adsabs/WIESP2022-NER huggingface dataset:
Paper: 2021A&A...650L...8C (fulltext)
spread function of the system. Its energy corresponds to the incoming flux and is normalized to 1. At this point, it is important to note that the sensitivity is bounded. Since the mask transmission, | m |, cannot be greater than 1 (| m | ≤ 1), Eq. ( 7 ) implies that TF| f ≤ 2. Hence, given Eq. ( 8 ), we conclude that, in the frame of our normalizations, the sensitivity cannot be greater than 2: This is an important result as it defines the theoretical limit for the sensitivity of an FFWFS. 2.3. Application to the ZWFS The convolutional formalism introduced in the previous section was then applied to the ZWFS in order to find a simple formula of its sensitivity according to the mask parameters. As previously mentioned, the ZWFS mask is defined by two free parameters. The first is the depth (phase shift) of the dot δ ; for the classical ZWFS, δ = π /2. The second is its diameter p ; for the classical ZWFS, p = 1.06 λ / D . As described in N’Diaye et al. (2013) , the dot diameter value was chosen in order to get an equivalent flux inside and outside the focal plane dot. This configuration with p = 1.06 λ / D also allows a uniform reference intensity distribution to be obtained. The purpose of this section is to demonstrate that this choice of p = 1.06 λ / D is actually not optimal, and that the sensitivity can be improved with a larger dot. For the sake of clarity, we carried out this study in one dimension. As such, the spatial frequency vector f becomes the scalar frequency f . We further assumed that the weighting function ω , which corresponds to the PSF, can be described as a top-hat function of diameter radius a . This simplified geometry is summarized by Fig. 1 a. From this simplified geometry, we then computed TF| f and plot this quantity in Fig. 1 b. The full derivation can be found in Appendix B . Fig. 1. Simplified description of the ZWFS. ( a ): simplified one-dimensional framework for convolutional derivations; in black, the dot diameter is equal to p and the phase shift is equal to δ . In green, the PSF is approximated by a normalized top-hat function with a diameter a . ( b ): transfer function of the ZWFS for two cases; in red the dot size is smaller than the PSF, and in blue the dot is larger than the PSF. The optimal case appears for p = a and not p = a /2, as done for the classical ZWFS. From Fig. 1 b one can distinguish two cases. The first is p ≥ a (i.e., the dot diameter is larger than the PSF characteristic size). Frequencies above ( p + a )/2 reach the sensitivity 2sin( δ ). Frequencies below ( p − a )/2 are at 0. The second is p a : the dot diameter is smaller than the PSF size. Frequencies over p have a value of 2 p / a × sin( δ ), which is smaller than the theoretical limit of 2. From this simplified model one can first conclude that a phase shift of δ = π /2 will maximize the sensitivity, as expected. But surprisingly, it shows that for this phase shift a dot radius of p = a offers a sensitivity of the optimal
Paper: 2020MNRAS.496.4372U (fulltext)
) UVB model – scaled to the effective optical depth fitting function of Kim et al. ( 2007 ). The solid curves represent our fiducial model (purple), our model with α QSO = 1.3 (pink), with α QSO = 2.0 (black), and our model with an ionization fraction based on the quasar emissivity function of Kulkarni et al. ( 2019 , green). These curves have all been scaled by the same factor as our uniform UVB model. The effective optical depth varies up to tens of percent, with our α QSO = 1.3 model showing the largest discrepancy due to the largest variance in IGM temperature. Figure 6. The evolution of the effective optical depth of the H i Lyman α forest for three of our He ii reionization models compared with a model with a uniform UVB (dashed, teal curve). The solid purple curve shows our model with α QSO = 1.7, C He iii = 3, E max = 150 eV, and z reion,He ii = 2.8−4. The pink and black curves shows the same model, but with α QSO = 1.3 and 2.0, respectively. The light green curve shows our model based on the quasar emissivity function of Kulkarni et al. ( 2019 ). The error bars show measurements from Schaye et al. ( 2003 ), Kim et al. ( 2007 ), and Calura et al. ( 2012 ). The bottom panel shows the relative differences between our four He ii reionization models and the uniform UVB model. From our simulated H i Lyman α forest spectra, we calculate the flux PDF for our two simulations shown in Fig. 5 . Because the patchiness of reionization will result in multiple temperatures at the same gas density, the flux PDF will reflect more bimodality after He ii reionization is underway. The left-hand panel of Fig. 7 shows the flux PDF for a redshift at the onset of He ii reionization ( z = 4) and towards the end of reionization in the right-hand panel ( z = 3). The solid curve shows our model with inhomogeneous reionization and the dashed curve shows the effect of a homogeneous UVB. Indeed, in the midst of He ii reionization, we find a more bimodal distribution in comparison to our simulation without inhomogeneous He ii reionization. We compare these cases with observational measurements of the flux PDF from Kim et al. ( 2007 ) and Calura et al. ( 2012 ) at z ≈ 3. While these measurements show broad agreement with both simulations, and may slightly favour our inhomogeneous He ii reionization, the uncertainty in the measurements is nearly equal to, or greater than the difference between our model and the uniform UVB case. Figure 7. Flux PDF from a simulation with inhomogeneous He ii reionization model (solid) and from a simulation with a uniform UVB (dashed). The left-hand panel shows a redshift at the onset of He ii reionization ( z = 4) and the right-hand panel shows a redshift just before its completion ( z = 3). The error bars show measurements from Kim et al. ( 2007 ) and Calura et al. ( 2012 ). From these same simulations, we show the 1D flux power spectrum in Fig. 8 . The left-hand panel presents the flux power spectrum for a redshift at the onset of He
Paper: 2018MNRAS.480.2165A (fulltext)
study. Dashed lines show the previous (grey) and improved (blue) sensitivity limits. The cut at the bottom left corner seems to represent the increasing difficulty for detecting sources with the lowest synchrotron log() values. A possible explanation is that for low-synchrotron peak values, the IC peak may move to lower energies, hindering the detections; but also this could be connected to incompleteness of the 2WHSP sample close to the synchrotron peak cut, at ν ∼ 10 15 Hz. As reported in Chang et al. ( 2017 ) the incompleteness might be induced by the multifrequency selection criteria that was used to build the 2WHSP sample. 4.3 Notable sources The stacked γ-ray SEDs displayed in Fig. 2 present two distinct outliers, namely the sources 1BIGB J224910.6 − 130002 and 1BIGB J194356.2+211821. The source 1BIGB J224910.6 − 130002, also known as RBS 1899, is a blazar of unknown redshift classified as an EHSP due to its synchrotron peak position estimated at 10 17.5 Hz. Interestingly, the Fermi -LAT spectrum obtained with over 9.3 yr of data shows a steep decaying behaviour in log (ν) versus log (ν F ν ) space, with photon spectral index Γ ∼ 2.33 and relatively bright in γ-rays, as seen in Fig. 5(a) . However, this unusual behaviour is probably due to the averaging of the quiescent state with a series of γ-ray flares that happened along 2016 to 2017, as can be seen in the light curve reported in Fig. 5(b) . During these flaring episodes, the flux increased of ∼40 times with respect to the quiescent phase. This may also explain why the source was undetected in previous FGL and FHL catalogues. This is an important example of a γ-ray blazar that could represent a major target for the future CTA during periods of extreme activity (as detected during MJD 57662–57687, Fig. 5a in black). It also illustrates the strong impact of flaring activity in attempting a population study of extreme or faint blazars, since the most extreme γ-ray activity is smoothed over time when integrating over years of Fermi -LAT observations. Figure 5. Left plot: SED of 1BIGBJ224910.6 − 130002, showing the mean γ-ray SED obtained by integrating over 9.3yr in blue, and during the 25 d flare peak (MJD 57662.65–57687.65) in black. Right plot: Light curve of 1BIGBJ224910.6 − 130002 considering 1 month time bins, integrating over the broad-band of 900 MeV up to 500 GeV. Flux calculated only for > 3σ bins (red points), error bars, and upper limits (black cross) are calculated using Integral method considering 95 per cent of confidence level. The second peculiar source, 1BIGB J194356.2+211821 also known as HESS J1943+213, is the most extreme blazar of the 1BIGB catalogue, with log (ν p ) = 18.1. It was serendipitously discovered by the H.E.S.S. Collaboration (H.E.S.S. Collaboration et al. 2011 ) at VHE γ-rays during a galactic plane survey and later confirmed with VERITAS observations (VERITAS Collaboration et al. 2015 ). Due to the source’s position on the galactic plane and its
Paper: 2018A&A...609L...4Y (fulltext)
the significance of the migration of the giant planets was recognized, JFCs and Oort cloud comets (OCCs) were thought to have formed in distinct regions, where JFCs formed beyond Neptune and OCCs formed between giant planets ( Dones et al. 2004 ). Except for P/SW3, we see little if any systematic difference between JFCs and OCCs, even when freshly exposed interior materials were measured. In terms of chemical composition, surveys of cometary volatiles have also shown no systematic difference between JFCs and OCCs, but that significant variations are present within each group ( Mumma Charnley 2011 ; A’Hearn et al. 2012 ). As growing observational evidence reveals that JFCs and OCCs formed within the same broad region, dynamical simulations, in the framework of planetary migration, also suggest that the Oort cloud and scattered disk are derived from a common parent population, i.e., the primordial trans-Neptunian disk ( Brasser Morbidelli 2013 ). Recent measurements of HDO/H 2 O in 67P ( Altwegg et al. 2015 ) by the ROSINA mass spectrometer on board the Rosetta spacecraft, also suggest that JFCs and OCCs formed in largely overlapping regions where the giant planets are today ( A’Hearn 2017 ). However, the proportions of comets formed at different heliocentric distances are not yet clear ( Dones et al. 2015 ). Füri Marty (2015) studied the distribution of N isotopes in the solar system and found that it follows a rough trend with increased 15 N abundances at larger radial distances from the Sun. Nitriles detected in cometary comae so far are consistently enriched in 15 N by a factor of ~3 relative to the protosolar nebula and a factor of 1.8 relative to the terrestrial planets ( Füri Marty 2015 ). The significant enrichment in 15 N is unlikely to be due to primordial nucleosynthetic heterogeneities, but rather a result of isotope fractionation processes during the formation of the solar system ( Füri Marty 2015 ). Possible processes include ion-molecule reactions in the interstellar medium under cold temperature with sufficient density ( Charnley Rodgers 2002 ) or photodissociation of N 2 by UV light from the proto-Sun or nearby stars ( Muskatel et al. 2011 ). Unlike nitrogen, the available carbon isotope ratios of comets, including the newly derived 12 C/ 13 C ratio of ER61, are consistent with the solar system abundance ratio of 90 ± 10 ( Wyckoff et al. 2000 ). The in situ measurements of the CO 2 isotopologs in the coma of 67P/Churyumov-Gerasimenko, taken by the Double Focusing Mass Spectrometer (DFMS) of the ROSINA, find that 12 C/ 13 C = 84 ± 4 ( Hässig et al. 2017 ). The DFMS measurements show a slight enrichment in 13 C in terms of the 12 C/ 13 C ratio compared to an average 12 C/ 13 C ratio of 91.0 ± 3.6 ( Manfroid et al. 2009 ; Bockelée-Morvan et al. 2015 ). However, given the large uncertainties of other ground-based observations, this difference seen in the DFMS measurements of 67P is not statistically significant. Our observations
Paper: 2020ApJS..247...58W (fulltext)
gyros that have failed to date. During the decline of G2 performance, and as stability issues with G3 were encountered, the number of consecutive HST orbits without a bias update evolved from eight orbits to four in 2017 June, and finally to two orbits in early 2018, affecting multi-orbit coverage of PJs 12 and 19, as well as OPAL 2018 and 2019 observations of Jupiter. 2.7. Photometric Calibration The latest photometric calibration for WFC3/UVIS (Deustua et al. 2016 , 2017 ), called “UVIS 2.0,” includes new flat fields and normalization procedures (Mack et al. 2016 ) compared to the earlier “UVIS 1.0” calibration pipeline in use prior to 2016. The WFC3 quad filters (i.e., FQ889N for the Jupiter observations discussed here) have not been updated, so we have included an estimated 3% reduction in calibrated fluxes in quad filters, based on average changes in photometric calibrations for other filters between the UVIS 1.0 and 2.0 calibration systems. Photometric uncertainties in the UVIS 2.0 calibration are estimated to be 1.2%–1.3% (Deustua et al. 2016 , 2017 ). Calibrated HST data are images with data numbers corresponding to count rates in units of e − s −1 , and the FITS header keyword PHOTFLAM gives the inverse sensitivity factor to convert to spectral irrandiance units of erg cm −2 s −1 Å −1 . But for solar system science in the NUV–NIR range, reflectance in I / F units is commonly used. To convert the image data units of e − s −1 to I / F , we provide a FITS header keyword PHOTIF and its uncertainty, SIG_PHOT, such thatwhere Ω is the solid angle of a WFC3/UVIS pixel with default astrodrizzle parameters (0.03962 2 arcsec 2 ), and πF ⊙ is the solar spectral irradiance at Jupiter’s orbital distance at the time of the exposure within the bandpass of the spectral element. To calculate πF ⊙ , we use the Colina et al. ( 1996 ) solar spectrum, the heliocentric distance from the JPL Horizons ephemeris system, 13 13 JPL Horizons URL is https://ssd.jpl.nasa.gov/?horizons . and spectral element throughput curves available directly for download from STScI. 14 14 WFC3/UVIS throughputs are at http://stsci.edu/WFC3/UVIS/SystemThroughput or http://stsci.edu/hst/instrumentation/wfc3/performance/throughputs . The SIG_PHOT uncertainty in our I / F calibration is dominated by a systematic 5% uncertainty in the solar spectrum (Colina et al. 1996 ), but also includes a 1% error typical of photometric zero-point uncertainty (Deustua et al. 2017 ). 2.8. Temporal Coverage Although most HST observations had timing linked to Juno perijoves, there are two exceptions. Observations in the Outer Planet Atmospheres Legacy (OPAL) program were taken near solar opposition, to maximize spatial resolution at Jupiter (Simon et al. 2015 ). OPAL 2017 observations happened very close to perijove 5. A cluster of imaging observations in 2017 January were planned to coincide with a Juno perijove, under the original plan with 14 day Juno science orbits. When Juno was instead kept on
Paper: 2020MNRAS.495.1943Y (fulltext)
that discs are around half as common in binary systems closer than ∼20 au as they are around single stars or stars with wide stellar companions (Yelverton et al. 2019 ). There are competing theories for the formation of both giant planets and close binaries. The formation of close binaries may result from turbulent fragmentation of a molecular cloud, or may occur somewhat later via gravitational instability in a circumstellar disc (see e.g. Kratter 2011 for a review). Giant planets could either form via core accretion or, like close binaries, via the direct gravitational collapse of a circumstellar disc (see e.g. Mordasini et al. 2010 for a review). Thus, it is possible that giant planets and close binary companions form via essentially the same mechanism. If this were the case, then it may be expected that giant planets and close binaries have similar effects on debris discs, which is not what we observe. This could suggest that the two kinds of companion do not both form in the same way, though it could alternatively be the case that they do both form via gravitational instability but that the more massive stellar companions stir planetesimal discs more effectively and thus drive faster collisional decay. 4.4.2 Comparison with directly imaged planets Meshkat et al. ( 2017 ) found tentative evidence that giant planets at tens to hundreds of au – i.e. those probed by direct imaging – are more common in systems with detected debris discs than in those without, at the 88 per cent confidence level. While this result cannot be considered statistically significant, it is none the less suggestive that planets at separations comparable to typical debris discs have a stronger correlation with these discs than the much closer-in planets studied in this paper (recall from Table 6 that we found a difference at a lower confidence level of ∼70 per cent). The apparently contrasting results for planets on close and wide orbits could be understood in terms of a simple model in which the majority of planets form via core accretion regardless of separation, and in which RV planets like those in our study form within a few au (rather than migrating in from large semimajor axes). Within this model, detection of a planet through RV would suggest that planetesimals were able to form in the inner planetary system, while detection of a Kuiper belt analogue would indicate that planetesimals formed in the outer region of the system. However, it is not necessarily the case that the formation of planetesimals at close separations implies that the same should be possible at much larger separations (and vice versa), since, for example, the dust density in protoplanetary discs tends to decrease with stellocentric distance (e.g. Andrews et al. 2009 ), while ice particles that may aid planetesimal growth cannot exist within a few au of the star (e.g. Johansen et al. 2014 ). Additionally, the dynamical environment in the inner and outer systems may be different if undetected
Paper: 2020MNRAS.495.1760O (fulltext)
an ideal tool with which to calibrate other tracers of star formation. Finally, CO emission is the consequence of the interaction between CO molecules and molecular hydrogen (H 2 ) in regions where stars are formed – giant molecular clouds (GMCs). CO emission is correlated with the virial mass of GMCs observed in the Milky Way and in nearby spiral galaxies (Young Scoville 1991 ). Historically, the IR–RC–CO parameters have been studied using three empirical relationships, each one having different properties: first, the RC–IR correlation (e.g. Yun, Reddy Condon 2001 ; Ivison et al. 2010 ; Thomson et al. 2014 ; Magnelli et al. 2015 ; Dumas et al. 2011 ; Tabatabaei et al. 2013a , b ), valid up to z ∼ 2 and at a resolution of ≥ 500 pc. Second, the Schmidt–Kennicutt (SK) IR–CO law (Kennicutt 1998 ; Kennicutt Evans 2012 ), valid for bright galaxies with a high SFR as well as for low-surface-brightness galaxies with a low SFR. The form of the SK law is still debated because observations of disc and starburst galaxies have shown that this relationship can be fitted either by a single or by a bi-modal solution for the redshift range 0.05–2.5 (e.g. Genzel et al. 2010 ; Daddi et al. 2010 ; Ivison et al. 2011 ; Kennicutt Evans 2012 ; Tacconi et al. 2013 ; Santini et al. 2014 ; Freundlich et al. 2019 ). The slope of the SK law, which defines the depletion time of the gas in a galaxy (τ dep ≡ M gas /SFR), is central to our understanding of the mechanisms that govern star formation. According to models, a linear slope implies that star-formation activity is not driven only by the self-gravity of the galaxy (Semenov, Kravtsov Gnedin 2017 , 2019 ). Several studies dedicated to the spatially resolved KS relationship on subkiloparsec scales found a wide range in the value of the slope (indicated by the power-law index, N ∼ 0.6–3; ) of the KS relationship (Bigiel et al. 2008 ; Kennicutt Evans 2012 ; Rahman et al. 2011 ; Viaene et al. 2014 ; Casasola et al. 2015 ). The spread in the value of N may be intrinsic, suggesting that different star formation relationships exist; alternatively, it may result from the assumptions adopted in each study. Daddi et al. ( 2010 ) suggest that L IR (tracing SFR) and (tracing molecular gas mass) show no evolution in normal star-forming galaxies up to z = 1.5. However, Tacconi et al. ( 2018 ) suggest that τ dep ∝ (1 + z ) −0.62 , implying that τ dep decreases by a factor of ~2×, up to the redshift limit explored in our study, z = 1.5. Third, the RC–CO correlation (e.g. Adler et al. 1991 ; Murgia et al. 2002 , 2005 ; Leroy et al. 2005 ; Paladino et al. 2006 ; Schinnerer et al. 2013 ; Liu et al. 2015 ) is valid for a variety of galaxy types and also down to scales of a few hundred parsecs in some selected local galaxies. In this work, we derived equation ( 3 ) by combining RC–IR and IR–CO relationships from the literature to obtain the molecular gas mass density of the Universe, after deriving the integrated MGMF from measurements of
Paper: 2020ApJ...894...70L (fulltext)
them by 1000. For the labels, we normalize the total magnetic field strength by dividing it by 5000 and the inclination and azimuth angles by dividing them by π . The two numbers, 1000 and 5000, are used here because most of the Stokes measurements have values between −1000 and +1000, and their total magnetic field strengths range from −5000 to +5000 G. After obtaining the estimated vector magnetic field, which is inferred by our trained model, of a test data sample (pixel), we can derive the three Cartesian components of the magnetic field, namely, B x , B y , and B z , of the pixel as follows:where B total denotes the total magnetic field strength, φ is the inclination angle, and θ is the azimuth angle. 3. Methodology We use a CNN to infer vector magnetic fields from Stokes profiles of GST/NIRIS. Our CNN model helps in denoising inversions by exploiting the spatial information of the Stokes profiles. Figure 2 presents the architecture of our network. It contains an input layer, three convolutional blocks, two fully connected layers, and an output layer. The input layer receives a sequence of Stokes Q , U , and V components, each having 60 wavelength sampling points, with three channels. Each channel corresponds to a Stokes component. Figure 2. Architecture of our CNN. This network is comprised of an input layer, three convolutional blocks, two fully connected layers, and an output layer. The input of the CNN is a three-channel sequence of Stokes Q , U , and V components each having 60 wavelength sampling points. The intermediate outputs of the three convolutional blocks have 64, 128, and 256 channels, respectively. There are 1024 neurons activated by ReLUs in both of the two fully connected layers. The output layer has three neurons activated by the Tanh function, where each neuron produces a value in the range (−1, 1) representing the total magnetic field strength, inclination angle, and azimuth angle, respectively. After the input layer, there are three convolutional blocks with the following structures. The first convolutional block consists of two convolutional layers that take, as input, the output from the previous layer and filter it with 64 kernels of sizes 3 × 1 × 3 and 3 × 1 × 64, respectively, and a max-pooling layer with a pooling factor of 2. The second convolutional block consists of two convolutional layers with filters of 128 kernels of sizes 3 × 1 × 64 and 3 × 1 × 128, respectively, and a max-pooling layer with a pooling factor of 2. The third convolutional block consists of two convolutional layers with filters of 256 kernels of sizes 3 × 1 × 128 and 3 × 1 × 256, respectively. The third convolutional block does not contain a max-pooling layer. The activation functions used in both the convolutional and fully connected layers are rectified linear units (ReLUs; Goodfellow et al. 2016 ), defined asThe output of the three convolutional blocks is flattened into a sequence, which is then sent to the two fully connected layers