adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021MNRAS.505..991C (fulltext)
part of the GAMA survey. This provides the substantial advantage of deep and complete spectroscopic coverage to select targets and define environment. The GAMA regions also contain excellent photometric data across 21 bands from ultraviolet to far-infrared (Driver et al. 2016 ). Bryant et al. ( 2015 ) describe the selection of targets in the GAMA regions in detail, but for completeness we outline the selection here. The SAMI Galaxy Survey targets in the GAMA regions were selected within three 4 × 12° regions along the equator (Dec. ≃0°), centred at approximate right RA of 9, 12, and 15 h (see Table 1 ). Galaxies were targeted based on cuts in the redshift-stellar mass plane, with a stellar mass proxy that used i -band magnitude and g − i colour (Taylor et al. 2011 ; Bryant et al. 2015 ). Galaxies were selected within a series of four stepped volumes, with higher stellar mass limits at higher redshift (see fig. 4 of Bryant et al. 2015 ). The primary sample is limited to redshift z 0.095. Observations aimed to have high and uniform completeness for this primary sample. A secondary sample was also defined that included high mass galaxies to higher redshift ( z 0.115) and used fainter stellar mass limits. As the secondary targets were observed at lower priority, these are less complete (see Table 1 ). Fig. 1 shows the distribution of primary (observed: red, unobserved: blue) and secondary targets (observed: magenta, unobserved: cyan) in redshift and RA. A set of filler galaxies, at lower priority that the secondary targets, was also defined. These are discussed in Section 2.1.3 . Figure 1. The distribution of SAMI galaxies within the three GAMA regions. We show the original GAMA redshift survey (small grey points), observed (red), and unobserved (blue) primary SAMI galaxies, observed (magenta) and unobserved (cyan) secondary SAMI galaxies. Table 1. The coordinates and number of objects in the GAMA regions. For each region, we list the RA and declination (Dec.). For both the primary and secondary samples, we list the number of observed galaxies N obs , the number of good targets N good (i.e. those not flagged as bad for photometric reasons such as a bright star in the field of view) and the number of all targets N all (including objects with bad flags). The listed completeness for the primary sample is N obs / N good . Each potential target was visually checked to identify problems, such as bright nearby stars or sources being a sub-component of a larger galaxy. These were flagged within the input catalogue and their priority set so that they were not observed. The column named BAD_CLASS in the input catalogue (named InputCatGAMADR3) contains flags for different types of problem sources. A full description of the flags is given by Bryant et al. ( 2015 ). A small subset of the sources required adjustment of the coordinates for targeting (0.6 per cent of the input catalogue). This was typically in order to place an asymmetric galaxy or a close pair
Paper: 2020ApJ...903..106C (fulltext)
the best-fit model yielding erg s −1 . Figure 1. Top: deconvolved spectrum (crosses) and best-fit model (solid line) observed with XMM-Newton. PN, MOS1, and MOS2 cameras are plotted in black, red, and blue, respectively. Bottom: residuals are shown. The spectrum is rebinned for visual clarity. Table 1 CQ 4479 X-Ray Fitting Parameters Note. X-ray spectrum fit parameter results from an absorbed power-law fit corrected for galactic absorption. When both parameters are left free, or one of each parameter is fixed using previous fit results, we consistently find an upper limit solution to characteristic of an unobscured quasar. 2.3. Far-ultraviolet to Far-infrared Photometry Far-ultraviolet (FUV) to far-infrared (FIR) observations are obtained from the Stripe 82X catalog (Ananna et al. 2017 ). The target has FUV and NUV coverage from GALEX (Martin et al. 2005 ), and optical coverage from the coadded SDSS catalogs (Jiang et al. 2014 ; Fliri Trujillo 2015 ). In the infrared, Stripe 82X was observed with the Spitzer Space Telescope at 3.6 and 4.5 μ m (Papovich et al. 2016 ; Timlin et al. 2016 ) as well as the WISE 3.4, 4.6, 12, and 22 μ m bands (Wright et al. 2010 ). Herschel/SPIRE observations using 250, 350, and 500 μ m bands are also available for the Stripe82X field from the Herschel Stripe82 Survey (HerS; Viero et al. 2014 ). For more information on the Stripe 82X catalog and its counterpart identification protocol, please see LaMassa et al. ( 2016 ) and Ananna et al. ( 2017 ). 2.4. SOFIA HAWC+ Observations Due to the large gap in spectral coverage from 22 to 250 μ m, crucial for measuring the dust-obscured star formation rate (SFR) and the AGN contribution to the mid-IR, new observations using SOFIA/HAWC+ (Dowell et al. 2013 ; Smith et al. 2014 ) were taken using band C at 89 μ m (PI: A. Kirkpatrick, PID: 07-0096). CQ 4479 was observed for ∼7223 s on 2019 September 7 during SOFIA Cycle 7 using the HAWC+ total intensity OTFMAP mode with a Lissajous pattern in nominal water vapor conditions. At this redshift, each 155 pixel subtends a linear width of 8.4 kpc. The total field of view of HAWC+ in this setup is 42 by 27, with an FWHM of 78. Following data acquisition, the observations were reduced using the HAWCDPR PIPELINE (Harper et al. 2018 ). Flux calibration was performed using an average reference calibration factor across the detector. Source extraction was performed using the CRUSH V. 2.50-2 detect routine (Kovács 2006 , 2008 ) in the faint object mode, yielding a flux measurement of 75.42 ± 14.2 mJy in a 78 FWHM beam (SNR = 5.31). The SOFIA HAWC+ band C image is shown in Figure 2 . Figure 2. Left: SOFIA HAWC+ Band C observation of CQ 4479 centered at the target coordinates of R.A. J2000 of 1 h 40 m 4071 and Decl. J2000 of 0°17′ 5817. For reference, the SOFIA HAWC+ Band C (89 μ m) PSF FWHM is the circle in the lower left (78). At z = 0.405, the spatial scale is 5.413 kpc/′′ (Wright 2006 ). Each pixel is 155, subtending a linear width of 8.4
Paper: 2021MNRAS.504.3084A (fulltext)
the DTW template matching scheme and a traditional peak detection algorithm. Curves are generated by varying the respective threshold parameter, DTW distance, and the peak prominence. 4 RESULTS Some of the detection scenarios involving true detection of long GRBs along with typical false-positive candidates are shown in Fig. 6 . The upper panels show the correctly identified events, while the lower panels depicted certain instances when the events picked up were due to noise artefacts. After the testing and validation steps mentioned in the previous section, we carried out a blind search targeting long GRB events on the CZTI data collected from 2015 October 8 to 2018 November 7. The pipeline detected 223 probable candidates, out of which 170 were already known to be GRBs. Detailed analysis of the rest led to the discovery of 35 long GRBs candidate events along with 18 false positives. Table 1 lists these newly discovered events along with their trigger time in UTC, T 90 , 2 peak count rate, total count rate, mean background counts, and detection significance with their uncertainties. Of these, GCN alerts were issued for two such events, namely GRB180526A (Sharma, Vibhute Bhattacharya 2018a ) and GRB180603A (Sharma, Vibhute Bhattacharya 2018b ). The observed number of false triggers are higher than the expected as shown in Fig. 5 . While the origin of many of these is not well understood, some of them seem to be related to the scenario shown in Fig. 6 (lower right), where we see multiple short-duration transients all occurring within tens of seconds. Figure 6. Few detection scenarios encountered while searching for long GRBs with one-second binning. The upper panel shows the correctly identified events, while the lower panel highlights instances of contamination from non-GRB transients. The horizontal red line determines the threshold caused by the noise background level, and significant points above this are clustered using the DBSCAN algorithm. Each cluster is uniquely coloured, with the vertical line providing the cluster centre. Table 1. GRBs candidate events detected with the machine learning algorithm described in this paper. GCN circulars have been issued for the highlighted events. Certain minor modifications were necessary to make the pipeline sensitive to GRBs occurring at shorter time-scales. As these events showed a higher sensitivity to the time bins’ size, it was necessary to vary the bin size across time-scales ranging from 32 to 512 ms for the entire data chunk and select the value that maximizes the peak count in at least three of the four quadrants. The time bin’s optimal value was determined using the differential evolution (Storn 1996 ) global optimizer, which performed a gradient-free direct search across this continuous parameter space. Besides, the 3σ detection threshold used for longer GRBs had to be increased to 4.5σ to minimize contamination from the background noise. In Fig. 7 we report the triggers seen for
Paper: 2016ApJ...833...99W (fulltext)
value, as many of our fitting regions do not have enough counts to constrain either or abundance. 3. TEMPERATURE MAPS Temperature maps shown in Figure 2 were derived following the method described in Markevitch et al. ( 2000 ). We extracted six narrow-band flux images between 0.8 and 9 keV, excluding the 1.7–1.9 keV edge and 7.3–7.6 keV (possibly affected by poor subtraction of the instrumental lines). Both flux and error images were smoothed prior to deriving the temperature map. A single-temperature MEKAL model was then fitted to a set of six flux values from each pixel of the image, resulting in a smoothed temperature map. The absorption column and metal abundance were fixed to the cluster best-fit values. Two smoothing methods were used, as described below. Figure 2. (a) Variable smoothing temperature map with X-ray contours. The holes in the map are masked point sources. (b) Wavelet temperature map. The fitted images were reconstructed from component wavelet scales of 6.6, 13, 26, 53, 105, and 210 kpc. 3.1. Smoothing with the Variable-width Gaussian Kernel For this approach, the narrow-band images were smoothed using a Gaussian kernel for which the width at each image pixel is determined by surface brightness in the 0.8–4 keV band, with the goal of preserving detail in bright regions. As a reference for this smoothing method, but also as a high-quality X-ray image that shows the cluster structure on all scales and omits point sources, we used wavelet reconstruction of the 0.8–4 keV image, as described in Vikhlinin et al. ( 1994 , 1998 ). We extracted wavelet components (with the atrous kernel and scales increasing in geometric progression) on scales of 53, 105, 210, and 420 kpc (or 157, 315, 630, and 126′′). Point sources are contained in wavelet components on smaller scales than the first scale above, thus not included. These image components were then co-added with the residual image smoothed by the 840 kpc scale wavelet. This procedure retains most of the statistically significant extended structures on various angular scales. The resulting image is shown in Figure 1 (b) next to the original unsmoothed image; we will use it as a reference when discussing various features in this cluster. Based on this reference image, the narrow-band images and their corresponding error images (with point sources excised) were identically smoothed by a variable-width Gaussian. By inspecting the error in the derived temperature map, we determined that using the Gaussian smoothing radius ∝ flux, and smoothing radius between 13 and 200 kpc, achieved a balance between revealing the temperature variations and suppressing noise. The resulting temperature map is shown in Figure 2 (a). To check the validity of values shown in the map, we extracted the proper spectra in a few hot spots and a cooler spot (regions T1–T4 and T5 in Figure 2 (a), respectively) and fitted their projected temperatures in XSPEC. For T1, we obtain keV; for T2, keV; for T3, we
Paper: 2017MNRAS.465.4074P (fulltext)
low-order radial modes may be simultaneously excited (their fig.3) andbeat pulsation is, thus, possible. The linear pulsation models disprove this scenario as well. We have computed a grid of models with masses in the 0.20.4M range, with luminosities in the 2070L range and metallicity range the same as considered in Fig. 10 (other model parameters are the same as in Smolec etal. 2016 ). We note that little is known about the expected physical parameters of BEPs, as only one object has been observed so far (see, however, Karczmarek etal. 2017 ). The range of the physical parameters given above is motivated by the model study by Smolec etal. ( 2013 ) and assumes that model periods are not far from those observed in RR Lyr stars. The models assume an homogeneous envelope with chemical composition the same as in RR Lyr-type models. For the 0.2M and 0.3M models, the periods of the unstable radial modes are significantly longer than those observed for our stars. The period ratios for the 0.4M models are plotted in Fig. 11 . The models have longer periods and higher period ratios, and are far from the region in which double-periodic stars reside. Clearly, the discussed stars are not fundamental and first-overtone double-mode radial pulsators, at least, not if we assume their physical parameters are close to those expected for RR Lyr stars or BEPs (which mimic RR Lyr stars). Other combinations of radial modes also do not fit the discussed group. Figure 11. As Fig. 10 , but for BEP models of 0.4M . The different colours correspond to different metallicities, as indicated in the top panel of Fig. 10 . We note that simple triangular light curve shape is characteristic for fundamental-mode HADS stars. The Fourier parameters of these stars are similar as well (see fig.8 and figs47 in Poretti 2001 , note that the cosine convention is adopted in that paper). The periods of these stars are typically below 0.2d, however, while for our stars they are longer than 0.28d. The period ratios in the radial multi-mode HADS stars are also vastly different, see e.g. fig.10 in Pietrukowicz etal. ( 2013 ). Assuming that the dominant variability does correspond to the radial fundamental mode (the large amplitude and non-linear light curve shape make this hypothesis most plausible), the additional variability may correspond to the non-radial pulsation mode. Low-degree modes may be excited in RR Lyr models (Van Hoolst, Dziembowski Kawaler 1998 ; Dziembowski Cassisi 1999 ), preferentially in the vicinity of the radial modes. Although the frequency spectrum of such modes is very dense, their growth rates do display a local maximum close to the position of the radial modes. In particular, for 1 modes, the growth rates approach that of the radial first-overtone mode and their frequency is higher than the radial first-overtone frequency [see, e.g. fig.1 in Van Hoolst etal. ( 1998 ) or fig.1 in Dziembowski ( 2016 )]. This would lead to period ratios a bit lower than observed for
Paper: 2019ApJ...870..136L (fulltext)
near T 0 . Figure 1. Results of the AGILE -GRID standard analysis for the three Sources A (upper panel), B (middle panel) and C (lower panel) of Table 1 . Left panels: gamma-ray lightcurves above 100 MeV around the IceCube T 0 . Right panels: maps in equatorial coordinates (J2000) of gamma-ray intensity above 100 MeV in (ph cm −2 s −1 sr −1 ) corresponding to the gamma-ray detection before T 0 shown on the left. The AGILE 95% confidence level (c.l.) location contours are shown in white; the IceCube error boxes or circles are shown in yellow. A systematic error of 01 should be added to each AGILE source determination. The positions of the classified AGNs from the BZCAT Catalog (Massaro et al. 2015 ) and the FERMI -LAT 3FGL gamma-ray sources (Acero et al. 2015 ) are shown in cyan and red, respectively. The standard AGILE data analysis indicates that in all cases the peak gamma-ray emission is similarly observed within 1–2 days from T 0 . For Sources B (IC-170321) and C (IC-170922), weak gamma-ray emission is also observed over longer integration timescales that include T 0 . In particular, for the new Source B an integration of 15 days, starting from 2017 March 15 (12:00 UT), shows a detection above 4 σ with a flux . The AGILE centroid has Galactic coordinates ( l , b ) = (224.59, −10.53) ± 0.42 (deg) (95% stat. c.l.) ± 0.1 (deg) (syst.) (R.A., decl. (J2000) = (98.58, −15.08) (deg)), and is fully compatible with the IceCube centroid (see Figure 2 ). Figure 2. Left panel: AGILE -GRID intensity maps, in (ph cm −2 s −1 sr −1 ) and equatorial coordinates (J2000), centered at the position of the IceCube event IC-170321, over a long integration time of 15 days around T 0 (( T 0 − 6; T 0 + 11) days). The AGILE 95% c.l. location contour obtained with the AGILE standard analysis is shown in white, the IceCube error box in yellow. Right panel: AGILE -GRID 7 day time bin gamma-ray lightcurve ( E > 100 MeV) around T 0 , obtained from the AGILE standard analysis performed at the IC-170321 position. 2.1. Post-trial False Alarm Probability To evaluate the probability that each of these three gamma-ray sources is associated with the neutrino events by chance, we first evaluated the FAR for an AGILE QL detection per unit time δt and per unit solid angle δ Ω. As the unit time δt , we assumed the standard integration time of the QL maps ( δt = 2 days). For δ Ω, we assumed the solid angle subtended by a cone with half-aperture matching the standard circular radius of 15 used in the database search ( δ Ω ≃ 2.15 × 10 −3 sr). 22 22 See Appendix A for details of the FAR computation. We then estimated the post-trial false alarm probability P i of a random occurrence in space and time of a neutrino and a gamma-ray transient event separated by an interval Δ t and by the solid angle ΔΩ (corresponding to the angular distance Δ θ ) as (Connaughton et al. 2016 )where N i is the number of trials for a symmetric time window, is the FAR per 2 day map and per unit solid angle for
Paper: 2017A&A...601A.117G (fulltext)
90072 two more transit windows of HD 10180 b, this time at 4.5 m. No transit structure was clearly detected in the resulting light curves (Fig. 4 , bottom), and a short MCMC analysis of the data led us to conclude that these new data do not increase the significance of the 2010 tentative detection. We thus conclude that the low-amplitude structure in our Cycle 6 Spitzer light curve is probably just correlated noise of instrumental origin that is not perfectly represented by our baseline model and that can easily be modeled by a shallow enough transit profile. For Spitzer , the average amplitude of these correlated noise structures is of a few dozen of ppm (see Sect. 6), making the firm detection of a unique transit shallower than 100 ppm impossible – similarly to the one expected for HD 10180 b (see discussion in Sect. 6 and our estimated detection thresholds in Table 1 ). This so-called red noise limit can be surpassed, but only by gathering more observations of the transit window, as we did here (see also the case of HD 40307 b in Gillon et al. 2010 ). Fig. 3 Same as Fig. 1 for the light curve of 2010 Jan. 16 for HD 10180 c. Fig. 4 Top : same as Fig. 3 , except that a possible transit of HD 10180 b was included in the global model. Highest posterior probability transit model for HD 10180 b is shown in purple. The posterior PDF for the transit timing of HD 10180 b derived from an RV analysis assuming 7 planets is shown above the figure. Bottom : 2013 Warm Spitzer photometry for HD 10180 binned per 30 min, corrected for the instrumental effects, normalized, and folded on the transit ephemeris for HD 10180 b based on our tentative detection of a transit and on the results of an RV analysis assuming 7 planets (d = time from most likely mid-transit time). 5.4. HD 13808 In 2012, we analyzed the HARPS dataset for the early K-dwarf HD 13808, confirming the existence of the two planets around it with periods of 14.2 d and 53.7 d announced by Mayor et al. ( 2011 , hereafter M11). Furthermore, our analysis revealed the existence of (1) a low-frequency signal that was well-modeled with a quadratic trend, whose origins is the magnetic cycle of the star (Queloz et al., in prep.). and (2) a low-amplitude Doppler signal with a period of 1.091 d which corresponds to a planet of with an interestingly high transit probability of 20%. The false-alarm probability (FAP) derived by Systemic for this short-period planet candidate was close to 1%. Based on this small FAP and the scientific importance of this putative planet if transiting, we decided to monitor two of its transit windows with Spitzer . Unfortunately, the resulting light curves did not show any convincing transit-like structure (Fig. 5 ). Our Spitzer data were not acquired during a transit window of the planet at 14.2 d, thus keeping its transiting nature unconstrained. In 2014, we analyzed the updated HARPS dataset. This analysis confirmed the existence of the low-frequency signal of magnetic cycle
Paper: 2019ApJ...887...13F (fulltext)
than 25 (i.e., approximately 5 σ for a χ 2 with 2 degrees of freedom). We use the same criterion to compare the Band+BB and Band+BB+PL models, or the Band+CPL and Band+CPL+PL models. The spectral parameters obtained from the best models along with the C-stat values are presented in Table 1 . Often the brighter GRBs require more complex models such as SBPL + BB, Band + BB, Band + CPL + PL; etc., for fitting their spectra. Figure 2 shows the νF ν energy spectrum using the best model of spectral fit for each GRB in Table 1 . The top left panel shows the spectra fitted with Band or Band + BB and the top right panel shows the spectra fitted with SBPL or SBPL + BB. The bottom left panel shows GRB 160625B spectrum fitted with Band + BB + PL and GRB 160509A spectrum fitted with Band + CPL + PL. The bottom right panel shows the spectra of GRB 130427A, GRB 090926A, GRB 090902B and GRB 090510, all fitted with Band + PL. The shaded regions correspond to the 1 σ confidence intervals of the models. Figure 2. νF ν spectra of Fermi -LAT/GBM GRBs with known redshift resulting from the time-integrated spectral analysis over the T 90 duration. The top left panel shows the Band or Band+BB model fits, while the top right panel shows SBPL or SBPL+BB model fits. The bottom panels show more complex models along with their components. The 1 σ confidence regions of the models are shown with shades of the same color as the model lines. 3.2. Isotropic Energy Calculation We computed the isotropic radiated energy E iso in the source rest frame at a luminosity distance d L aswhereis the bolometric fluence integrated over the minimum photon energy E min = 1 keV, and the maximum photon energy E max = 10 4 or 10 5 keV. Here, N i ( E ) represents the best-fit spectral model discussed previously. The luminosity distance strongly depends on the cosmological model. Assuming a flat ΛCDM cosmology with , the d L can be expressed as We have used Monte Carlo simulations to estimate the errors on S bolo and E iso . In particular, we assume the parameters of the spectral models follow a multivariate Gaussian function. Using the covariance matrix obtained from a spectral fit, we generate 10 4 sets of random values for the parameters to calculate S bolo in Equation ( 2 ). We select 68.27% confidence intervals from the resulting distribution of S bolo values to evaluate its error. The results of S bolo and E iso are reported in Table 2 . The values of (F10) and (F10) correspond to the energy interval 1–10 4 keV, while the values of (F100) and (F100) correspond to the energy interval 1–10 5 keV. The intrinsic peak energies of the νF ν spectra are reported as in Table 2 . In all cases, . Figure 3 shows the distributions of bolometric fluence, isotropic radiated energy and peak energy of the νF ν spectra of Fermi GRBs and those in the W2016 sample. There are small differences between the bolometric fluence (F10) and (F100) for the Fermi sample (the green and red histograms, respectively,