Khosa, C. K., Mars, L., Richards, J., & Sanz, V. (2020). Convolutional neural networks for direct detection of dark matter. J. Phys. G, 47(9), 095201–20pp.
Abstract: The XENON1T experiment uses a time projection chamber (TPC) with liquid xenon to search for weakly interacting massive particles (WIMPs), a proposed dark matter particle, via direct detection. As this experiment relies on capturing rare events, the focus is on achieving a high recall of WIMP events. Hence the ability to distinguish between WIMP and the background is extremely important. To accomplish this, we suggest using convolutional neural networks (CNNs); a machine learning procedure mainly used in image recognition tasks. To explore this technique we use XENON collaboration open-source software to simulate the TPC graphical output of dark matter signals and main backgrounds. A CNN turns out to be a suitable tool for this purpose, as it can identify features in the images that differentiate the two types of events without the need to manipulate or remove data in order to focus on a particular region of the detector. We find that the CNN can distinguish between the dominant background events (ER) and 500 GeV WIMP events with a recall of 93.4%, precision of 81.2% and an accuracy of 87.2%.
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Centelles Chulia, S., Cepedello, R., Peinado, E., & Srivastava, R. (2020). Scotogenic dark symmetry as a residual subgroup of Standard Model symmetries. Chin. Phys. C, 44(8), 083110–7pp.
Abstract: We demonstrate that a scotogenic dark symmetry can be obtained as a residual subgroup of the global U(1)(B-L) symmetry already present in the Standard Model. In addition, we propose a general framework in which the U(1)(B-L) symmetry is spontaneously broken into an even Z(2n) subgroup, setting the general conditions for neutrinos to be Majorana and for dark matter stability to exist in terms of the residual Z(2n). As an example, under this general framework, we build a class of simple models where, in a scotogenic manner, the dark matter candidate is the lightest particle running inside the mass loop of a neutrino. The global U(1)(B-L) symmetry in our framework, being anomaly free, can also be gauged in a straightforward manner leading to a richer phenomenology.
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Loya Villalpando, A. A., Martin-Albo, J., Chen, W. T., Guenette, R., Lego, C., Park, J. S., et al. (2020). Improving the light collection efficiency of silicon photomultipliers through the use of metalenses. J. Instrum., 15(11), P11021–13pp.
Abstract: Metalenses are optical devices that implement nanostructures as phase shifters to focus incident light. Their compactness and simple fabrication make them a potential cost-effective solution for increasing light collection efficiency in particle detectors with limited photosensitive area coverage. Here we report on the characterization and performance of metalenses in increasing the light collection efficiency of silicon photomultipliers (SiPM) of various sizes using an LED of 630 nm, and find a six to seven-fold increase in signal for a 1.3 x 1 3 mm(2) SiPM when coupled with a 10-mm-diameter metalens manufactured using deep ultraviolet stepper lithography. Such improvements could be valuable for future generations of particle detectors, particularly those employed in rare-event searches such as dark matter and neutrinoless double beta decay.
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Di Valentino, E., Melchiorri, A., Mena, O., & Vagnozzi, S. (2020). Interacting dark energy in the early 2020s: A promising solution to the H-0 and cosmic shear tensions. Phys. Dark Universe, 30, 100666–12pp.
Abstract: We examine interactions between dark matter and dark energy in light of the latest cosmological observations, focusing on a specific model with coupling proportional to the dark energy density. Our data includes Cosmic Microwave Background (CMB) measurements from the Planck 2018 legacy data release, late-time measurements of the expansion history from Baryon Acoustic Oscillations (BAO) and Supernovae Type Ia (SNeIa), galaxy clustering and cosmic shear measurements from the Dark Energy Survey Year 1 results, and the 2019 local distance ladder measurement of the Hubble constant H-0 from the Hubble Space Telescope. Considering Planck data both in combination with BAO or SNeIa data reduces the H-0 tension to a level which could possibly be compatible with a statistical fluctuation. The very same model also significantly reduces the Omega(m) – sigma(8) tension between CMB and cosmic shear measurements. Interactions between the dark sectors of our Universe remain therefore a promising joint solution to these persisting cosmological tensions.
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Villanueva-Domingo, P., & Villaescusa-Navarro, F. (2021). Removing Astrophysics in 21 cm Maps with Neural Networks. Astrophys. J., 907(1), 44–14pp.
Abstract: Measuring temperature fluctuations in the 21 cm signal from the epoch of reionization and the cosmic dawn is one of the most promising ways to study the universe at high redshifts. Unfortunately, the 21 cm signal is affected by both cosmology and astrophysics processes in a nontrivial manner. We run a suite of 1000 numerical simulations with different values of the main astrophysical parameters. From these simulations we produce tens of thousands of 21 cm maps at redshifts 10 <= z <= 20. We train a convolutional neural network to remove the effects of astrophysics from the 21 cm maps and output maps of the underlying matter field. We show that our model is able to generate 2D matter fields not only that resemble the true ones visually but whose statistical properties agree with the true ones within a few percent down to scales 2 Mpc(-1). We demonstrate that our neural network retains astrophysical information that can be used to constrain the value of the astrophysical parameters. Finally, we use saliency maps to try to understand which features of the 21 cm maps the network is using in order to determine the value of the astrophysical parameters.
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