p-values of the best-fit points of the two-component scenarios are equal, or better than, those of the one-component isospin-violating case. We also find very good agreement in the case of general two-component dark matter models.

The current paradigm for dark matter direct detection is to assume that the dark sector is solely composed of a single particle species. In this short paper, we make the observation that dark matter comprising both a light and a heavy component that modulate out of phase leads to interesting phenomenology in annual modulation experiments. For an illustrative example, we use the recently released DAMA/LIBRA phase-2 results with a lower energy threshold. Immediately after, it was argued that a one-component spin-independent dark matter explanation of the observed annual modulation is strongly disfavored or excluded unless isospin-violating couplings are invoked. We show that a simple two-component extension can reproduce the observed spectrum without the need to invoke fine-tuned couplings. Using the publicly available DAMA/LIBRA data, we perform a fit of the DAMA/LIBRA energy spectrum of the annual modulation amplitude to a scenario with two dark matter components. We also take into account how gravitational focusing affects the phases of the light and a heavy components differently, which leads to nontrivial effects in the total time-dependent rate. Our results show that there exists a unique solution in agreement with the data in the simplest case of isospin-conserving couplings with equal cross sections. The distinctive features found in this work are crucial for a dark matter interpretation of any observed annual modulation.

We study the case of multi-component dark matter, in particular how direct detection signals are modified in the presence of several stable weakly-interacting-massive particles. Assuming a positive signal in a future direct detection experiment, stemming from two dark matter components, we study the region in parameter space where it is possible to distinguish a one from a two-component dark matter spectrum. First, we leave as free parameters the two dark matter masses and show that the two hypotheses can be significantly discriminated for a range of dark matter masses with their splitting being the critical factor. We then investigate how including the effects of different interaction strengths, local densities or velocity dispersions for the two components modifies these conclusions. We also consider the case of isospin-violating couplings. In all scenarios, we show results for various types of nuclei both for elastic spin-independent and spin-dependent interactions. Finally, assuming that the two-component hypothesis is confirmed, we quantify the accuracy with which the parameters can be extracted and discuss the different degeneracies that occur. This includes studying the case in which only a single experiment observes a signal, and also the scenario of having two signals from two different experiments, in which case the ratios of the couplings to neutrons and protons may also be extracted.

Dark matter searches in gamma ray final states often make use of the fact that photons can be produced from final state muons. Modern Monte Carlo generators and dark matter codes include the effects of final state radiation from muons produced in the dark matter annihilation process itself, but neglect the O(1%) radiative correction that arises from the subsequent muon decay. After implementing this correction we demonstrate the effect that it can have on dark matter phenomenology by considering the case of dark matter annihilation to four muons via scalar mediator production. We first show that the AMS-02 positron excess can no longer easily be made consistent with this final state once the Fermi-LAT dwarf limits are calculated with the inclusion of radiative muon decays, and we next show that the Fermi-LAT galactic center gamma excess can be improved with this final state after inclusion of the same effect. We provide code and tables for the implementation of this effect in the popular dark matter code micrOMEGAs, providing a solution for any model producing final state muons.

We present LUXCalc, a new utility for calculating likelihoods and deriving weakly interacting massive particle (WIMP)-nucleon coupling limits from the recent results of the LUX direct search dark matter experiment. After a brief review of WIMP-nucleon scattering, we derive LUX limits on the spin-dependent WIMP-nucleon couplings over a broad range of WIMP masses, under standard assumptions on the relevant astrophysical parameters. We find that, under these and other common assumptions, LUX excludes the entire spin-dependent parameter space consistent with a dark matter interpretation of DAMA's anomalous signal, the first time a single experiment has been able to do so. We also revisit the case of spin-independent couplings, and demonstrate good agreement between our results and the published LUX results. Finally, we derive constraints on the parameters of an effective dark matter theory in which a spin-1 mediator interacts with a fermionic WIMP and Standard Model fermions via axial-vector couplings. A detailed appendix describes the use of LUXCalc with standard codes to place constraints on generic dark matter theories.