DarkSide-50 532-day dark matter search with low-radioactivity argon

Agnes, P.; Albuquerque, I. F. M.; Alexander, T.; Alton, A.; Araujo, G. R.; Ave, M.; Back, H. O.; Baldin, B.; Batignani, G.; Biery, K.; Bocci, V.; Bonfini, G.; Bonivento, W.; Bottino, B.; Budano, F.; Bussino, S.; Cadeddu, M.; Cadoni, Mariano; Calaprice, F.; Caminata, A.; Canci, N.; Candela, A.; Caravati, M.; Cariello, M.; Carlini, M.; Carpinelli, M.; Catalanotti, S.; Cataudella, V.; Cavalcante, P.; Cavuoti, S.; Chepurnov, A.; Cicalò, C.; Cocco, A. G.; Covone, G.; D’Angelo, D.; D’Incecco, M.; D’Urso, D.; Davini, S.; Candia, A. de; Cecco, S. De; Deo, M. De; Filippis, G. De; Rosa, G. De; Vincenzi, M. De; Derbin, A.; Devoto, A.; Eusanio, F. Di; Pietro, G. Di; Dionisi, C.; Downing, M.; Edkins, E.; Empl, A.; Fan, A.; Fiorillo, G.; Fitzpatrick, R. S.; Fomenko, K.; Franco, D.; Gabriele, F.; Galbiati, C.; Ghiano, C.; Giagu, S.; Giganti, C.; Giovanetti, G. K.; Gorchakov, O.E.; Goretti, A.; Granato, F.; Gromov, M.; Guan, Mengyun; Guardincerri, Y.; Gulino, M.; Hackett, B. R.; Herner, K.; Hosseini, B.; Hughes, Donald J.; Humble, P.; Hungerford, E.; Ianni, An.; Ippolito, V.; James, I.; Johnson, T.; Keeter, K. J.; Kendziora, C. L.; Kochanek, I.; Koh, G.; Korablëv, D.; Korga, G.; Kubankin, A. S.; Kuss, M.; Commara, M. La; Laí, M.; Li, X.; Lissia, M.; Longo, Giuseppe; Ma, Y.; Machado, A. A.; Machulin, I.; Mandarano, A.; Mapelli, L.; Mari, Stefano Maria; Maricic, J.; Martoff, C. J.; Messina, A.; Meyers, P. D.; Milinčić, R.; Monte, A.; Morrocchi, M.; Mount, B. J.; Muratova, V.; Musico, P.; Agasson, A. Navrer; Nozdrina, A.; Oleinik, A.; Orsini, M.; Ortica, F.; Pagani, L.; Pallavicini, M.; Pandola, L.; Pantic, E.; Paoloni, E.; Pelczar, K.; Pelliccia, N.; Pocar, A.; Pordes, S.; Poudel, S. S.; Pugachev, D. A.; Qian, Hao; Ragusa, F.; Razeti, M.; Razeto, A.; Reinhold, B.; Renshaw, A.; Rescigno, M.; Riffard, Q.; Romani, A.; Rossi, B.; Rossi, N.; Sablone, D.; Samoylov, O.; Sands, W.; Sanfilippo, Simone; Savarese, C.; Schlitzer, B.; Segreto, E.; Semenov, D.; Shchagin, A. V.; Sheshukov, A.; Singh, Prabhjot; Skorokhvatov, M.; Smirnov, O.; Sotnikov, A.; Stanford, C.; Stracka, S.; Suvorov, Y.; Tartaglia, R.; Testera, G.; Tonazzo, A.; Trinchese, P.; Unzhakov, E.; Verducci, M.; Vishneva, A.; Vogelaar, B.; Wada, M.; Waldrop, T. J.; Wang, H.; Wang, Y.; Watson, A.; Westerdale, S.; Wójcik, M.; Xiang, X.; Xiao, Xiang; Yang, Changgen; Ye, Z.; Zhu, C.; Zuzel, G.

doi:10.1103/physrevd.98.102006

Cited by 247 publications

(231 citation statements)

References 42 publications

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“…The limits at 90% CL from the PICO-60 experiment [40] in the case of spin-dependent scattering and from the XENON1T experiment [41] in the case of spin-independent scattering are also shown. Our limits are consistent with those obtained from DM direct search experiments in the same mass range [40][41][42][43]. However, we note that XENON1T and PICO-60 operate with target materials and under astrophysical time-scales different from what is used for the DM capture in the Sun.…”

Section: Resultssupporting

confidence: 88%

Search for dark matter cosmic-ray electrons and positrons from the Sun with the Fermi Large Area Telescope

et al. 2020

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We use 7 years of electron and positron Fermi-LAT data to search for a possible excess in the direction of the Sun in the energy range from 42 GeV to 2 TeV. In the absence of a positive signal we derive flux upper limits which we use to constrain two different dark matter (DM) models producing e + e − fluxes from the Sun. In the first case we consider DM model being captured by the Sun due to elastic scattering and annihilation into e + e − pairs via a long-lived light mediator that can escape the Sun. In the second case we consider instead a model where DM density is enhanced around the Sun through inelastic scattering and the DM annihilates directly into e + e − pairs. In both cases we perform an optimal analysis, searching specifically for the energy spectrum expected in each case, i.e., a box-like shaped and line-like shaped spectrum respectively. No significant signal is found and we can place limits on the spin-independent cross-section in the range from 10 −46 cm 2 to 10 −44 cm 2 and on the spin-dependent cross-section in the range from 10 −43 cm 2 to 10 −41 cm 2 . In the case of inelastic scattering the limits on the cross-section are in the range from 10 −43 cm 2 to 10 −41 cm 2 . The limits depend on the life time of the mediator (elastic case) and on the mass splitting value (inelastic case), as well as on the assumptions made for the size of the deflections of electrons and positrons in the interplanetary magnetic field. PACS numbers: 95.35.+d, 95.85.Ry

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Section: Resultssupporting

confidence: 88%

Search for dark matter cosmic-ray electrons and positrons from the Sun with the Fermi Large Area Telescope

et al. 2020

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“…For particle Dark Matter direct detection experiments, the main building block is cryogenic material single-phase time projection chamber (TPC), dual-phase TPC, and bubble chambers. Among the leading direct detection experiments, TPC is used in DarkSide-50 [1], LUX [2], PandaX-II [3] and XENON1T [4]. In this work, we focus on the output of TPC as a part of the XENON1T experiment.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural networks for direct detection of dark matter

Khosa

Mars

Richards

et al. 2020

J. Phys. G: Nucl. Part. Phys.

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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 an accuracy of 87.0% and a recall of 88.2%.

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“…(8.6) Figure 6: Constraints on the effective dark matter-proton coupling, c 2 p , from direct detection experiments xenon 1t [83] and DarkSide 50 [84].…”

Section: Portal Interactionsmentioning

confidence: 99%

“…We compare the effective coupling c p to the most stringent bounds on spin-independent dark matter-nucleon scattering: xenon 1t [83] and DarkSide 50 [84]. The results are presented in Fig.…”

Section: Portal Interactionsmentioning

confidence: 99%

Vector self-interacting dark matter

Chaffey

Tañedo

2020

Phys. Rev. D

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We present a model of vector dark matter that interacts through a low-mass vector mediator based on the Higgsing of an su(2) dark sector. The dark matter is charged under a u(1) gauge symmetry. Even though this symmetry is broken, the residual global symmetries of the theory prevent dark matter decay. We present the behavior of the model subject to the assumption that the dark matter abundance is due to thermal freeze out, including self-interaction targets for small scale structure anomalies and the possibility of interacting with the Standard Model through the vector mediator.

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DarkSide-50 532-day dark matter search with low-radioactivity argon

Cited by 247 publications

References 42 publications

Search for dark matter cosmic-ray electrons and positrons from the Sun with the Fermi Large Area Telescope

Search for dark matter cosmic-ray electrons and positrons from the Sun with the Fermi Large Area Telescope

Convolutional neural networks for direct detection of dark matter

Vector self-interacting dark matter

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