Multi-level cascaded electromagnetically induced transparency in cold atoms using an optical nanofibre interface

Ultrathin optical fibres integrated into cold atom setups are proving to be ideal building blocks for atom-photon hybrid quantum networks. Such optical nanofibres (ONF) can be used for the demonstration of nonlinear optics and quantum interference phenomena in atomic media. Here, we report on the observation of multilevel cascaded electromagnetically induced transparency (EIT) using an optical nanofibre to interface cold $^{87}$Rb atoms through the intense evanescent fields that can be achieved at ultralow probe and coupling powers. Both the probe (at 780 nm) and the coupling (at 776 nm) beams propagate through the nanofibre. The observed multipeak transparency spectra of the probe beam could offer a method for simultaneously slowing down multiple wavelengths in an optical nanofibre or for generating ONF-guided entangled beams, showing the potential of such an atom-nanofibre system for quantum information. We also demonstrate all-optical-switching in the all fibred system using the obtained EIT effect.

Learning Single Index Models in High Dimensions

Single Index Models (SIMs) are simple yet flexible semi-parametric models for classification and regression. Response variables are modeled as a nonlinear, monotonic function of a linear combination of features. Estimation in this context requires learning both the feature weights, and the nonlinear function. While methods have been described to learn SIMs in the low dimensional regime, a method that can efficiently learn SIMs in high dimensions has not been forthcoming. We propose three variants of a computationally and statistically efficient algorithm for SIM inference in high dimensions. We establish excess risk bounds for the proposed algorithms and experimentally validate the advantages that our SIM learning methods provide relative to Generalized Linear Model (GLM) and low dimensional SIM based learning methods.

Active Model Aggregation via Stochastic Mirror Descent

We consider the problem of learning convex aggregation of models, that is as good as the best convex aggregation, for the binary classification problem. Working in the stream based active learning setting, where the active learner has to make a decision on-the-fly, if it wants to query for the label of the point currently seen in the stream, we propose a stochastic-mirror descent algorithm, called SMD-AMA, with entropy regularization. We establish an excess risk bounds for the loss of the convex aggregate returned by SMD-AMA to be of the order of $Oleft(sqrt{frac{log(M)}{{T^{1-mu}}}}right)$, where $muin [0,1)$ is an algorithm dependent parameter, that trades-off the number of labels queried, and excess risk.

Sparse Linear Regression With Missing Data

This paper proposes a fast and accurate method for sparse regression in the presence of missing data. The underlying statistical model encapsulates the low-dimensional structure of the incomplete data matrix and the sparsity of the regression coefficients, and the proposed algorithm jointly learns the low-dimensional structure of the data and a linear regressor with sparse coefficients. The proposed stochastic optimization method, Sparse Linear Regression with Missing Data (SLRM), performs an alternating minimization procedure and scales well with the problem size. Large deviation inequalities shed light on the impact of the various problem-dependent parameters on the expected squared loss of the learned regressor. Extensive simulations on both synthetic and real datasets show that SLRM performs better than competing algorithms in a variety of contexts.

Toward a terahertz-driven electron gun

Femtosecond electron bunches with keV energies and eV energy spread are needed by condensed matter physicists to resolve state transitions in carbon nanotubes, molecular structures, organic salts, and charge density wave materials. These semirelativistic electron sources are not only of interest for ultrafast electron diffraction, but also for electron energy-loss spectroscopy and as a seed for x-ray FELs. Thus far, the output energy spread (hence pulse duration) of ultrafast electron guns has been limited by the achievable electric field at the surface of the emitter, which is 10 MV/m for DC guns and 200 MV/m for RF guns. A single-cycle THz electron gun provides a unique opportunity to not only achieve GV/m surface electric fields but also with relatively low THz pulse energies, since a single-cycle transform-limited waveform is the most efficient way to achieve intense electric fields. Here, electron bunches of 50 fC from a flat copper photocathode are accelerated from rest to tens of eV by a microjoule THz pulse with peak electric field of 72 MV/m at 1 kHz repetition rate. We show that scaling to the readily-available GV/m THz field regime would translate to monoenergetic electron beams of ~100 keV.

Study of Redshifted HI from the Epoch of Reionization with Drift scan

The detection of the Epoch of Reionization (EoR) in the redshifted 21-cm line is a challenging task. Here we formulate the detection of the EoR signal using the drift scan strategy. This method potentially has better instrumental stability as compared to the case where a single patch of sky is tracked. We demonstrate that the correlation time between measured visibilities could extend up to 1-2 hr for an interferometer array such as the Murchison Widefield Array (MWA), which has a wide primary beam. We estimate the EoR power based on cross-correlation of visibilities across time and show that the drift scan strategy is capable of the detection of the EoR signal with comparable/better signal-to-noise as compared to the tracking case. We also estimate the visibility correlation for a set of bright point sources and argue that the statistical inhomogeneity of bright point sources might allow their separation from the EoR signal.

Warming early Mars with CO2 and H2

The presence of valleys on ancient terrains of Mars suggest that liquid water flowed on the martian surface 3.8 billion years ago or before. The above-freezing temperatures required to explain valley formation could have been transient, in response to frequent large meteorite impacts on early Mars, or they could have been caused by long-lived greenhouse warming. Climate models that consider only the greenhouse gases carbon dioxide and water vapor have been unable to recreate warm surface conditions, given the lower solar luminosity at that time. Here we use a one-dimensional climate model to demonstrate that an atmosphere containing 1.3-4 bar of CO2 and water vapor, along with 5 to 20 percent H2, could have raised the mean surface temperature of early Mars above the freezing point of water. Vigorous volcanic outgassing from a highly reduced early martian mantle is expected to provide sufficient atmospheric H2 and CO2, the latter from the photochemical oxidation of outgassed CH4 and CO, to form a CO2-H2 greenhouse. Such a dense early martian atmosphere is consistent with independent estimates of surface pressure based on cratering data.

High contrast qubit interactions using multimode cavity QED

We introduce a new multimode cavity QED architecture for superconducting circuits which can be used to implement photonic memories, more efficient Purcell filters, and quantum simulations of photonic materials. We show that qubit interactions mediated by multimode cavities can have exponentially improved contrast for two qubit gates without sacrificing gate speed. Using two-qubits coupled via a three-mode cavity system we spectroscopically observe multimode strong couplings up to 102MHz and demonstrate suppressed interactions off-resonance of 10kHz when the qubits are ~600MHz detuned from the cavity resonance. We study Landau-Zener transitions in our multimode systems and demonstrate quasi-adiabatic loading of single photons into the multimode cavity in 25ns. We introduce an adiabatic gate protocol to realize a controlled-Z gate between the qubits in 95ns and create a Bell state with 94.7% fidelity. This corresponds to an on/off ratio (gate contrast) of 1000.

On the Markovian assumption in the excursion set approach: the approximation of Markov Velocities

The excursion set approach uses the statistics of the density field smoothed on a wide range of scales, to gain insight into a number of interesting processes in nonlinear structure formation, such as cluster assembly, merging and clustering. The approach treats the curve defined by the height of the overdensity fluctuation field when changing the smoothing scale as a random walk. The steps of the walks are often assumed to be uncorrelated, so that the walk heights are a Markov process, even though this assumption is known to be inaccurate for physically relevant filters. We develop a model in which the walk steps, rather than heights, are a Markov process, and correlations between steps arise because of nearest neighbour interactions. This model is a particular case of a general class, which we call Markov Velocity models. We show how these can approximate the walks generated by arbitrary power spectra and filters, and, unlike walks with Markov heights, provide a very good approximation to physically relevant models. We define a Markov Velocity Monte Carlo algorithm to generate walks whose first crossing distribution is very similar to that of TopHat-smoothed LCDM walks. Finally, we demonstrate that Markov Velocity walks generically exhibit a simple but realistic form of assembly bias, so we expect them to be useful in the construction of more realistic merger history trees.

C IV absorption line variability in X-ray bright BALQSOs

We report kinematic shift and strength variability of C IV broad absorption line (BAL) trough in two high-ionization X-ray bright QSOs SDSS J085551+375752 (at zem ~ 1.936) and SDSS J091127+055054 (at zem ~ 2.793). Both these QSOs have shown combination of profile shift, appearance and disappearance of absorption components belonging to a single BAL trough. The observed average kinematic shift of whole BAL profile resulted in an average deceleration of ~ -0.7 +- 0.1, -2.0 +- 0.1 cm/s^2 over a rest-frame time-span of 3.11 yr and 2.34 yr for SDSS J085551+375752 and SDSS J091127+055054, respectively. To our knowledge, these are the largest kinematic shifts exceeding by factor of about 2.8, 7.8 than the highest deceleration reported in the literature; making both of them as a potential candidate to investigate outflows using multi-wavelength monitoring for their line and continuum variability. We explore various possible mechanisms to understand the observed profile variations. Outflow models involving many small self-shielded clouds moving probably in a curved path provides the simplest explanation for the C IV BAL strength and velocity variations along with the X-ray bright nature of these sources.