Search for the Sagittarius Tidal Stream of Axion Dark Matter around 4.55 μ\mueV

We report the first search for the Sagittarius tidal stream of axion dark matter around 4.55 $\mu$eV using CAPP-12TB haloscope data acquired in March of 2022. Our result excluded the Sagittarius tidal stream of Dine-Fischler-Srednicki-Zhitnitskii and Kim-Shifman-Vainshtein-Zakharov axion dark matter densities of $\rho_a\gtrsim0.184$ and $\gtrsim0.025$ GeV/cm$^{3}$, respectively, over a mass range from 4.51 to 4.59 $\mu$eV at a 90% confidence level.Comment: 6 pages, 7 Figures, PRD Letter accepte

Extensive search for axion dark matter over 1\,GHz with CAPP's Main Axion eXperiment

We report an extensive high-sensitivity search for axion dark matter above 1\,GHz at the Center for Axion and Precision Physics Research (CAPP). The cavity resonant search, exploiting the coupling between axions and photons, explored the frequency (mass) range of 1.025\,GHz (4.24\,$\mu$eV) to 1.185\,GHz (4.91\,$\mu$eV). We have introduced a number of innovations in this field, demonstrating the practical approach of optimizing all the relevant parameters of axion haloscopes, extending presently available technology. The CAPP 12\,T magnet with an aperture of 320\,mm made of Nb$_3$Sn and NbTi superconductors surrounding a 37-liter ultralight-weight copper cavity is expected to convert DFSZ axions into approximately $10^2$ microwave photons per second. A powerful dilution refrigerator, capable of keeping the core system below 40\,mK, combined with quantum-noise limited readout electronics, achieved a total system noise of about 200\,mK or below, which corresponds to a background of roughly $4\times 10^3$ photons per second within the axion bandwidth. The combination of all those improvements provides unprecedented search performance, imposing the most stringent exclusion limits on axion--photon coupling in this frequency range to date. These results also suggest an experimental capability suitable for highly-sensitive searches for axion dark matter above 1\,GHz.Comment: A detailed axion dark matter article with 27 pages, 22 figure

Multivariate Bayesian decoding of single-trial event-related fMRI responses for memory retrieval of voluntary actions.

This study proposes a method for classifying event-related fMRI responses in a specialized setting of many known but few unknown stimuli presented in a rapid event-related design. Compared to block design fMRI signals, classification of the response to a single or a few stimulus trial(s) is not a trivial problem due to contamination by preceding events as well as the low signal-to-noise ratio. To overcome such problems, we proposed a single trial-based classification method of rapid event-related fMRI signals utilizing sparse multivariate Bayesian decoding of spatio-temporal fMRI responses. We applied the proposed method to classification of memory retrieval processes for two different classes of episodic memories: a voluntarily conducted experience and a passive experience induced by watching a video of others' actions. A cross-validation showed higher classification performance of the proposed method compared to that of a support vector machine or of a classifier based on the general linear model. Evaluation of classification performances for one, two, and three stimuli from the same class and a correlation analysis between classification accuracy and target stimulus positions among trials suggest that presenting two target stimuli at longer inter-stimulus intervals is optimal in the design of classification experiments to identify the target stimuli. The proposed method for decoding subject-specific memory retrieval of voluntary behavior using fMRI would be useful in forensic applications in a natural environment, where many known trials can be extracted from a simulation of everyday tasks and few target stimuli from a crime scene

Comparison of initial feature masks for decoding.

<p>(a) The initial feature masks from statistical parametric maps generated from all trials (total 80 trials, colored in red and yellow) and all trials except for a test trial (total 79 trials, colored in green and yellow) of a participant were compared. The initial feature masks show 95% overlaps (in yellow color). (b) The initial feature masks derived from the statistical parametric maps of union and difference between voluntary experience and passive experience (VE & PE and VE − PE) in an individual participant are displayed. Red-yellow color indicates increased activation in memory retrieval of a voluntary experience than in a passive experience, while reverse for blue colors.</p

Exemplary display of distributed sparse feature maps used to decode voluntary and passive visual stimuli in two participants.

<p>The feature maps in (a) and (b) were generated based on the voxel weights from two MVB models. In these examples, model parameters (voxel weights) estimated by assuming the unknown target stimuli as either voluntary experience class (VE→VE) or passive experience class (VE→PE) are displayed, with red colors for positive weights and blue colors for negative weights. The size of spheres indicates the strength of weights. The histograms of the voxel weights (features) show small non-zero values showing sparsity.</p

Illustration of the one-model and two-models MVB approaches.

<p>(a) To classify an unknown stimulus, the one-model method builds a MVB model with a regressor of known stimuli by ignoring the unknown targets. In this model, multivariate Bayesian parameter estimation (MVBPE) estimates model parameters, β. The weighted sum of fMRI voxel time series (weighted by the model parameters, β) was correlated with a regressor of A class (X_A) and a regressor of B class (X_B), then the regressor with higher correlation was chosen for the target class label. (b) Two-models approach builds two types of MVB models with two regressors with A and B classes for the target stimulus. The model with higher free-energy (estimate of log-evidence) was chosen for the target class label. This model utilizes the free-energy estimated by MVBPE instead of model parameters, β. (c) The T-value weighted sum of fMRI time series (GLM analysis) was correlated with two regressors (X_A, X_B). A class with higher correlation coefficient was chosen as the class for the target. (d) The class was assigned to class A when the dot-product of weights of SVM classifier (trained with single trial regression coefficients in the GLM analysis) and the regression coefficients (β) for each single stimulus was higher than zero, and otherwise class B.</p

Effects of interstimulus interval on the free energy approximation.

<p>(a) The Free energy approximation had a significant positive correlation with interstimulus intervals between onset times of present and next stimulus (POST-ISI) (r = 0.2277, p = 0.0423). (b) There was a tendency toward positive correlation between the Free energy approximation and the total interstimulus interval (TOTAL-ISI) (r = 0.2169, p = 0.0532).</p

An illustration for overlapping effects of hemodynamic responses on the spatial patterns.

<p>(a) The illustration is based on the rapid event related design with three classes of events, with short intervals between events. (b) Each stimulus event elicits a class-specific hemodynamic response. (c) Due to a long hemodynamic response for a neural event, overlapped hemodynamic responses of preceding events are generally observed at each time point in the rapid event-related design. (d, e) The intrinsic neural responses can construct class-specific spatial patterns for each event, (f) whereas the overlapped responses contaminated event-specific spatial patterns.</p

Classification accuracy of the sparse MVB compared to other classification methods.

<p>(a) In the MVB model-based classification for single trial, the two-models approach showed significantly higher accuracy than the single-model approach (p < 0.001). (b) Comparison results of MVB model-based classification performance compared to GLM and SVM for single, two and three trials are displayed. The classification accuracy of all the methods are statistically higher than the chance level of 0.5 after one sample t-tests (p < 0.05). The proposed MVB (VE & PE) method with a feature mask containing both voluntary experience (VE) and passive experience (PE) showed greater classification accuracy for single and multiple trials than the classification method based on GLM (T-weighted), SVM (the parameter C = 1 over the feature mask VE & PE), and MVB (VE—PE, over the feature mask in the contrast between voluntary experience versus passive experience).</p

Detecting brain states using multivariate Bayesian inversion scheme.

<p>(a) A general overview for decoding. (b) An example of application in a rapid event related design to models with different design matrices X_A (assigning 1 for the regressor A and 0 for the regressor B at the target stimulus) and X_B (assigning 0 for the regressor A and 1 for the regressor B at the target stimulus) assuming the unknown target class as class A and class B, respectively. The class for the unknown stimulus was chosen by selecting the model with higher free-energy F among models with different design matrices X_A and X_B.</p