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The second "Mineral Detection of Neutrinos and Dark Matter" (MDvDM'24) meeting was held January 8-11, 2024 in Arlington, VA, USA, hosted by Virginia Tech's Center for Neutrino Physics. This document collects contributions from this workshop, providing an overview of activities in the field. MDvDM'24 was the second topical workshop dedicated to the emerging field of mineral detection of neutrinos and dark matter, following a meeting hosted by IFPU in Trieste, Italy in October 2022. Mineral detectors have been proposed for a wide variety of applications, including searching for dark matter, measuring various fluxes of astrophysical neutrinos over gigayear timescales, monitoring nuclear reactors, and nuclear disarmament protocols; both as paleo-detectors using natural minerals that could have recorded the traces of nuclear recoils for timescales as long as a billion years and as detectors recording nuclear recoil events on laboratory timescales using natural or artificial minerals. Contributions to this proceedings discuss the vast physics potential, the progress in experimental studies, and the numerous challenges lying ahead on the path towards mineral detection. These include a better understanding of the formation and annealing of recoil defects in crystals; identifying the best classes of minerals and, for paleo-detectors, understanding their geology; modeling and control of the relevant backgrounds; developing, combining, and scaling up imaging and data analysis techniques; and many others. During the last years, MDvDM has grown rapidly and gained attention. Small-scale experimental efforts focused on establishing various microscopic readout techniques are underway at institutions in North America, Europe and Asia. We are looking ahead to an exciting future full of challenges to overcome, surprises to be encountered, and discoveries lying ahead of us., Comment: Summary and proceedings of the MDvDM'24 conference, Jan 8-11 2024

Traditionally for improving the segmentation performance of models, most approaches prefer to use adding more complex modules. And this is not suitable for the medical field, especially for mobile medical devices, where computationally loaded models are not suitable for real clinical environments due to computational resource constraints. Recently, state-space models (SSMs), represented by Mamba, have become a strong competitor to traditional CNNs and Transformers. In this paper, we deeply explore the key elements of parameter influence in Mamba and propose an UltraLight Vision Mamba UNet (UltraLight VM-UNet) based on this. Specifically, we propose a method for processing features in parallel Vision Mamba, named PVM Layer, which achieves excellent performance with the lowest computational load while keeping the overall number of processing channels constant. We conducted comparisons and ablation experiments with several state-of-the-art lightweight models on three skin lesion public datasets and demonstrated that the UltraLight VM-UNet exhibits the same strong performance competitiveness with parameters of only 0.049M and GFLOPs of 0.060. In addition, this study deeply explores the key elements of parameter influence in Mamba, which will lay a theoretical foundation for Mamba to possibly become a new mainstream module for lightweighting in the future. The code is available from https://github.com/wurenkai/UltraLight-VM-UNet .

In the field of medical image segmentation, variant models based on Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs) as the base modules have been very widely developed and applied. However, CNNs are often limited in their ability to deal with long sequences of information, while the low sensitivity of ViTs to local feature information and the problem of secondary computational complexity limit their development. Recently, the emergence of state-space models (SSMs), especially 2D-selective-scan (SS2D), has had an impact on the longtime dominance of traditional CNNs and ViTs as the foundational modules of visual neural networks. In this paper, we extend the adaptability of SS2D by proposing a High-order Vision Mamba UNet (H-vmunet) for medical image segmentation. Among them, the proposed High-order 2D-selective-scan (H-SS2D) progressively reduces the introduction of redundant information during SS2D operations through higher-order interactions. In addition, the proposed Local-SS2D module improves the learning ability of local features of SS2D at each order of interaction. We conducted comparison and ablation experiments on three publicly available medical image datasets (ISIC2017, Spleen, and CVC-ClinicDB), and the results all demonstrate the strong competitiveness of H-vmunet in medical image segmentation tasks. The code is available from https://github.com/wurenkai/H-vmunet .

In this paper we calculate the fully differential cross sections for inclusive heavy quark production in deep-inelastic scattering. We construct proper projection operators to give all possible azimuthal angle distributions of the heavy quark for unpolarized and longitudinally polarized scatterings. These projection operators are expressed in terms of momenta of incoming hadron, virtual photon and detected heavy quark. The azimuthal angle distributions are calculated to next-to-leading order of $\alpha_s$, i.e., $O(\alpha_s^2)$, in a unified way. Analytic expressions of the hard coefficients are given. Numerical results on future electron-ion colliders are also given. It is found that at least three azimuthal angle asymmetries can be more than $1\%$ in typical kinematical regions of these colliders., Comment: Numerical results are updated by using consistent PDF sets. Conclusions are not changed. Presentations are improved to match the published version

The horizon of a classical black hole (BH), functioning as a one-way membrane, plays a vital role in the dynamic evolution of binary BHs, capable of absorbing fluxes entirely. Tidal heating, stemming from this phenomenon, exerts a notable influence on the production of gravitational waves(GWs). If at least one member of a binary is an exotic compact object (ECO) instead of a BH, the absorption of fluxes is expected to be incomplete and the tidal heating would be different. Thus, tidal heating can be utilized for model-independent investigations into the nature of compact object. In this paper, assuming that the extreme-mass-ratio inspiral (EMRI) contains a stellar-mass compact object orbiting around a massive ECO with a reflective surface, we compute the GWs from the generic EMRI orbits. Using the accurate and analytic flux formulas in the black hole spacetime, we adapted these formulas in the vicinity of the ECO surface by incorporating a reflectivity parameter. Under the adiabatic approximation, we can evolve the orbital parameters and compute the EMRI waveforms. The effect of tidal heating for the spinning and non-spinning objects can be used to constrain the reflectivity of the surface at the level of 10^-6 by computing the mismatch and fisher information matrix., Comment: 10 pages, 5 figures, 1 table

Space-based gravitational wave detectors like TianQin or LISA could observe extreme-mass-ratio-inspirals (EMRIs) at millihertz frequencies. The accurate identification of these EMRI signals from the data plays a crucial role in enabling in-depth study of astronomy and physics. We aim at the identification stage of the data analysis, with the aim to extract key features of the signal from the data, such as the evolution of the orbital frequency, as well as to pinpoint the parameter range that can fit the data well for the subsequent parameter inference stage. In this manuscript, we demonstrated the identification of EMRI signals without any additional prior information on physical parameters. High-precision measurements of EMRI signals have been achieved, using a hierarchical search. It combines the search for physical parameters that guide the subsequent parameter inference, and a semi-coherent search with phenomenological waveforms that reaches precision levels down to $10^{-4}$ for the phenomenological waveform parameters $\omega_{0}$, $\dot{\omega}_{0}$, and $\ddot{\omega}_{0}$. As a result, we obtain measurement relative errors of less than 4% for the mass of the massive black hole, while keeping the relative errors of the other parameters within as small as 0.5%., Comment: 16 pages, 7 figures, comments welcom

Space-based gravitational wave detectors such as TianQin, LISA, and TaiJi have the potential to outperform themselves through joint observation. To achieve this, it is desirable to practice joint data analysis in advance on simulated data that encodes the intrinsic correlation among the signals found in different detectors that operate simultaneously. In this paper, we introduce \texttt{GWSpace}, a package that can simulate the joint detection data from TianQin, LISA, and TaiJi. The software is not a groundbreaking work that starts from scratch. Rather, we use as many open-source resources as possible, tailoring them to the needs of simulating the multi-mission science data and putting everything into a ready-to-go and easy-to-use package. We shall describe the main components, the construction, and a few examples of application of the package. A common coordinate system, namely the Solar System Barycenter (SSB) coordinate system, is utilized to calculate spacecraft orbits for all three missions. The paper also provides a brief derivation of the detection process and outlines the general waveform of sources detectable by these detectors., Comment: 24 pages, 13 figures, GWSpace will be uploaded at https://github.com/TianQinSYSU/GWSpace

TianQin and LISA are space-based laser interferometer gravitational wave (GW) detectors planned to be launched in the mid-2030s. Both detectors will detect low-frequency GWs around $10^{-2}\,{\rm Hz}$, however, TianQin is more sensitive to frequencies above this common sweet-spot while LISA is more sensitive to frequencies below $10^{-2}\,{\rm Hz}$. Therefore, TianQin and LISA will be able to detect the same sources but with different accuracy depending on the source and its parameters. We consider some of the most important astrophysical sources -- massive black hole binaries, stellar-mass black hole binaries, double white dwarfs, extreme mass ratio inspirals, light and heavy intermediate mass ratio inspirals, as well as the stochastic gravitational background of astrophysical origin -- that TianQin and LISA will be able to detect. For each of these sources, we analyze how far they can be detected (detection distance) and how well their parameters can be measured (detection accuracy) using a Fisher Matrix analysis. We compare the results obtained by the three detection scenarios (TianQin alone, LISA alone, and joint detection by LISA and TianQin) highlighting the gains from joint detection as well as the contribution of TianQin and LISA to a combined study of astrophysical sources. In particular, we consider the different orientations, lifetimes, and duty cycles of the two detectors to explore how they can give a more complete picture when working together., Comment: 49 pages, 63 figures

Recently discovered regular X-ray bursts known as quasi-periodic eruptions have a proposed model that suggests a tidal stripping white dwarf inspiralling into the galaxy's central black hole on an eccentric orbit. According to this model, the interaction of the stripping white dwarf with the central black hole would emit gravitational wave signals as well, their detection can help explore the formation mechanism of quasi-periodic eruptions and facilitate multi-messenger observations. In this paper, we aim to perform a preliminary study of the gravitation wave observation of TianQin on this stripping white dwarf model. We investigated the horizon distance of TianQin on this type of gravitation wave signal and found it can be set to 200Mpc. We also find that those stripping white dwarf model sources with central black hole mass within $10^4\sim10^{5.5}M_\odot$ are more likely to be detected by TianQin. We assessed the parameter estimation precision of TianQin on those stripping white dwarf model sources. Our result shows that, even in the worst case, TianQin can determine the central black hole mass, the white dwarf mass, the central black hole spin, and the orbital initial eccentricity with a precision of $10^{-2}$. In the optimistic case, TianQin can determine the central black hole mass and the white dwarf mass with a precision of $10^{-7}$, determine the central black hole spin with a precision of $10^{-5}$, and determine the orbital initial eccentricity with a precision of $10^{-8}$. Moreover, TianQin can determine the luminosity distance with a precision of $10^{-1}$ and determine the sky localization with a precision of $10^{-2}\sim10$ $\rm deg^2$., Comment: 9 pages, 4 figures

Kernel Regularized Least Squares (KRLS) is a popular method for flexibly estimating models that may have complex relationships between variables. However, its usefulness to many researchers is limited for two reasons. First, existing approaches are inflexible and do not allow KRLS to be combined with theoretically-motivated extensions such as random effects, unregularized fixed effects, or non-Gaussian outcomes. Second, estimation is extremely computationally intensive for even modestly sized datasets. Our paper addresses both concerns by introducing generalized KRLS (gKRLS). We note that KRLS can be re-formulated as a hierarchical model thereby allowing easy inference and modular model construction where KRLS can be used alongside random effects, splines, and unregularized fixed effects. Computationally, we also implement random sketching to dramatically accelerate estimation while incurring a limited penalty in estimation quality. We demonstrate that gKRLS can be fit on datasets with tens of thousands of observations in under one minute. Further, state-of-the-art techniques that require fitting the model over a dozen times (e.g. meta-learners) can be estimated quickly., Comment: Accepted version available at DOI below; corrected small typos