Status of the ANAIS Dark Matter Project at the Canfranc Underground Laboratory

The ANAIS experiment aims at the confirmation of the DAMA/LIBRA signal. A detailed analysis of two NaI(Tl) crystals of 12.5 kg each grown by Alpha Spectra will be shown: effective threshold at 1 keVee is at reach thanks to outstanding light collection and robust PMT noise filtering protocols and the measured background is well understood down to 3 keVee, having quantified K, U and Th content and cosmogenic activation in the crystals. A new detector was installed in Canfranc in March 2015 together with the two previous modules and preliminary characterization results will be presented. Finally, the status and expected sensitivity of the full experiment with 112 kg will be reviewed.Comment: Contributed to the 11th Patras Workshop on Axions, WIMPs and WISPs, Zaragoza, June 22 to 26, 201

Neutron background at the Canfranc Underground Laboratory and its contribution to the IGEX-DM dark matter experiment

A quantitative study of the neutron environment in the Canfranc Underground Laboratory has been performed. The analysis is based on a complete set of simulations and, particularly, it is focused on the IGEX-DM dark matter experiment. The simulations are compared to the IGEX-DM low energy data obtained with different shielding conditions. The results of the study allow us to conclude, with respect to the IGEX-DM background, that the main neutron population, coming from radioactivity from the surrounding rock, is practically eliminated after the implementation of a suitable neutron shielding. The remaining neutron background (muon-induced neutrons in the shielding and in the rock) is substantially below the present background level thanks to the muon veto system. In addition, the present analysis gives us a further insight on the effect of neutrons in other current and future experiments at the Canfranc Underground Laboratory. The comparison of simulations with the body of data available has allowed to set the flux of neutrons from radioactivity of the Canfranc rock, (3.82 +- 0.44) x 10^{-6} cm^{-2} s^{-1}, as well as the flux of muon-induced neutrons in the rock, (1.73 +- 0.22(stat) \+- 0.69(syst)) x 10^{-9} cm^{-2} s^{-1}, or the rate of neutron production by muons in the lead shielding, (4.8 +- 0.6 (stat) +- 1.9 (syst)) x 10^{-9} cm^{-3} s^{-1}.Comment: 17 pages, 8 figures, elsart document class; final version to appear in Astroparticle Physic

Production and relevance of cosmogenic radionuclides in NaI(Tl) crystals

The cosmogenic production of long-lived radioactive isotopes in materials is an hazard for experiments demanding ultra-low background conditions. Although NaI(Tl) scintillators have been used in this context for a long time, very few activation data were available. We present results from two 12.5 kg NaI(Tl) detectors, developed within the ANAIS project and installed at the Canfranc Underground Laboratory. The prompt data taking starting made possible a reliable quantification of production of some I, Te and Na isotopes with half-lives larger than ten days. Initial activities underground were measured and then production rates at sea level were estimated following the history of detectors; a comparison of these rates with calculations using typical cosmic neutron flux at sea level and a selected description of excitation functions was also carried out. After including the contribution from the identified cosmogenic products in the detector background model, we found that the presence of 3H in the crystal bulk would help to fit much better our background model and experimental data. We have analyzed the cosmogenic production of 3H in NaI, and although precise quantification has not been attempted, we can conclude that it could imply a very relevant contribution to the total background below 15 keV in NaI detectors.Comment: Proceedings of the Low Radioactivity Techniques 2015 workshop, March 2015, Seattle (US

Background model of NaI(Tl) detectors for the ANAIS Dark Matter Project

A thorough understanding of the background sources is mandatory in any experiment searching for rare events. The ANAIS (Annual Modulation with NaI(Tl) Scintillators) experiment aims at the confirmation of the DAMA/LIBRA signal at the Canfranc Underground Laboratory (LSC). Two NaI(Tl) crystals of 12.5 kg each produced by Alpha Spectra have been taking data since December 2012. The complete background model of these detectors and more precisely in the region of interest will be described. Preliminary background analysis of a new 12.5 kg crystal received at Canfranc in March 2015 will be presented too. Finally, the power of anticoincidence rejection in the region of interest has been analyzed in a 4x 5 12.5 kg detector matrix.Comment: Contributed to the 11th Patras Workshop on Axions, WIMPs and WISPs, Zaragoza, June 22 to 26, 201

Using Wavelets to reject background in Dark Matter experiments

A method based on wavelet techniques has been developed and applied to background rejection in the data of the IGEX dark matter experiment. The method is presented and described in some detail to show how it efficiently rejects events coming from noise and microphonism through a mathematical inspection of their recorded pulse shape. The result of the application of the method to the last data of IGEX is presented.Comment: 14 pages, 8 figures. Submitted to Astrop. Phy

Analysis of backgrounds for the ANAIS-112 dark matter experiment

The ANAIS (Annual modulation with NaI(Tl) Scintillators) experiment aims at the confirmation or refutation of theDAMA/LIBRA positive annual modulation signal in the low energy detection rate, using the same target and technique, at the Canfranc Underground Laboratory (LSC) in Spain. ANAIS-112, consisting of nine 12.5 kg NaI(Tl) modules produced by Alpha Spectra Inc. in a 3x3matrix configuration, is taking data smoothly in "dark matter search" mode since August, 2017, after a commissioning phase and operation of the first detectors during the last years in various setups. A large effort has been carried out withinANAIS to characterize the background of sodium iodide detectors, before unblinding the data and performing the first annual modulation analysis. Here, the background models developed for all the nine ANAIS-112 detectors are presented. Measured spectra from threshold to high energy in different conditions are well described by the models based on quantified activities independently estimated following several approaches. In the region from 1 to 6 keVee the measured, efficiency corrected background level is 3.58+-0.02 keV-1 kg-1 day-1; NaI crystal bulk contamination is the dominant background source being 210Pb, 40K, 22Na and 3H contributions the most relevant ones. This background level, added to the achieved 1 keVee analysis threshold (thanks to the outstanding light collection and robust filtering procedures developed), allow ANAIS-112 to be sensitive to the modulation amplitude measured by DAMA/LIBRA, and able to explore at three sigma level in 5 years the WIMP parameter region singled out by this experiment.Comment: Final version for publicatio

Algorithmic and human prediction of success in human collaboration from visual features

This is the final version. Available on open access from Nature Research via the DOI in this recordData availability: The full dataset including aggregated features of each group of the >43K groups used in our analyses is available at the following link: https://doi.org/10.7910/DVN/HDT2RN. All photos used in this work are publicly available, posted on public Facebook pages. However, we do not release the raw images, or the individual-level raw features extracted using the Face++ API. More details are provided at the link above.The publisher correction to this article is available in ORE at http://hdl.handle.net/10871/124927As groups are increasingly taking over individual experts in many tasks, it is ever more important to understand the determinants of group success. In this paper, we study the patterns of group success in Escape The Room, a physical adventure game in which a group is tasked with escaping a maze by collectively solving a series of puzzles. We investigate (1) the characteristics of successful groups, and (2) how accurately humans and machines can spot them from a group photo. The relationship between these two questions is based on the hypothesis that the characteristics of successful groups are encoded by features that can be spotted in their photo. We analyze >43K group photos (one photo per group) taken after groups have completed the game—from which all explicit performance-signaling information has been removed. First, we find that groups that are larger, older and more gender but less age diverse are significantly more likely to escape. Second, we compare humans and off-the-shelf machine learning algorithms at predicting whether a group escaped or not based on the completion photo. We find that individual guesses by humans achieve 58.3% accuracy, better than random, but worse than machines which display 71.6% accuracy. When humans are trained to guess by observing only four labeled photos, their accuracy increases to 64%. However, training humans on more labeled examples (eight or twelve) leads to a slight, but statistically insignificant improvement in accuracy (67.4%). Humans in the best training condition perform on par with two, but worse than three out of the five machine learning algorithms we evaluated. Our work illustrates the potentials and the limitations of machine learning systems in evaluating group performance and identifying success factors based on sparse visual cues