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Register a new userGround-based detection of G star superflares with NGTS
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We present high cadence detections of two superflares from a bright G8 star (V = 11.56) with the Next Generation Transit Survey (NGTS). We improve upon previous superflare detections by resolving the flare rise and peak, allowing us to fit a solar flare inspired model without the need for arbitrary break points between rise and decay. Our data also enables us to identify substructure in the flares. From changing starspot modulation in the NGTS data we detect a stellar rotation period of 59 hours, along with evidence for differential rotation. We combine this rotation period with the observed textit{ROSAT} X-ray flux to determine that the stars X-ray activity is saturated. We calculate the flare bolometric energies as $5.4^{+0.8}_{-0.7}times10^{34}$ and $2.6^{+0.4}_{-0.3}times10^{34}$ erg and compare our detections with G star superflares detected in the textit{Kepler} survey. We find our main flare to be one of the largest amplitude superflares detected from a bright G star. With energies more than 100 times greater than the Carrington event, our flare detections demonstrate the role that ground-based instruments such as NGTS can have in assessing the habitability of Earth-like exoplanets, particularly in the era of textit{PLATO}.
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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 texttt{autovet} code used to implement the algorithm publicly accessible. texttt{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.
We present the discovery of NGTS J214358.5-380102, an eccentric M-dwarf binary discovered by the Next Generation Transit Survey. The system period of 7.618 days is greater than many known eclipsing M-dwarf binary systems. Its orbital eccentricity of $0.323^{+0.0014}_{-0.0037}$, is large relative to the period and semi-major axis of the binary. Global modelling of photometry and radial velocities indicate stellar masses of $M_A$=$0.426 ^{+0.0056}_{-0.0049}$, $M_B$=$0.455 ^{+0.0058}_{-0.0052}$ and stellar radii $R_A$=$0.461 ^{+0.038}_{-0.025}$ $R_B$=$0.411 ^{+0.027}_{-0.039}$, respectively. Comparisons with stellar models for low mass stars show that one star is consistent with model predictions whereas the other is substantially oversized. Spectral analysis of the system suggests a primary of spectral type M3V, consistent with both modelled masses and radii, and with SED fitting of NGTS photometry. As the most eccentric eclipsing M-dwarf binary known, NGTS J214358.5-380102 provides an interesting insight into the strength of tidal effects in the circularisation of stellar orbits.
Recently, many superflares on solar-type stars were discovered as white-light flares (WLFs). A correlation between the energies (E) and durations (t) of superflares is derived as $tpropto E^{0.39}$, and this can be theoretically explained by magnetic reconnection ($tpropto E^{1/3}$). In this study, we carried out a statistical research on 50 solar WLFs with SDO/HMI to examine the t-E relation. As a result, the t-E relation on solar WLFs ($tpropto E^{0.38}$) is quite similar stellar superflares, but the durations of stellar superflares are much shorter than those extrapolated from solar WLFs. We present the following two interpretations; (1) in solar flares, the cooling timescale of WL emission may be longer than the reconnection one, and the decay time can be determined by the cooling timescale; (2) the distribution can be understood by applying a scaling law $tpropto E^{1/3}B^{-5/3}$ derived from the magnetic reconnection theory.
Recently, many superflares on solar-type stars have been discovered as white-light flares (WLFs). The statistical study found a correlation between their energies ($E$) and durations ($tau$): $tau propto E^{0.39}$ (Maehara et al. 2017 $EP& S$, 67, 59), similar to those of solar hard/soft X-ray flares: $tau propto E^{0.2-0.33}$. This indicates a universal mechanism of energy release on solar and stellar flares, i.e., magnetic reconnection. We here carried out a statistical research on 50 solar WLFs observed with textit{SDO}/HMI and examined the correlation between the energies and durations. As a result, the $E$--$tau$ relation on solar WLFs ($tau propto E^{0.38}$) is quite similar to that on stellar superflares ($tau propto E^{0.39}$). However, the durations of stellar superflares are one order of magnitude shorter than those expected from solar WLFs. We present the following two interpretations for the discrepancy. (1) In solar flares, the cooling timescale of WLFs may be longer than the reconnection one, and the decay time of solar WLFs can be elongated by the cooling effect. (2) The distribution can be understood by applying a scaling law ($tau propto E^{1/3}B^{-5/3}$) derived from the magnetic reconnection theory. In this case, the observed superflares are expected to have 2-4 times stronger magnetic field strength than solar flares.
In order to discuss the potential impact of solar superflares on space weather, we investigated statistical relations among energetic proton peak flux with energy higher than $ 10 rm MeV$ ($F_p$), CME speed near the Sun ($V_{CME}$) obtained by {it SOHO}/LASCO coronagraph and flare soft X-ray peak flux in 1-8AA band ($F_{SXR}$) during 110 major solar proton events (SPEs) recorded from 1996 to 2014. The linear regression fit results in the scaling relations $V_{CME} propto F_{SXR}^alpha$, $F_ppropto F_{SXR}^beta$ and $F_ppropto V_{CME}^gamma$ with $alpha = 0.30pm 0.04$, $beta = 1.19 pm 0.08$ and $gamma = 4.35 pm 0.50$, respectively. On the basis of simple physical assumptions, on the other hand, we derive scaling relations expressing CME mass ($M_{CME}$), CME speed and energetic proton flux in terms of total flare energy ($E_{flare}$) as, $M_{CME}propto E_{flare}^{2/3}$, $V_{CME}propto E_{flare}^{1/6}$ and $F_{p}propto E_{flare}^{5/6}propto V_{CME}^5$, respectively. We then combine the derived scaling relations with observation, and estimated the upper limit of $V_{CME}$ and $F_p$ to be associated with possible solar superflares.
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