Statistical analysis of thermospheric gravity waves from Fabry-Perot Interferometer measurements of atomic oxygen

Data from the Fabry-Perot Interferometers at KEOPS (Sweden), Sodankylä (Finland), and Svalbard (Norway), have been analysed for gravity wave activity on all the clear nights from 2000 to 2006. A total of 249 nights were available from KEOPS, 133 from Sodankylä and 185 from the Svalbard FPI. A Lomb-Scargle analysis was performed on each of these nights to identify the periods of any wave activity during the night. Comparisons between many nights of data allow the general characteristics of the waves that are present in the high latitude upper thermosphere to be determined. Comparisons were made between the different parameters: the atomic oxygen intensities, the thermospheric winds and temperatures, and for each parameter the distribution of frequencies of the waves was determined. No dependence on the number of waves on geomagnetic activity levels, or position in the solar cycle, was found. All the FPIs have had different detectors at various times, producing different time resolutions of the data, so comparisons between the different years, and between data from different sites, showed how the time resolution determines which waves are observed. In addition to the cutoff due to the Nyquist frequency, poor resolution observations significantly reduce the number of short-period waves (5 h) detected. Comparisons between the number of gravity waves detected at KEOPS and Sodankylä over all the seasons showed a similar proportion of waves to the number of nights used for both sites, as expected since the two sites are at similar latitudes and therefore locations with respect to the auroral oval, confirming this as a likely source region. Svalbard showed fewer waves with short periods than KEOPS data for a season when both had the same time resolution data. This gives a clear indication of the direction of flow of the gravity waves, and corroborates that the source is the auroral oval. This is because the energy is dissipated through heating in each cycle of a wave, therefore, over a given distance, short period waves lose more energy than long and dissipate before they reach their target

High time resolution measurements of the thermosphere from Fabry-Perot Interferometer measurements of atomic oxygen

Recent advances in the performance of CCD detectors have enabled a high time resolution study of the high latitude upper thermosphere with Fabry-Perot Interferometers(FPIs) to be performed. 10-s integration times were used during a campaign in April 2004 on an FPI located in northern Sweden in the auroral oval. The FPI is used to study the thermosphere by measuring the oxygen red line emission at 630.0 nm, which emits at an altitude of approximately 240 km. Previous time resolutions have been 4 min at best, due to the cycle of look directions normally observed. By using 10 s rather than 40 s integration times, and by limiting the number of full cycles in a night, high resolution measurements down to 15 s were achievable. This has allowed the maximum variability of the thermospheric winds and temperatures, and 630.0 nm emission intensities, at approximately 240 km, to be determined as a few minutes. This is a significantly greater variability than the often assumed value of 1 h or more. A Lomb-Scargle analysis of this data has shown evidence of gravity wave activity with waves with short periods. Gravity waves are an important feature of mesospherelower thermosphere (MLT) dynamics, observed using many techniques and providing an important mechanism for energy transfer between atmospheric regions. At high latitudes gravity waves may be generated in-situ by localised auroral activity. Short period waves were detected in all four clear nights when this experiment was performed, in 630.0 nm intensities and thermospheric winds and temperatures. Waves with many periodicities were observed, from periods of several hours, down to 14 min. These waves were seen in all parameters over several nights, implying that this variability is a typical property of the thermosphere

High concentrations of flavor chemicals are present in electronic cigarette refill fluids.

We characterized the flavor chemicals in a broad sample of commercially available electronic cigarette (EC) refill fluids that were purchased in four different countries. Flavor chemicals in 277 refill fluids were identified and quantified by gas chromatography-mass spectrometry, and two commonly used flavor chemicals were tested for cytotoxicity with the MTT assay using human lung fibroblasts and epithelial cells. About 85% of the refill fluids had total flavor concentrations >1 mg/ml, and 37% were >10 mg/ml (1% by weight). Of the 155 flavor chemicals identified in the 277 refill fluids, 50 were present at ≥1 mg/ml in at least one sample and 11 were ≥10 mg/ml in 54 of the refill fluids. Sixty-one% (170 out of 277) of the samples contained nicotine, and of these, 56% had a total flavor chemical/nicotine ratio >2. Four chemicals were present in 50% (menthol, triacetin, and cinnamaldehyde) to 80% (ethyl maltol) of the samples. Some products had concentrations of menthol ("Menthol Arctic") and ethyl maltol ("No. 64") that were 30 times (menthol) and 100 times (ethyl maltol) their cytotoxic concentration. One refill fluid contained cinnamaldehyde at ~34% (343 mg/ml), more than 100,000 times its cytotoxic level. High concentrations of some flavor chemicals in EC refill fluids are potentially harmful to users, and continued absence of any regulations regarding flavor chemicals in EC fluids will likely be detrimental to human health

Intercomparison of numerical models of flaring coronal loops

The proposed Benchmark Problem consists of an infinitesimal magnetic flux tube containing a low-beta plasma. The field strength is assumed to be so large that the plasma can move only along the flux tube, whose shape remains invariant with time (i.e., the fluid motion is essentially one-dimensional). The flux tube cross section is taken to be constant over its entire length. In planar view the flux tube has a semi-circular shape, symmetric about its midpoint s = s sub max and intersecting the chromosphere-corona interface (CCI) perpendicularly at each foot point. The arc length from the loop apex to the CCI is 10,000 km. The flux tube extends an additional 2000 km below the CCI to include the chromosphere, which initially has a uniform temperature of 8000 K. The temperature at the top of the loop was fixed initially at 2 X 1 million K. The plasma is assumed to be a perfect gas (gamma = 5/3), consisting of pure hydrogen which is considered to be fully ionized at all temperatures. For simplicity, moreover, the electron and ion temperatures are taken to be everywhere equal at all times (corresponding to an artificially enhanced electron-ion collisional coupling). While there was more-or-less unanimous agreement as to certain global properties of the system behavior (peak temperature reached, thermal-wave time scales, etc.), no two groups could claim satisfactory accord when a more detailed comparison of solutions was attempted

The Diagnostic Potential of Transition Region Lines under-going Transient Ionization in Dynamic Events

We discuss the diagnostic potential of high cadence ultraviolet spectral data when transient ionization is considered. For this we use high cadence UV spectra taken during the impulsive phase of a solar flares (observed with instruments on-board the Solar Maximum Mission) which showed excellent correspondence with hard X-ray pulses. The ionization fraction of the transition region ion O V and in particular the contribution function for the O V 1371A line are computed within the Atomic Data and Analysis Structure, which is a collection of fundamental and derived atomic data and codes which manipulate them. Due to transient ionization, the O V 1371A line is enhanced in the first fraction of a second with the peak in the line contribution function occurring initially at a higher electron temperature than in ionization equilibrium. The rise time and enhancement factor depend mostly on the electron density. The fractional increase in the O V 1371A emissivity due to transient ionization can reach a factor of 2--4 and can explain the fast response in the line flux of transition regions ions during the impulsive phase of flares solely as a result of transient ionization. This technique can be used to diagnostic the electron temperature and density of solar flares observed with the forth-coming Interface Region Imaging Spectrograph.Comment: 18 pages, 6 figure

The Classification of Periodic Light Curves from non-survey optimized observational data through Automated Extraction of Phase-based Visual Features

We implement two hidden-layer feedforward networks to classify 3011 variable star light curves. These light curves are generated from a reduction of non-survey optimized observational images gathered by wide-field cameras mounted on the Liverpool Telescope. We extract 16 features found to be highly informative in previous studies but achieve only 19.82% accuracy on a 30% test set, 5.56% above a random model. Noise and sampling defects present in these light curves poison these features primarily by reducing our Periodogram period match rate to fewer than 5%. We propose using an automated visual feature extraction technique by transforming the phase-folded light curves into image based representations. This eliminates much of the noise and the missing phase data, due to sampling defects, should have a less destructive effect on these shape features as they still remain at least partially present. We produced a set of scaled images with pixels turned either on or off based on a threshold of data points in each pixel defined as at minimum one fifth of those of the most populated pixel for each light curve. Training on the same feedforward network, we achieve 29.13% accuracy, a 13.16% improvement over a random model and we also show this technique scales with an improvement to 33.51% accuracy by increasing the number of hidden layer neurons. We concede that this improvement is not yet sufficient to allow these light curves to be used for automated classification and in conclusion we discuss a new pipeline currently being developed that simultaneously incorporates period estimation and classification. This method is inspired by approximating the manual methods employed by astronomers

GRAPE: Genetic Routine for Astronomical Period Estimation

Period estimation is an important task in the classification of many variable astrophysical objects. Here we present GRAPE: A Genetic Routine for Astronomical Period Estimation, a genetic algorithm optimised for the processing of survey data with spurious and aliased artefacts. It uses a Bayesian Generalised Lomb-Scargle (BGLS) fitness function designed for use with the Skycam survey conducted at the Liverpool Telescope. We construct a set of simulated light curves using both regular survey cadence and the unique Skycam variable cadence with four types of signal: sinusoidal, sawtooth, symmetric eclipsing binary and eccentric eclipsing binary. We apply GRAPE and a frequency spectrum BGLS periodogram to the light curves and show that the performance of GRAPE is superior to the frequency spectrum for any signal well modelled by the fitness function. This is due to treating the parameter space as a continuous variable.We also show that the Skycam sampling is sufficient to correctly estimate the period of over 90% of the sinusoidal shape light curves relative to the more standard regular cadence.We note that GRAPE has a computational overhead which makes it slower on light curves with low numbers of observations and faster with higher numbers of observations and discuss the potential optimisations used to speedup the runtime. Finally, we analyse the period dependence and baseline importance of the performance of both methods and propose improvements which will extend this method to the detection of quasi-periodic signals

An Initial Inquiry Into Mountain Ungulate Space Use Within the Greater Yellowstone Area

The expansion of mountain goats (Oreamnos americanus) throughout the Greater Yellowstone Area (GYA) has continued since their initial introduction in the 1940’s. Mountain goats occupy similar habitats as native bighorn sheep (Ovis canadensis) and harbor pathogens known to be detrimental to bighorn sheep recovery efforts. In 2006 the Greater Yellowstone Area Mountain Ungulate Project initiated a large-scale collaring effort to enhance our understanding of the spatial dynamics of both species and the potential impacts of mountain goat range expansion on regional bighorn sheep. The research is unique in spatial scale and encompasses ten study areas with examples of both sympatric and allopatric mountain ungulate populations. To date we have instrumented 122 individuals (76 BHS and 46 MTGs) with GPS collars and have recovered 45 collars (22 BHS and 23 MTGs) from the field. Initial inquiries into space use across species and study areas with respect to seasonal migrations and elevational changes will be discussed. An early investigation of the heterogeneity in mountain ungulate space use and movement strategies throughout the GYA will help to inform future capture efforts and provide insights into mountain ungulate competition across space and time

Classifying Periodic Astrophysical Phenomena from non-survey optimized variable-cadence observational data

Modern time-domain astronomy is capable of collecting a staggeringly large amount of data on millions of objects in real time. Therefore, the production of methods and systems for the automated classification of time-domain astronomical objects is of great importance. The Liverpool Telescope has a number of wide-field image gathering instruments mounted upon its structure, the Small Telescopes Installed at the Liverpool Telescope. These instruments have been in operation since March 2009 gathering data of large areas of sky around the current field of view of the main telescope generating a large dataset containing millions of light sources. The instruments are inexpensive to run as they do not require a separate telescope to operate but this style of surveying the sky introduces structured artifacts into our data due to the variable cadence at which sky fields are resampled. These artifacts can make light sources appear variable and must be addressed in any processing method. The data from large sky surveys can lead to the discovery of interesting new variable objects. Efficient software and analysis tools are required to rapidly determine which potentially variable objects are worthy of further telescope time. Machine learning offers a solution to the quick detection of variability by characterising the detected signals relative to previously seen exemplars. In this paper, we introduce a processing system designed for use with the Liverpool Telescope identifying potentially interesting objects through the application of a novel representation learning approach to data collected automatically from the wide-field instruments. Our method automatically produces a set of classification features by applying Principal Component Analysis on set of variable light curves using a piecewise polynomial fitted via a genetic algorithm applied to the epoch-folded data. The epoch-folding requires the selection of a candidate period for variable light curves identified using a genetic algorithm period estimation method specifically developed for this dataset. A Random Forest classifier is then used to classify the learned features to determine if a light curve is generated by an object of interest. This system allows for the telescope to automatically identify new targets through passive observations which do not affect day-to-day operations as the unique artifacts resulting from such a survey method are incorporated into the methods. We demonstrate the power of this feature extraction method compared to feature engineering performed by previous studies by training classification models on 859 light curves of 12 known variable star classes from our dataset. We show that our new features produce a model with a superior mean cross-validation F1 score of 0.4729 with a standard deviation of 0.0931 compared with the engineered features at 0.3902 with a standard deviation of 0.0619. We show that the features extracted from the representation learning are given relatively high importance in the final classification model. Additionally, we compare engineered features computed on the interpolated polynomial fits and show that they produce more reliable distributions than those fit to the raw light curve when the period estimation is correct