adsabs/WIESP2022-NER huggingface dataset:
Paper: 2017ApJ...848L..13A (fulltext)
). Tanvir et al. ( 2005 ) found a stronger correlation of BATSE SGRBs with early-type than late-type galaxies, which is not expected if nearby SGRBs arise from giant magnetar flares in nearby galaxies, but is consistent with a BNS origin. GRB 170817A is clearly associated with a BNS merger, but even without the connection to GW170817, the spectrum of GRB 170817A in the GBM data strongly disfavored the BB fit expected for a giant magnetar flare. Another possible signature of a giant magnetar flare is the ringing in its tail at the NS period of a few seconds, which could be detected by GBM or by SPI-ACS for flares outside our galaxy providing it was close enough. A search for periodic or quasi-periodic emission in the GBM data for GRB 170817A (Goldstein et al. 2017 ) found no periodic modulation, providing another discriminant between SGRBs and nearby extragalactic giant magnetar flares that might be masquerading as SGRBs. 6.4. Predicted Detection Rates The intrinsic specific volumetric SGRB rate is often quoted to be around 10 Gpc (see, e.g., Guetta Piran 2006 ; Coward et al. 2012 ; Fong et al. 2015 ; with the value originating from Nakar et al. 2006 , who noted that the true rate could be much higher). However, unlike GW signals, SGRBs do not have a clear relationship between the observed distance and brightness. As discussed in the previous section, this can be due to intrinsic variations in SGRB luminosities, as well as structure in the jet. In this Section, we investigate the former scenario presenting the implications of GW170817/GRB 170817A for future GW and SGRB observations in terms of a simple standard model for the SGRB luminosity distribution. Similar interpretations for other, perhaps more elaborate, models are straightforward. We model the SGRB luminosity function as a broken power law, with a logarithmic distribution 186 186 To get the linear distribution of luminosities, both and must be increased by 1. where is the peak isotropic luminosity (in the source frame) between 1 keV and 10 MeV, and and give the power law decay below and above the break at L ⋆ . 187 187 Other studies use a smaller energy band when defining the luminosity, and this has an impact on the value of L ⋆ , although not on the slopes of the power law components. Here, we follow Wanderman Piran ( 2015 ) in using erg s −1 , , and . The other important parameter is the minimum SGRB luminosity, which determines the lower cutoff of the luminosity distribution. This is poorly constrained as only nearby low luminosity SGRBs are observable. In Wanderman Piran ( 2015 ) the minimum luminosity is taken to be erg s −1 , while other studies use values ranging from erg s −1 to erg s −1 (Regimbau et al. 2015 ). We assume a threshold value for detectability in GBM of 2 photons cm −2 s −1 for the 64 ms peak photon flux in the 50–300 keV band, which is higher than the minimum detectability value to account for the sky-dependent sensitivity of GBM. Furthermore, we model the SGRB
Paper: 2016ApJ...828...32L (fulltext)
pixels and 1's to the valid ones. This form of the 2PT function is the same as the unbiased measure described in Kleiner Dickman ( 1984 ), though we used the column density of each pixel without subtracting the mean column density. In this way, we are more sensitive to the correlation of the density field, rather than density fluctuations. We adopted a flat weighting, where all pixel pairs were assigned with an identical weight. This otherwise unbiased estimator may be biased when the scales of the lags (i.e., the spatial separation of two pixels in a pair) are comparable with the observational FOV (Kleiner Dickman 1984 ). Figure 14 shows the obtained 2PT correlation functions with the directional information averaged out. Figure 14. Normalized 2PT functions for all the sources, showing the correlation strengths as a function of spatial separation. For separations with pixel pairs of less than total pixel number, the correlation strengths are plotted with dashed lines. The gray filled region indicates approximately where the rapidly decreasing (RD) components end. The averaged 2PT correlation functions of W49A, G10.6-0.4, W33, W43-Main, and G10.2-0.3, have a common feature: a rapidly decreasing (RD) correlation strength at short lags, and a sudden transition to a shallower decrease at larger lags. The RD component corresponds to localized clumps/cores, while the shallower decreasing (SD) component comes from the broader molecular cloud. We note that all the derived 2PT correlation functions show a steep decrease in correlation strength at large lags because of the limited FOV. To avoid this bias in our analysis, we exclude the range of lags that have pixel pairs less than ∼90% of the number of total pixels. For sources like W43-south and G10.2-0.3, where the structures of column density are composed of widely scattered overdensities, the correlation strengths at shorter lags do not decrease as significantly as the correlation strengths of the more centrally condensed sources, like W49A, W33, and G10.6-0.4. They also do not show an obvious RD component at short lags. There are some bumps seen in the 2PT functions for several sources at larger lags, which may present the characteristic scales of some localized overdensities. We do not quantify these features in this paper. The average azimuthal profiles of our obtained 2PT functions are intended to capture the major similarities and differences between the target sources. We hypothesize that the steeply decreasing component and the shallower one represent structures that were created or supported with different physical mechanisms, and thereby have different characteristic spatial scales and spatial distributions. More discussion about the physical implications is deferred to Section 4.1 . For the sake of quantifying and archiving the observed 2PT functions for comparison with other observations, theories, and numerical simulations, we perform segmented linear fits of these 2PT functions in log
Paper: 2016ApJ...819...79G (fulltext)
to three and four for C nTh + C Th and C nTh + C Th + ( C )PL, respectively. In this article we reanalyzed three famous bright long GRBs (namely, GRBs 941017, 970111, and 990123) observed with BATSE on board the Compton Gamma Ray Observatory ( CGRO )—which was operating in low-Earth orbit from 1991 to 2000—in the context of the new paradigm to verify its consistency with these archival data. This article does not aim to be a full validation of the new model and relation, but rather to show that the results obtained with Fermi also hold with the data of other instruments too. It is a crucial step since it reduces the risk that instrumental calibration/systematic effects may account for the results. A comprehensive study of the new model and relation using an extensive sample of GRBs detected with multiple instruments is in progress, but it is out of the scope of the current paper. In Sections 2 and 3 we discuss the data selection criteria and our analysis methodology. In Section 4 we present our results. We will see how the view we had of those GRBs—which attracted a lot of attention in the scientific community—may be completely refreshed in the framework of the new model. 2. DATA SELECTION We focus this first article of a series on a limited sample of three famous bright long GRBs detected with BATSE. These GRBs are of particular interest because they have often been and still are cited to support or discard theoretical interpretations for GRB prompt emission. GRB 990123 was the first GRB to be simultaneously detected during its prompt phase in the γ -ray and optical bands (Akerlof et al. 1999 ). The prompt emission spectra of this burst in the keV–MeV energy range was considered to be adequately fitted to a single Band function (Briggs et al. 1999 ). A redshift estimate for the burst of 1.61 was reported by Kelson et al. ( 1999 ) and Hjorth et al. ( 1999 ). GRB 941017—which was also observed with EGRET on board CGRO —was the first burst of the pre- Fermi era for which a strong deviation at high energy from the Band function was reported (González et al. 2003 ). GRB 941017 was considered to be adequately fitted to a combination of a Band function fading with time and an additional PL remaining constant and incompatible with the synchrotron models. Unfortunately no redshift was measured for this GRB. Finally, we also analyzed GRB 970111 which was thought to be adequately fitted to a Band function but with positive values of at early times. The photometric redshift of this burst was estimated to be 0.2 z 1.4 based on its probable association with host galaxies at this redshift range (Gorosabel et al. 1998 ). Unfortunately, many fainter galaxies may lie in the same region of the sky and the association with the detected objects is not secure, which jeopardizes the validity of this photometric redshift. The light curves of GRBs 941017, 970111, and 990123 are presented in Figures 1 – 3 , respectively. Figure 1. GRB 941017 count light curves as
Paper: 2016A&A...587A..14V (fulltext)
come back to this problem in Sect. 4.4 and discuss how our findings can describe the smallest diameter of a jet. 2.2. Geometric inversion of local sources As seen in our observations, jet-like features are detected in most images and rotate with the nucleus. Similar studies of other comets have shown that jets can often be associated with specific locations on the surface, presenting a different composition and/or morphology than other areas. For instance, some areas of comet 9P/Tempel 1 would remain active and produce jets even when far into the night, hinting at sublimation of material that is much more volatile than water ice ( Feaga et al. 2007 ; Vincent et al. 2010 ). The high resolution provided by OSIRIS allows an accurate determination of active sources in principle, although the process is not straightforward. Indeed, single images provide only two-dimensional information, and one needs to combine several observations to reconstruct the true three-dimensional structures we want to investigate. To achieve this we developed two independent techniques: blind and direct inversion, as explained in the following sections. These methods are very similar to those used by Sekanina et al. (2004) and Farnham et al. (2013) in their respective studies of the jets of comets 81P/Wild 2 and 9P/Tempel 1. Another aspect of jet studies is the detailed investigation of the physics taking place in the jet itself, as cometary material arises from the surface and possibly fragments or sublimates. We focus here on the use of our inversions to understand the sources themselves better and describe their morphology, evolution, and the associated surface physical processes. Readers interested in photometric properties of dust jets from 67P are referred to Lara et al. (2015) and Lin et al. (2015) or to Vincent et al. (2013) for a numerical modeling of these features on a larger scale. 2.2.1. Blind inversion This first algorithm considers each feature in the images as an isolated jet. Although it seems obvious that a jet can be tracked from one image to another, appearance can be deceiving, and we do not impose this condition for the inversion. For each collimated structure, it is safe to assume that the dust is emitted in a plane defined by the observer (Rosetta) and the central line of the jet. A single image does not allow us to determine whether the jet is inclined toward or away from the observer. In a second step, we calculate the intersection between the jet plane and a shape model of the nucleus, oriented beforehand to match the geometric conditions of our observations. We used a shape model reconstructed by photogrammetry ( Preusker et al. 2015 ). Spacecraft and comet attitudes and trajectories were retrieved with the SPICE library ( Acton 1996 ). Each jet-plane/comet intersection defines a line of possible sources on the surface of the nucleus. For the same jet observed at different times, this technique provides different sets of source lines, but
Paper: 2015ApJ...809...84K (acknowledgments)
The simulation results were obtained on NASA’s Pleiades supercomputer at NASA Ames Research Center. This work was partially supported by NASA grants NNX10AC55G and NNH11ZDA001N-LWSCSW, and Oak Ridge Associated Universities.
Paper: 2017MNRAS.471L.135S (acknowledgments)
Acknowledgements One of the authors (KH) is indebted to Dr. T. Y. Saito for valuable discussion on the CTA sensitivity. This work is supported by the Theoretical Institute for Advanced Research in Astrophysics (TIARA) operated under Academia Sinica, and by the Ministry of Science and Technology of the Republic of China (Taiwan) through grants 103-2628-M-007-003-MY3, 105-2112-M-007-033-MY2, 105-2112-M-007-002, 103-2112-M-001-032-MY3.
Paper: 2016ApJ...817...50G (acknowledgments)
The authors are indebted to Jeff Coughlin for his expert review and suggestion to use the Kepler FFIs to evaluate false positive scenarios. E.G. was supported by the International Short Visit program of the Swiss National Science Foundation. This paper includes data collected by the Kepler mission, funding for which is provided by the NASA Science Mission directorate. Some of the data presented herein were obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California and NASA. The Observatory was made possible by the generous financial support of the W.M. Keck Foundation. This research has made use of the NASA Exoplanet Archive, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program. This research has also made use of the Aladin Sky Atlas and the SIMBAD database, developed and operated at the CDS, Strasbourg Observatory in France. Facilities : IRTF:SpeX , UH2.2m:SNIFS , Keck2:ESI/NIRC-2 .
Paper: 2021MNRAS.505.6155M (fulltext)
next section, we describe the framework developed and its features, followed by a description of the various methods implemented within it to tackle this problem. After this, we describe the data set provided for the challenge, and what additional techniques were utilized to ‘expand’ on this data set. We then go on to describe what metrics are presented by our framework, and how methods are evaluated, and compare the performances achieved with those achieved in other works. We conclude the work by describing further improvements that can be implemented in order to make gravitational lensing detection methods more efficient and more accurate. 1.1 LEXACTUM The framework developed in this work has been named Lens EXtrActor CaTania University of Malta (LEXACTUM). The first of its main features are the image augmentation techniques described in Section 2.2 which can be toggled on or off to train for a greater number of epochs without overfitting. Another feature is the modularity of the code, allowing for the rather easy development of new models, with very easy integration of new models into the pipeline. Other features include the ability to set parameters from the command line. Examples of such parameters are the data set path, whether to train a model or load one from disk, the name of the model (to train or load), the number of epochs to train for, the batch size, and whether to use image augmentation during training or not. LEXACTUM also uses a custom ‘Data Generator’, which loads and pre-processes images in batches with the CPU, while the GPU can train on the last batch of images. Apart from image augmentation during training, all images are normalized using ZScale (NOAO 1997 ). Like other components, the normalization method can be easily swapped out for other techniques. Furthermore, LEXACTUM comes with a ‘results’ package, which scores the trained models and calculates several metrics, described in detail in Section 3 . Moreover, LEXACTUM saves trained models to disk, and also provides functionality for loading trained models. Finally, the ‘visualize features’ component was created, which allows for the viewing of the feature maps at every convolutional layer that a trained model is ‘looking at’ during execution. All of the architectures described in Section 1.2.5 were implemented in LEXACTUM. All of these models were then trained from scratch, on both the Space and Ground data sets, using ZScale normalization and image augmentation, for a varying number of epochs. As a starting point, all models were trained for 5 or 10, 25 or 50, 100, and 250 epochs. After that, if, say, a particular model obtained promising results, and did not seem to be overfitting (judging by the loss and accuracy of the validation set) after 250 epochs, it would then be further trained for 500, or even 1000 epochs. 1.2 Literature review 1.2.1 Conventional methods The methods described in this subsection are not implemented in LEXACTUM, and are only presented to give
Paper: 2017ApJ...851L...7M (fulltext)
UV O vi measurements. It is thus highly unlikely that the poorer data quality of X-ray spectra contribute much to the O vi problem. XMM-Newton detection of the z = 0 O vi absorption line toward Mrk 421 is robust, with a sigma detection ( mÅ, error; Rasmussen et al. 2007 ). Moreover, the line was also independently detected by Chandra with similar strength ( mÅ; Williams et al. 2005 ), a detection, increasing the combined significance to . Thus, we cannot just dismiss this robust detection as a statistical accident. 2. Saturation. If UV O vi lines are saturated, the column density might be underestimated (Arav et al. 2003 ). For the unresolved lines, the observed equivalent width (EW) is related both to the column density and the velocity dispersion parameter (or the Doppler parameter b ). The column density and the b -parameter can be disentangled by using different transitions of the same ion, as we did in Williams et al. ( 2005 ) for the z = 0 absorption along the Mrk 421 sightline. Using the UV O vi absorption line doublet, we constrained the b- parameter tightly and found that for this value of b , the X-ray O vi line has a column density about 0.5 dex higher than the UV line. If the X-ray O vi line is saturated, the column density would be even higher. Thus the O vi discrepancy cannot be attributed to simple phenomena such as saturation in the z = 0 systems, so the solution has to lie elsewhere, and may be different for different systems. 3. Variability. Another possibility is variability, discussed by Arav et al. ( 2003 ). AGN absorption lines are known to vary, so if UV and X-ray measurements are not obtained at the same time, the measured column densities may be different. However, variability cannot be the cause of the O vi discrepancy in CGM/IGM systems that do not vary. 4. Size of the emission region. In AGNs, the size of the X-ray emission region is known to be smaller than the UV continuum size. Therefore, if part of the O vi X-ray absorption happens inside the UV continuum region, and both X-ray and UV O vi absorption takes place outside the UV emitting region, there might be a discrepancy, with X-ray O vi absorption probing a larger column density. Once again, this cannot be the cause of the O vi discrepancy in CGM/ISM systems that are far from the background quasars. 5. Excitation. In a high-temperature plasma, electron impact excitation and recombination may suppress absorption from the ground level and contribute to emission. However, the Einstein A 21 coefficient for the 2 p –2 s decay is extremely large (), which means any excited O vi atom decays immediately to the ground state. Even in a plasma with an unrealistically high collision/photoexcitation rate (), the standard Boltzmann equation only allows for the ground state to be depleted by a factor of one-half, and the observed discrepancy is much larger than this. 6. Atomic physics. The wavelengths, oscillator strengths, photoionization cross-sections of the UV O vi λλ
Paper: 2019MNRAS.486.5349D (acknowledgments)
ACKNOWLEDGEMENTS We thank the anonymous referee for comments that helped to improve this manuscript. LD, MA, and KCF acknowledge funding from the Australian Research Council (projects FL110100012 and DP160103747). LD gratefully acknowledges a scholarship from Zonta International District 24. DMN was supported by the Allan C. and Dorothy H. Davis Fellowship. MA’s work was conducted as part of the research by Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), through project number CE170100013. Part of this research was conducted at the Munich Institute for Astro- and Particle Physics (MIAPP) of the DFG cluster of excellence ‘Origin and Structure of the Universe’. This publication makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation. The GALAH survey is based on observations made at the Australian Astronomical Observatory, under programmes A/2013B/13, A/2014A/25, A/2015A/19, A/2017A/18. We acknowledge the traditional owners of the land on which the AAT stands, the Gamilaraay people, and pay our respects to elders past and present.