|
|
NEXT Collaboration(Kekic, M. et al), Benlloch-Rodriguez, J. M., Carcel, S., Carrion, J. V., Diaz, J., Felkai, R., et al. (2021). Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment. J. High Energy Phys., 01(1), 189–22pp.
Abstract: Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in Xe-136. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a Th-228 calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses.
|
|
|
|
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.
|
|
|
|
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.
|
|
|
|
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.
|
|
|
|
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.
|
|
|
|
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%.
|
|
|
|
Melcon, A. A., Cuendis, S. A., Cogollos, C., Diaz-Morcillo, A., Dobrich, B., Gallego, J. D., et al. (2020). Scalable haloscopes for axion dark matter detection in the 30 μeV range with RADES. J. High Energy Phys., 07(7), 084–28pp.
Abstract: RADES (Relic Axion Detector Exploratory Setup) is a project with the goal of directly searching for axion dark matter above the 30 μeV scale employing custom-made microwave filters in magnetic dipole fields. Currently RADES is taking data at the LHC dipole of the CAST experiment. In the long term, the RADES cavities are envisioned to take data in the BabyIAXO magnet. In this article we report on the modelling, building and characterisation of an optimised microwave-filter design with alternating irises that exploits maximal coupling to axions while being scalable in length without suffering from mode-mixing. We develop the mathematical formalism and theoretical study which justifies the performance of the chosen design. We also point towards the applicability of this formalism to optimise the MADMAX dielectric haloscopes.
|
|
|
|
ANTARES Collaboration(Albert, A. et al), Colomer, M., Gozzini, R., Hernandez-Rey, J. J., Illuminati, G., Khan-Chowdhury, N. R., et al. (2020). Search for dark matter towards the Galactic Centre with 11 years of ANTARES data. Phys. Lett. B, 805, 135439–6pp.
Abstract: Neutrino detectors participate in the indirect search for the fundamental constituents of dark matter (DM) in form of weakly interacting massive particles (WIMPs). In WIMP scenarios, candidate DM particles can pair-annihilate into Standard Model products, yielding considerable fluxes of high-energy neutrinos. A detector like ANTARES, located in the Northern Hemisphere, is able to perform a complementary search looking towards the Galactic Centre, where a high density of dark matter is thought to accumulate. Both this directional information and the spectral features of annihilating DM pairs are entered into an unbinned likelihood method to scan the data set in search for DM-like signals in ANTARES data. Results obtained upon unblinding 3170 days of data reconstructed with updated methods are presented, which provides a larger, and more accurate, data set than a previously published result using 2101 days. A non-observation of dark matter is converted into limits on the velocity-averaged cross section for WIMP pair annihilation.
|
|
|
|
Caputo, A., Regis, M., & Taoso, M. (2020). Searching for sterile neutrino with X-ray intensity mapping. J. Cosmol. Astropart. Phys., 03(3), 001–21pp.
Abstract: The cosmological X-ray emission associated to the possible radiative decay of sterile neutrinos is composed by a collection of lines at different energies. For a given mass, each line corresponds to a given redshift. In this work, we cross correlate such line emission with catalogs of galaxies tracing the dark matter distribution at different redshifts. We derive observational prospects by correlating the X-ray sky that will be probed by the eROSITA and Athena missions with current and near future photometric and spectroscopic galaxy surveys. A relevant and unexplored fraction of the parameter space of sterile neutrinos can be probed by this technique.
|
|
|
|
NEXT Collaboration(Fernandes, A. F. M. et al), Alvarez, V., Benlloch-Rodriguez, J. M., Carcel, S., Carrion, J. V., Diaz, J., et al. (2020). Low-diffusion Xe-He gas mixtures for rare-event detection: electroluminescence yield. J. High Energy Phys., 04(4), 034–18pp.
Abstract: High pressure xenon Time Projection Chambers (TPC) based on secondary scintillation (electroluminescence) signal amplification are being proposed for rare event detection such as directional dark matter, double electron capture and double beta decay detection. The discrimination of the rare event through the topological signature of primary ionisation trails is a major asset for this type of TPC when compared to single liquid or double-phase TPCs, limited mainly by the high electron diffusion in pure xenon. Helium admixtures with xenon can be an attractive solution to reduce the electron diffu- sion significantly, improving the discrimination efficiency of these optical TPCs. We have measured the electroluminescence (EL) yield of Xe-He mixtures, in the range of 0 to 30% He and demonstrated the small impact on the EL yield of the addition of helium to pure xenon. For a typical reduced electric field of 2.5 kV/cm/bar in the EL region, the EL yield is lowered by similar to 2%, 3%, 6% and 10% for 10%, 15%, 20% and 30% of helium concentration, respectively. This decrease is less than what has been obtained from the most recent simulation framework in the literature. The impact of the addition of helium on EL statistical fluctuations is negligible, within the experimental uncertainties. The present results are an important benchmark for the simulation tools to be applied to future optical TPCs based on Xe-He mixtures.
|
|
