Applying the matched-filter technique to the search for dark matter transients with networks of quantum sensors

Panelli, Guglielmo; Roberts, B. M.; Derevianko, Andrei

doi:10.1140/epjqt/s40507-020-00081-9

Cited by 8 publications

(10 citation statements)

References 45 publications

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“…[42]; see Refs. [33,42] for a description of the GPS clock data relevant to the analysis. The calculated skewness in the clock-comparison residuals is…”

Section: Discussionmentioning

confidence: 99%

“…A general challenge with searching for transient signals is that they are difficult to distinguish from conventional noise. One approach [11,31] is to use a network of devices, and search for the correlated propagation of transients that sweep through the network at galactic velocities, v g ∼ 300 km/s (see also [24,[32][33][34][35][36]). However, objects of spatial extent smaller than the network node separation would not produce such a signature.…”

Section: Introductionmentioning

confidence: 99%

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Precision Measurement Noise Asymmetry and Its Annual Modulation as a Dark Matter Signature

Roberts

Derevianko

2021

Universe

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Dark matter may be composed of self-interacting ultralight quantum fields that form macroscopic objects. An example of which includes Q-balls, compact non-topological solitons predicted by a range of theories that are viable dark matter candidates. As the Earth moves through the galaxy, interactions with such objects may leave transient perturbations in terrestrial experiments. Here we propose a new dark matter signature: an asymmetry (and other non-Gaussianities) that may thereby be induced in the noise distributions of precision quantum sensors, such as atomic clocks, magnetometers, and interferometers. Further, we demonstrate that there would be a sizeable annual modulation in these signatures due to the annual variation of the Earth velocity with respect to dark matter halo. As an illustration of our formalism, we apply our method to 6 years of data from the atomic clocks on board GPS satellites and place constraints on couplings for macroscopic dark matter objects with radii R<104km, the region that is otherwise inaccessible using relatively sparse global networks.

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“…[42]; see Refs. [33,42] for a description of the GPS clock data relevant to the analysis. The calculated skewness in the clock-comparison residuals is…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Precision Measurement Noise Asymmetry and Its Annual Modulation as a Dark Matter Signature

Roberts

Derevianko

2021

Universe

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“…e proposed quantum wavelet transform filtering in this article is an innovative part for mammographic noise reduction. QWT stands for matched filter named quantum wavelet transform technique from [33] and also quantum image filtering in the frequency domain in [32,34], which is based on fast Fourier transform (FFT). It should be noted that the initial value of threshold is experimental, which is defined by trial and error.…”

Section: Preprocessing Phasementioning

confidence: 99%

Breast Sentinel Lymph Node Cancer Detection from Mammographic Images Based on Quantum Wavelet Transform and an Atrous Pyramid Convolutional Neural Network

Qasim

Mohammed

Bayat

2022

Scientific Programming

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This study proposes an optimal approach to reduce noise in mammographic images and to identify salt-and-pepper, Gaussian, Poisson, and impact noises to determine the exact mass detection operation after these noise reductions. It therefore offers a method for noise reduction operations called quantum wavelet transform filtering and a method for precision mass segmentation called the image morphological operations in mammographic images based on the classification with an atrous pyramid convolutional neural network (APCNN) as a deep learning model. The hybrid approach called a QWT-APCNN is evaluated in terms of criteria compared with previous methods such as peak signal-to-noise ratio (PSNR) and mean-squared error (MSE) in noise reduction and accuracy of detection for mass area recognition. The proposed method presents more performance of noise reduction and segmentation in comparison with state-of-the-art methods. In this paper, we used the APCNN based on the convolutional neural network (CNN) as a new deep learning method, which is able to extract features and perform classification simultaneously, but it is intended as far as possible, empirically for the purpose of this research to be able to determine breast cancer and then identify the exact area of the masses and then classify them according to benign, malignant, and suspicious classes. The obtained results presented that the proposed approach has better performance than others based on some evaluation criteria such as accuracy with 98.57%, sensitivity with 90%, specificity with 85%, and also ROC and AUC with a rate of 86.77.

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“…There are several criteria [25] for a network to detect the signal pattern due to a macroscopic DM object sweeping through a network of N s sensors:…”

Section: Overview Of Existing Networkmentioning

confidence: 99%

Global Quantum Sensor Networks as Probes of the Dark Sector

Derevianko

Pustelny

2022

The Search for Ultralight Bosonic Dark Matter

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Most dark matter searches to date employ a single sensor for detection. In this chapter, we explore the power of distributed networks in dark matter searches. Compared to a single sensor, networks offer several advantages, such as the ability to probe spatiotemporal signatures of the putative signal and, as a result, an improved rejection of false positives, better sensitivity, and improved confidence in the dark matter origin of the sought-after signal. We illustrate our general discussion with two examples: (1) the Global Network of Optical Magnetometers for Exotic physics searches (GNOME) and (2) the constellation of atomic clocks on board satellites of the Global Positioning System (GPS).

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Applying the matched-filter technique to the search for dark matter transients with networks of quantum sensors

Cited by 8 publications

References 45 publications

Precision Measurement Noise Asymmetry and Its Annual Modulation as a Dark Matter Signature

Precision Measurement Noise Asymmetry and Its Annual Modulation as a Dark Matter Signature

Breast Sentinel Lymph Node Cancer Detection from Mammographic Images Based on Quantum Wavelet Transform and an Atrous Pyramid Convolutional Neural Network

Global Quantum Sensor Networks as Probes of the Dark Sector

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