A Chromium Plating Bath Containing Sodium Benzenesulfonate Instead of Sulfuric Acid

At the present day chromium plated articles are saving the country many tons of badly needed metals. The beautiful, bright,luster that chromium has is no longer the prime quality that makes its use so extensive. Today its extreme hardness has taken the foremost place. Parts worn by use and re-claimed with chromium, not only wear longer than the original ones· but they also save valuable metal and time put into new ones. New articles plated with chromium have increased life, and thus the time and metal needed for replacement is greatly reduced. The present chromium plating baths containing sulfuric acid as the active agent in electrolysis presents many problems leading to investigation and experimentation as far as the throwing power, plating range, and efficiency is concerned. Many addition agents have been tried with different degrees of success. The purpose of this investigation is to improve on the above factors and show that bright deposits can be obtained using an organic addition agent such as sodium benzenesulfonate

Bundle Security Protocol for ION

This software implements bundle authentication, conforming to the Delay-Tolerant Networking (DTN) Internet Draft on Bundle Security Protocol (BSP), for the Interplanetary Overlay Network (ION) implementation of DTN. This is the only implementation of BSP that is integrated with ION

NGTS-4b: A sub-Neptune transiting in the desert

We report the discovery of NGTS-4b, a sub-Neptune-sized planet transiting a 13th magnitude K-dwarf in a 1.34 d orbit. NGTS-4b has a mass M = 20.6 ± 3.0 M⊕ and radius R = 3.18 ± 0.26 R⊕, which places it well within the so-called ‘Neptunian Desert’. The mean density of the planet (3.45 ± 0.95 g cm−3) is consistent with a composition of 100  per cent H2O or a rocky core with a volatile envelope. NGTS-4b is likely to suffer significant mass loss due to relatively strong EUV/X-ray irradiation. Its survival in the Neptunian desert may be due to an unusually high-core mass, or it may have avoided the most intense X-ray irradiation by migrating after the initial activity of its host star had subsided. With a transit depth of 0.13 ± 0.02 per cent, NGTS-4b represents the shallowest transiting system ever discovered from the ground, and is the smallest planet discovered in a wide-field ground-based photometric survey

Planet Hunters NGTS: New Planet Candidates from a Citizen Science Search of the Next Generation Transit Survey Public Data

We present the results from the first two years of the Planet Hunters Next Generation Transit Survey (NGTS) citizen science project, which searches for transiting planet candidates in data from the NGTS by enlisting the help of members of the general public. Over 8000 registered volunteers reviewed 138,198 light curves from the NGTS Public Data Releases 1 and 2. We utilize a user weighting scheme to combine the classifications of multiple users to identify the most promising planet candidates not initially discovered by the NGTS team. We highlight the five most interesting planet candidates detected through this search, which are all candidate short-period giant planets. This includes the TIC-165227846 system that, if confirmed, would be the lowest-mass star to host a close-in giant planet. We assess the detection efficiency of the project by determining the number of confirmed planets from the NASA Exoplanet Archive and TESS Objects of Interest (TOIs) successfully recovered by this search and find that 74% of confirmed planets and 63% of TOIs detected by NGTS are recovered by the Planet Hunters NGTS project. The identification of new planet candidates shows that the citizen science approach can provide a complementary method to the detection of exoplanets with ground-based surveys such as NGTS

Scintillation-limited photometry with the 20-cm NGTS telescopes at Paranal Observatory

Ground-based photometry of bright stars is expected to be limited by atmospheric scintillation, although in practice observations are often limited by other sources of systematic noise. We analyse 122 nights of bright star (Gmag ≲ 11.5) photometry using the 20-cm telescopes of the Next-Generation Transit Survey (NGTS) at the Paranal Observatory in Chile. We compare the noise properties to theoretical noise models and we demonstrate that NGTS photometry of bright stars is indeed limited by atmospheric scintillation. We determine a median scintillation coefficient at the Paranal Observatory of CY=1.54⁠, which is in good agreement with previous results derived from turbulence profiling measurements at the observatory. We find that separate NGTS telescopes make consistent measurements of scintillation when simultaneously monitoring the same field. Using contemporaneous meteorological data, we find that higher wind speeds at the tropopause correlate with a decrease in long-exposure (t = 10 s) scintillation. Hence, the winter months between June and August provide the best conditions for high-precision photometry of bright stars at the Paranal Observatory. This work demonstrates that NGTS photometric data, collected for searching for exoplanets, contains within it a record of the scintillation conditions at Paranal

NGTS clusters survey -- II. White-light flares from the youngest stars in Orion

We present the detection of high energy white-light flares from pre-main sequence stars associated with the Orion complex, observed as part of the Next Generation Transit Survey (NGTS). With energies up to $5.2\times10^{35}$ erg these flares are some of the most energetic white-light flare events seen to date. We have used the NGTS observations of flaring and non-flaring stars to measure the average flare occurrence rate for 4 Myr M0-M3 stars. We have also combined our results with those from previous studies to predict average rates for flares above $1\times10^{35}$ ergs for early M stars in nearby young associations.STFC ST/M001962/1; ST/P000495/

NGTS-21b: An Inflated Super-Jupiter Orbiting a Metal-poor K dwarf

We report the discovery of NGTS-21b, a massive hot Jupiter orbiting a low-mass star as part of the Next Generation Transit Survey (NGTS). The planet has a mass and radius of $2.36 \pm 0.21$ M$_{\rm J}$, and $1.33 \pm 0.03$ R$_{\rm J}$, and an orbital period of 1.543 days. The host is a K3V ($T_{\rm eff}=4660 \pm 41$, K) metal-poor (${\rm [Fe/H]}=-0.26 \pm 0.07$, dex) dwarf star with a mass and radius of $0.72 \pm 0.04$, M$_{\odot}$,and $0.86 \pm 0.04$, R$_{\odot}$. Its age and rotation period of $10.02^{+3.29}_{-7.30}$, Gyr and $17.88 \pm 0.08$, d respectively, are in accordance with the observed moderately low stellar activity level. When comparing NGTS-21b with currently known transiting hot Jupiters with similar equilibrium temperatures, it is found to have one of the largest measured radii despite its large mass. Inflation-free planetary structure models suggest the planet's atmosphere is inflated by $\sim21\%$, while inflationary models predict a radius consistent with observations, thus pointing to stellar irradiation as the probable origin of NGTS-21b's radius inflation. Additionally, NGTS-21b's bulk density ($1.25 \pm 0.15$, g/cm$^3$) is also amongst the largest within the population of metal-poor giant hosts ([Fe/H] < 0.0), helping to reveal a falling upper boundary in metallicity-planet density parameter space that is in concordance with core accretion formation models. The discovery of rare planetary systems such as NGTS-21 greatly contributes towards better constraints being placed on the formation and evolution mechanisms of massive planets orbiting low-mass stars.Comment: 12 pages, 13 figures, accepted for publication in MNRA

Automatic vetting of planet candidates from ground based surveys : machine learning with NGTS

State of the art exoplanet transit surveys are producing ever increasing quantities of data. To make the best use of this resource, in detecting interesting planetary systems or in determining accurate planetary population statistics, requires new automated methods. Here we describe a machine learning algorithm that forms an integral part of the pipeline for the NGTS transit survey, demonstrating the efficacy of machine learning in selecting planetary candidates from multi-night ground based survey data. Our method uses a combination of random forests and self-organising-maps to rank planetary candidates, achieving an AUC score of 97.6% in ranking 12368 injected planets against 27496 false positives in the NGTS data. We build on past examples by using injected transit signals to form a training set, a necessary development for applying similar methods to upcoming surveys. We also make the autovet code used to implement the algorithm publicly accessible. autovet is designed to perform machine learned vetting of planetary candidates, and can utilise a variety of methods. The apparent robustness of machine learning techniques, whether on space-based or the qualitatively different ground-based data, highlights their importance to future surveys such as TESS and PLATO and the need to better understand their advantages and pitfalls in an exoplanetary context