adsabs/WIESP2022-NER huggingface dataset:
Paper: 2020ApJ...895..104L (fulltext)
the test set, where the training set builds the model, and the testing set evaluates the model's performance. In principle, the predictor performs better with a larger fraction of training data. On the other hand, the performance statistic will have greater variance if there is less testing data (Kohavi 1995 ). To strike a balance, we have tried changing the ratios of our training and testing sets from 50:50 to 90:10, and we verify that the XGBoost performs the best in both AUROCC and f1-score with a 70:30 split. To avoid over-fitting, we adopt an early stopping in XGBoost after five training iterations that do not yield any improvements. To control the balance of positive and negative weights, we set the scale _ pos _ weight = 28.7, which is given by the ratio between the number of negative and positive instances (4705/164; see Section 2.3 ). 3.3. Feature Selection Feature selection is an important process in machine learning that strongly influences the performance of the model. Feature selection is a procedure of selecting a subset of relevant features for model construction. In general, proper feature selection can increase the efficiency by reducing the training duration, enhancing generalization by reducing over-fitting, and improving the prediction performance (see Chen Guestrin 2016 ; An et al. 2019 ). A trained XGBoost model will automatically calculate feature importance and provide the feature importance scores. In this work, we first trained the XGBoost model based on the training data set and selected features by sorting feature importance scores calculated from the trained model. We then iteratively trained the model based on the selected subset of features until the point of best performance. We verified that both f1-score and AUROCC increase with the number of selected features until we use up all 79 features. Therefore, we did not reduce the number of feature vectors in this work. Considering the limitations of our computational resources, we repeated this procedure 10 times by using a different combination of training and test data sets in each iteration and estimated the average feature importance score from these 10 realizations. The top five important features in our sample are: K , ([3.6]−[4.5]), ( K − [4.5]), ([3.6]–[5.8]), and ( H − K ), which are similar to those photometric wave bands used by Chen et al. ( 2016 ; Optical-Infrared Triple Color: ( z − K ), ( K − [3.6]), and ([3.6]–[4.5])) and An et al. ( 2019 ; ( z − K ), ( J − K ), ( K − [3.6]), and ([3.6]–[4.5])). The top five important features in our sample can be associated to fundamental physical properties. The K -band flux roughly maps to the stellar mass, while the SMGs appear to be red and occupy a relatively well-defined region in near-infrared color–color space (see also Chen et al. 2016 ; An et al. 2019 ). 3.4. Tuning the Hyper-parameters The hyper-parameters are a set of parameters that define the machine-learning algorithm as a mathematical formula. The
Paper: 2019ApJ...883...38A (fulltext)
site at a coronal location at or above the looptop. Having said that, we note that some spectral evolution of the electrons injected at the looptop along with the pitch-angle distribution can still affect the exact delay values, while the field inclination is taken into account within our 3D model. We checked that an SHS evolution of the injected nonthermal electron spectrum, where the spectral index varies by a few percent, while the electrons are beamed along the magnetic field can reduce the TOF distance by a factor of two as needed. However, such a model does not contain any population of nonthermal electrons reflected back to the coronal portion of the flaring loop(s) needed to account for the microwave emission. From this, we conclude that an electron beam with a modest angular scatter, rather than an ideal field-aligned beam, is responsible for the observed deviation of the loop half-length from the estimated TOF distance. Such an angular distribution is capable of reducing their “effective” speed along the magnetic field typically by a factor of 1.5–3. As we noted above, some angular spread is also required to supply a population of the upward-reflected electrons needed to interpret the microwave data. However, an isotropic angular distribution does not look likely given that it assumes a rather large pitch angle of the majority of streaming electrons and, thus, efficient trapping, which is not observed. Thus, the data favor an anisotropic beam-like distribution of the accelerated electrons with a modest opening angle of 30°–40°. The mirror ratio m = 3 in Loop II of our 3D model; thus, the electrons launched downward at the pitch-angle θ ≲ 35° will not be reflected at the footpoint and, thus, will not contribute to the trapping. The mean parallel velocity for the electrons launched at the looptop at θ = 35° is . Electrons with θ ≳ 35° will be reflected and will contribute to the brightness peaks observed at 5.7 GHz from the LF–loop. The observed delay of 70 ms between 5.7 GHz peaks and 102.2–293.5 keV emission peaks, both produced by presumably 400 keV electrons, places the 5.7 GHz source at a distance ∼5 Mm from the mirror points at the bases of loop II. Microwave emission at 17 and 34 GHz delayed by 120 ms requires an additional time for the electrons to be transported from Loop II to Loop I and, perhaps, additional time for trapping in Loop I. Thus, the time delays are naturally consistent with the identified magnetic configuration of the flare and the TPP model in which the particles are injected at the looptop of Loop II and then propagate to produce the observed variety of the HXR and microwave components. 3.2. Accelerated Electron Population In Section 2.4 we determined the HXR spectrum of subpeaks to be a single power-law with the indexes about 2.4. This spectrum is injected during a subpeak in addition to a preexisting population of nonthermal electrons responsible for a gradual (background) component of the burst.
Paper: 2018MNRAS.481.4332A (fulltext)
β > 1 where L max remains irrelevant. Thus, equation ( 4 ) reduces to EE detection probability: Perley et al. ( 2009 ) caution that many non-EE short bursts are faint, and the presence of an extended emission might have gone undetected resulting in their classification as non-EE bursts. Norris et al. ( 2010 ), who did a detailed study on the sensitivity of BAT to extended emission conclude that BAT should be able to detect extended emission to intensities ∼10 −2 counts cm −2 s −1 , however it is only seen up to around ∼0.005 counts cm −2 s −1 indicating a true absence of EE in some bursts. Going by the latter, one does not necessarily have to incorporate an additional EE detection probability. However, we take a conservative stand and try to account for a possible selection bias against the detection of the faint extended emission component. The redshift-dependent EE detection probability should involve the luminosity function of extended emission, but with the limited sample it is difficult to arrive at the same. Therefore, we considered a single constant luminosity for extended emission. This approximation is simplistic, given the two orders of magnitude spread in EE luminosity (see Fig. 3 ). For the threshold of BAT, we used ε BAT = 0.005 counts cm −2 s −1 , motivated by the typical average fluxes EE is seen in BAT as mentioned by Norris et al. ( 2010 ). Thus, equation ( 4 ), in this particular case, reduces to as, P () becomes a step function, H ( L EE − 4π d L () 2 ε BAT ) which is zero if L EE 4π d L () 2 ε BAT and unity otherwise. We ran our simulations for two values of L EE around the average luminosity of the six bursts (1.5 × 10 56 counts s −1 ), L EE = 10 56 , and 2 × 10 56 counts s −1 resulting in a max of 1.8 and 2.53, respectively. The results we obtain from these are nearly identical. Figure 3. Average luminosity of the EE component, from the data presented in Norris et al. ( 2010 ). Pen symbol is GRB 090510A. The error bars on other bursts are smaller than the symbol. One of our weak-EE bursts, 080123, is not a part of the data of Norris et al. ( 2010 ). We included GRB160410 following the GCN report by Sakamoto et al. ( 2016 ). We used a det = 0.16 sq cm to convert numbers from Sakamoto et al. ( 2016 ) and calculated the average luminosity. It can be seen that L EE varies by around two orders of magnitude. For our analysis we fixed values near the mean of these bursts (dotted and dash–dotted lines). The yellow solid line is the threshold, . We also considered a P () = 1 throughout, i.e. BAT does not miss any EE component due to sensitivity issues. Throughout this paper, we have used a standard cosmological model with H 0 = 71 km s −1 Mpc −1 , Ω m = 0.3, and Ω λ = 0.7. 4 ANALYSIS AND RESULTS In this section, we describe our analysis of the redshift distribution for both EE and non-EE bursts, and compare the parameters focusing mostly on the delay time distribution function. We begin by giving a detailed account of how we set up
Paper: 2021MNRAS.503.1431H (fulltext)
component of this line and the corresponding tomogram are presented in Fig. 8 . Meanwhile, the same line is treated as an absorption feature on the right-hand side. The emission component of the line basically repeats the pattern observed in H α. The observed trailed spectrum is faint, but the program picks up the signal correctly, reflecting it in the reconstructed spectrum. The low-velocity component definitely dominates, and hence a compact spot materializes close to the L1 point, in contrast with the diffuse appearance in the H α case. The HVC is hardly traceable in the observed trailed spectra, but apparently some emission comes from here too. Hence, a coma-like extension in the velocity map is visible 5 SYSTEM PARAMETERS The average mass of WDs in CVs above the period gap is M WD ( P orb > 3 h) = 0.86(20) M ⊙ (Zorotovic et al. 2011 ). For simplicity, we adopted M WD = 0.8 M ⊙ . Taking into account the semi-empirical relation of secondary type versus orbital period, we expect a secondary with a mass of M 2 ≈ 0.3 M ⊙ and spectral type of M3V–M4V (Knigge 2006 ). The expected mass ratio and separations are q ≡ M 2 / M WD ≈ 0.4 and a = 1.23 R ⊙ , respectively. The accretion-disc truncation radius 4 is R ⊙ . The corresponding primary, secondary, and L1 orbital velocities are 117, 292, and 129 km s −1 , respectively (see Fig. 9 ). Figure 9. A geometric model of BG Tri. The centre of mass (x) and corresponding Lagrangian points are marked. Velocities of the L1 point, the disc outflow area (green), and the WD are denoted. The LVC, which is emitted from the irradiated hemisphere of the secondary star, has a velocity exceeding that of the L1 point and less than the orbital velocity of the star. There are numerous flux measurements of BG Tri available in the public domain. We fetched all available data from VizieR 5 (Ochsenbein, Bauer Marcout 2000 ). We revised the available data, since some measurements are erroneous (e.g. the SDSS data are not correct because the object is too bright), and compiled those that passed scrutiny in Table 2 . Selected data are plotted using circles in the bottom panel of Fig. 10 , after the interstellar reddening correction of E ( B − V ) = 0.03 (Green et al. 2015 ). Using the Gaia distance of d = 334(8) pc, we can calculate the luminosity of different components. In particular, even a hot 50 kK white dwarf will have a negligible contribution to the optical flux. A secondary M3–4 V Roche lobe filling star has some insignificant influence in the IR. A comparison of the observed fluxes with those expected from different components confirms that the flux in the entire wavelength range is formed mostly by the accretion disc. The dashed lines in the bottom panel of Fig. 10 indicate a simple accretion-disc spectrum as a composition of multiple blackbodies, from concentric rings with a corresponding distribution of temperatures throughout the stationary optically thick disc (La Dous 1989 ). Since the observed flux would
Paper: 2019MNRAS.484.3119N (fulltext)
the MOS 1, MOS 2, and pn data with the xspec model const*phabs ( cemekl ) (see text for details). 2.4 RGS data Note that the same multitemperature model used for the EPIC spectral fit provides an adequate description of the RGS spectra when fixing all the parameters of the model to those derived above but adjusting consistently the model normalizations. In particular, inspecting Fig. 5 (where the best fit is superimposed to the RGS1 and RGS2 data re-binned in order to have at least a significance of 10σ per bin), one can note the existence of the O VIII Ly α emission line at ≃ 0.65 keV, and emission lines in the energy range 0.56–0.57 keV (21.6–22.1 ) possibly associated with the He-like transitions of O VII , i.e. the resonance line r (corresponding to the transitions between the n = 2 shell and the n = 1 ground state), and the intercombination ( i ) and the forbidden ( f ) lines. In this respect, as shown by Porquet Dubau ( 2000 ) the relative emission strength of the r, i , and f lines is a good indicator of the physical conditions of density and temperature of the gas. Figure 5. The best-fitting model superimposed on the RGS 1 (red) and RGS 2 (black) data. Note the presence a strong O VIII Ly α line at 18.96 and the O VII He-like triplet. In order to have a measure of the line fluxes, we followed the phenomenological spectral analysis method described in Nucita et al. ( 2010 ) and references therein. In particular, the unbinned RGS spectra are divided in intervals of 100 channels wide and Gaussians are fitted to all the identified emission lines. For each (single) emission line the centroid energy is free to vary and, when triplets are identified, the relative distance between the central energies was frozen to the value predicted by atomic physics. The local continuum was always modelled as a power law with a fixed photon index Γ = 1 and normalization free to vary. Since we are dealing with the unbinned spectra, we estimated the goodness of the fit by using the C-statistic (see Cash 1979 ). We further consider the line (or triplet) detected at confidence level when, repeating the fit with the continuum only, we obtained a change in the C-statistic value (Δ C ) by at least 2.3. The result of this analysis (see also Table 2 ) showed that the O VII resonance line is much more weaker than the intercombination line so that ionization processes may occur in the medium (Porquet Dubau 2000 ). This is also clear when using the standard line ratios R = f / i, L = r / i , and G = ( f + i )/ r as standard diagnostic. In particular, as clear from table 2, the large (small) values of the G ( R ) ratio imply photoionization and, since R decreases with increasing values of the electron density n e , a lower limit on the electron density of ≃10 11 cm −3 (Porquet Dubau 2000 ). Table 1. Spectral parameters for the best-fitting model ( const * phabs *( cemekl ) in xspec , see text for details). We also give the 0.3–10 keV absorbed and unabsorbed fluxes.
Paper: 2017MNRAS.469..448B (fulltext)
the 2 level in the Y and J bands, consistent with the measured magnitudes in the HST /WFC3 imaging. To determine the photometry and colour of the Pop III/DCBH candidate, we measured photometry on the ground-based imaging centred on the position of component A using small circular apertures of 1.2arcsec in diameter. We also measured the photometry in larger apertures of 2arcsec in diameter to compare directly with the results of S15 , and using a diameter of 3arcsec to provide a more appropriate measure of the total flux. The larger apertures were centred on the peak of the Y J UltraVISTA image, which is offset by 0.35arcsec to the north-west of component A (the stamps shown in Fig. 1 are centred on this position). The results are shown in Table 1 . All photometry was corrected to total magnitudes assuming a point-source correction. 2 S15 found a strong excess in the J -band photometry for CR7 using the UltraVISTA DR2 data, when the continuum level was derived from fitting to the Y , H and K s bands. The observed offset of m AB 0.4 0.13mag was interpreted as the contribution to the measured photometry from the spectroscopically observed He ii emission line. Converting the J -band excess into an estimated rest-frame equivalent width, S15 inferred an equivalent width of EW 0 80 20 for the He ii line. Table 1. The measured photometry for CR7 utilizing deeper near-infrared data from UltraVISTA DR3 and mid-infrared imaging from the SPLASH survey. The upper part of the table shows the available HST and Spitzer photometry. For the full object photometry, labelled as component Full, the photometry was measured in 3-arcsec-diameter circular apertures on the HST /WFC3 data, and the Spitzer /IRAC photometry was obtained using a deconfusion analysis based on the UltraVISTA K s -band data. The lower part of the table shows the ground-based photometry measured in 1.2-, 2.0- and 3-arcsec-diameter circular apertures. Note that the aperture photometry has been corrected to total assuming a point-source correction. The final row of the table reproduces the photometry and value as measured in S15 . Using the deeper near-infrared imaging now available, we find a smaller J -band excess than that determined in S15 , with for the 1.2-arcsec-diameter measurement. Here, the continuum level was determined from the best-fitting Bruzual Charlot ( 2003 ) SED to the Y , H , K s and [3.6] bands with the redshift fixed to z 6.6 (as shown in Fig. 2 ). If, instead, the continuum level in the J band is determined from a power-law fit to the Y , H and K s bands, we find an excess of . An offset of 0.2mag was also found using the two larger aperture measurements, and the results are unchanged if point spread function (PSF) homogenized images are used. To understand the differences between our results and those of S15 , we compared photometric catalogues from the older DR2 data used by S15 to those from the DR3 data. In the four bands, we find no significant zero-point offset
Paper: 2016MNRAS.463.3829R (fulltext)
2015 ). Although the SED of these CDs presents several overlapping features with a ‘normal’ SED of TDs, it would be misleading to treat them in a similar way. For instance, the implications for the presence of another star in the star--disc system entails an incorrect measurement of the luminosity and temperature, which translates into inaccurate age and mass estimates. This is the case of Coku Tau/4 and CS Cha that were originally described as TDs (Forrest et al. 2004 ; Espaillat et al. 2007 ), but eventually were presented as CDs (Guenther et al. 2007 ; Ireland Kraus 2008 ). This misclassification would be reflected in the estimation of birthplaces and time-scales for formation of sub-stellar companions (brown dwarfs) and/or planetary systems, and the demographic properties of these populations (e.g. Najita, Andrews Muzerolle 2015 ). Therefore, determining a more accurate relative picture of the lifetime of TDs and CDs requires a comprehensive survey capable of resolving close binaries ( ≲ 30 au) and measuring their frequency in objects previously classified as TDs through their SEDs. Although the open gap in the inner region of the disc might have different physical origins, in this paper we seek to identify if the dispersion of the primordial material in the inner region of the disc is a result of the tidal interaction between a close binary system and the disc. At small separations, detecting faint companions orbiting bright stars, that in addition, are surrounded by dusty material, can be challenging due to the high contrast between the companion and the primary star. However, observations of objects at early ages provide favourable IR contrast ratios for the detection of so far unresolved faint companions because of their intrinsically higher luminosity (Δ K 5 mag). We use the Non-Redundant Mask interferometry (NRM) technique and NIRC2 instrument located at the Keck II telescope, which offers a solution to reach angular resolutions with the necessary contrast and is resistant to speckle noise in the image by measuring a self-calibrating quantity known as closure phase (e.g. Martinache 2011 ). In order to achieve a higher accuracy in the detection limits of our data, the NRM completeness as a function of position and contrast utilizes a combination of a MonteCarlo Integration approach, giving a random sample of artificial binary stars, and Bayesian Inference , which uses prior probability density functions of the binary orbital parameters. We have restricted the selection of objects to regions with an age of ∼1–3 Myr and within a distance of about 220 pc. Taurus-Aurigae, IC348 region (Perseus) and Ophiucus star-forming regions satisfy these criteria (Loinard et al. 2008 ; Wilking, Gagné Allen 2008 ). This article is arranged as follows. In Section 2 , we present the motivation for the sample selection, description of observations together with the data analysis and a review of the target properties such as distance to the star-forming
Paper: 2019MNRAS.484.4495H (fulltext)
pronounced, indicating an increase by ∼100 km s −1 . Remarkably, we observed at the epoch of the sudden magnetic field increase (indicated by the red triangle) that the radial velocity of all spectral lines dropped by several 10 km s −1 . To better highlight the observed changes in radial velocities, we show in Fig. 9 the radial velocity shift correlations between the lines belonging to different elements. Figure 7. Radial velocities measured for different spectral lines in the observations obtained between 2014 and 2018. Figure 8. Radial velocities measured for different spectral lines in the observations obtained between 2017 and 2018. Figure 9. Radial velocity correlations for hydrogen and He i lines. The observed dispersion of the radial velocity measurements in these figures is probably related to β Cephei-like pulsations. The atmospheric parameters presented by Castro et al. ( 2015 ) suggest that HD 54879 has already slightly evolved from the ZAMS and is passing through the β Cephei instability strip. As mentioned above, Järvinen et al. ( 2017 ) were not able to detect significant velocity shifts in high-resolution HARPSpol observations of HD 54879, most likely due to much longer exposure times, of the order of 1–3 h. During the long HARPSpol exposures, any spectral variability is smeared over the pulsation cycle and difficult to detect. In contrast, FORS 2 observations carried out using an 8 m telescope have a duration of only 10–20 min and are expected to be more strongly affected by the β Cephei-like pulsations. According to Telting et al. ( 2006 ), at least half of the late-O to early-B solar-neighbourhood stars that are located in the β Cephei instability strip are actually pulsating in radial and/or non-radial modes. With respect to the remarkable change of the spectral appearance of HD 54879 during the sudden magnetic field increase, it is important to mention that a recent study of the most strongly magnetized O-type star NGC 1624-2 using UV observations with HST /COS revealed similar dramatic variations in the resonance line profiles between rotation phases when looking nearly at the magnetic pole and those when looking nearly at the magnetic equator (see fig. 1 in David-Uraz et al. 2018 ). Similar to our observations of HD 54879, these line profiles show at these two rotation phases very different characteristics including the line intensity, line shape and width, and radial velocity. It should however be noted that the variability of the UV resonance lines in massive stars is usually related to the stellar wind or to the magnetosphere, while for HD 54879 we observe line profile changes in the photospheric lines. It cannot be excluded that the observed changes in radial velocity are caused by an invisible wide companion. Indeed, Castro et al. ( 2015 ) compared their radial velocity measurement from one HARPSpol spectrum with other measurements in the literature and concluded that HD 54879 could be a member of a long-period