85Kr and 39Ar background in GENIUS

GENIUS is a proposal for a large scale detector of rare events like double beta decay, cold dark matter and low-energy solar neutrinos in real time. The idea of GENIUS is to operate a large amount of ``naked'' Ge detectors in liquid nitrogen, with the aim of reducing the background down to a level of 10^(-3) counts/kg keV y. In this work we investigate the contribution to the background of GENIUS coming from argon (39Ar) and krypton (85Kr) contamination in the liquid nitrogen.Comment: In press, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment (2003

Signatures of the Milky Way's Dark Disk in Current and Future Experiments

In hierarchical structure formation models of disk galaxies, a dark matter disk forms as massive satellites are preferentially dragged into the disk-plane where they dissolve. Here, we quantify the importance of this dark disk for direct and indirect dark matter detection. The low velocity of the dark disk with respect to the Earth enhances detection rates in direct detection experiments at low recoil energy. For WIMP masses M_{WIMP} >~ 50 GeV, the detection rate increases by up to a factor of 3 in the 5 - 20 keV recoil energy range. Comparing this with rates at higher energy is sensitive to M_{WIMP}, providing stronger mass constraints particularly for M_{WIMP}>~100 GeV. The annual modulation signal is significantly boosted by the dark disk and the modulation phase is shifted by ~3 weeks relative to the dark halo. The variation of the observed phase with recoil energy determines M_{WIMP}, once the dark disk properties are fixed by future astronomical surveys. The low velocity of the particles in the dark disk with respect to the solar system significantly enhances the capture rate of WIMPs in the Sun, leading to an increased flux of neutrinos from the Sun which could be detected in current and future neutrino telescopes. The dark disk contribution to the muon flux from neutrino back conversion at the Earth is increased by a factor of ~5 compared to the SHM, for rho_d/rho_h=0.5.Comment: 5 pages, 7 figures, To appear in the proceedings of Identification of Dark Matter 2008 (IDM2008), Stockholm, 18-22 August 2008; corrected one referenc

Joint Learning of Sentence Embeddings for Relevance and Entailment

We consider the problem of Recognizing Textual Entailment within an Information Retrieval context, where we must simultaneously determine the relevancy as well as degree of entailment for individual pieces of evidence to determine a yes/no answer to a binary natural language question. We compare several variants of neural networks for sentence embeddings in a setting of decision-making based on evidence of varying relevance. We propose a basic model to integrate evidence for entailment, show that joint training of the sentence embeddings to model relevance and entailment is feasible even with no explicit per-evidence supervision, and show the importance of evaluating strong baselines. We also demonstrate the benefit of carrying over text comprehension model trained on an unrelated task for our small datasets. Our research is motivated primarily by a new open dataset we introduce, consisting of binary questions and news-based evidence snippets. We also apply the proposed relevance-entailment model on a similar task of ranking multiple-choice test answers, evaluating it on a preliminary dataset of school test questions as well as the standard MCTest dataset, where we improve the neural model state-of-art.Comment: repl4nlp workshop at ACL Berlin 201