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  1. Abstract

    Ultra-pure NaI(Tl) crystals are the key element for a model-independent verification of the long standing DAMA result and a powerful means to search for the annual modulation signature of dark matter interactions. The SABRE collaboration has been developing cutting-edge techniques for the reduction of intrinsic backgrounds over several years. In this paper we report the first characterization of a 3.4 kg crystal, named NaI-33, performed in an underground passive shielding setup at LNGS. NaI-33 has a record low$$^{39}$$39K contamination of 4.3 ± 0.2 ppb as determined by mass spectrometry. We measured a light yield of 11.1 ± 0.2 photoelectrons/keV and an energy resolution of 13.2% (FWHM/E) at 59.5 keV. We evaluated the activities of$$^{226}$$226Ra and$$^{228}$$228Th inside the crystal to be$$5.9\pm 0.6~\upmu $$5.9±0.6μBq/kg and$$1.6\pm 0.3~\upmu $$1.6±0.3μBq/kg, respectively, which would indicate a contamination from$$^{238}$$238U and$$^{232}$$232Th at part-per-trillion level. We measured an activity of 0.51 ± 0.02 mBq/kg due to$$^{210}$$210Pb out of equilibrium and a$$\alpha $$αquenching factor of 0.63 ± 0.01 at 5304 keV. We illustrate the analyses techniques developed to reject electronic noise in the lower part of the energy spectrum. A cut-based strategy and a multivariate approach indicated a rate, attributed to the intrinsic radioactivity of the crystal, of$$\sim $$1 count/day/kg/keV in the [5–20] keV region.

  2. Neural programs are highly accurate and structured policies that perform algorith- mic tasks by controlling the behavior of a computation mechanism. Despite the potential to increase the interpretability and the compositionality of the behavior of artificial agents, it remains difficult to learn from demonstrations neural networks that represent computer programs. The main challenges that set algorithmic do- mains apart from other imitation learning domains are the need for high accuracy, the involvement of specific structures of data, and the extremely limited observabil- ity. To address these challenges, we propose to model programs as Parametrized Hierarchical Procedures (PHPs). A PHP is a sequence of conditional operations, using a program counter along with the observation to select between taking an elementary action, invoking another PHP as a sub-procedure, and returning to the caller. We develop an algorithm for training PHPs from a set of supervisor demonstrations, only some of which are annotated with the internal call structure, and apply it to efficient level-wise training of multi-level PHPs. We show in two benchmarks, NanoCraft and long-hand addition, that PHPs can learn neural pro- grams more accurately from smaller amounts of both annotated and unannotated demonstrations.
  3. An option is a short-term skill consisting of a control policy for a specified region of the state space, and a termination condition recognizing leaving that region. In prior work, we proposed an algorithm called Deep Discovery of Options (DDO) to discover options to accelerate reinforcement learning in Atari games. This paper studies an extension to robot imitation learning, called Discovery of Deep Continuous Options (DDCO), where low-level continuous control skills parametrized by deep neural networks are learned from demonstrations. We extend DDO with: (1) a hybrid categorical–continuous distribution model to parametrize high-level policies that can invoke discrete options as well continuous control actions, and (2) a cross-validation method that relaxes DDO’s requirement that users specify the number of options to be discovered. We evaluate DDCO in simulation of a 3-link robot in the vertical plane pushing a block with friction and gravity, and in two physical experiments on the da Vinci surgical robot, needle insertion where a needle is grasped and inserted into a silicone tissue phantom, and needle bin picking where needles and pins are grasped from a pile and categorized into bins. In the 3-link arm simulation, results suggest that DDCO can take 3x fewer demonstrations tomore »achieve the same reward compared to a baseline imitation learning approach. In the needle insertion task, DDCO was successful 8/10 times compared to the next most accurate imitation learning baseline 6/10. In the surgical bin picking task, the learned policy successfully grasps a single object in 66 out of 99 attempted grasps, and in all but one case successfully recovered from failed grasps by retrying a second time.« less
  4. Abstract SABRE is a dark matter direct detection experiment aiming to measure the annual modulation of the dark matter interaction rate in NaI(Tl) crystals. SABRE focuses on the achievement of an ultra-low background rate operating high-purity NaI(Tl) crystals in a liquid scintillator veto for active background rejection. Moreover, twin experiments will be located in both Northern and Southern hemispheres (Italy and Australia) to disentangle any possible contribution from seasonal or site-related effects. In this article the results of the first measurements with a NaI(Tl) crystal for the SABRE experiment performed at LNGS are presented.
  5. Abstract SABRE is a dark matter direct detection experiment based on NaI(Tl) scintillating crystals. The primary goal of the experiment is to test the dark matter interpretation of the DAMA/LIBRA annual modulation signal. To reach its purpose, SABRE will operate an array of ultra-low background NaI(Tl) crystals within an active veto, based on liquid scintillator. Finally two twin detectors will be used, one in the northern hemisphere at Laboratori Nazionali del Gran Sasso, Italy (LNGS) and the other, first of its kind, in the southern hemisphere, in the Stawell Underground Physic Laboratory (SUPL). The collaboration has successfully developed a NaI(Tl) crystal with the impressive potassium content of about 4 ppb, according to the mass spectroscopy measurements. A value that, if confirmed, would be about 3 times lower than the DAMA/LIBRA crystals one. The first phase of the SABRE experiment, called SABRE Proof of Principle (PoP), aims to prove the achieved radiopurity by direct measurement of crystals at LNGS. This work reports the status of the PoP setup and the recent progresses on the development of low radioactivity NaI(Tl) crystals.
  6. The SABRE (Sodium-iodide with Active Background REjection) experiment is a new detector based on NaI(Tl) scintillating crystals for the dark matter detection through the annual modulation. With ultra-pure crystals and an active veto system, based on liquid scintillator surrounding the crystal array, SABRE will reach unprecedented low background and the highest sensitivity among the present NaI(Tl) experiments. Moreover SABRE will be the first dark matter search with twin detectors located in the North and South hemispheres, in Gran Sasso National Laboratories (LNGS), Italy, and Stawell Underground Laboratories (SUPL), Australia, respectively. The double location will help to quantify possible seasonal effects, and is a unique feature to identify a modulation of dark matter origins. SABRE is presently in the Proof-of-Principle (PoP) phase, with the goal to measure the crystal intrinsic and cosmogenic backgrounds of one 5 kg crystal and the active veto efficiency. We have performed a full geometry Monte Carlo simulation in order to evaluate the background contributions in the two distinct operation modes foreseen for the PoP: the potassium Measurement Mode (KMM) and the Dark Matter Measurement Mode (DMM), where the liquid scintillator detector is used in coincidence or anti-coincidence with the crystal, respectively. This paper presents the resultsmore »of a detailed background simulation and the expected sensitivity for the SABRE full scale experiment.« less