adsabs/WIESP2022-NER huggingface dataset:
Paper: 2019A&A...622L...9S (fulltext)
event (90% and 99% Poisson errors shown in blue). For this figure we assumed that the population in total produces 10 neutrino events (within a given time). While the selection bias is strong for sources detected with a single event, it quickly vanishes as soon as three or more events are detected from the same source. The effective densities of several source classes are indicated (compare Table 1 ). For each source class two different luminosity functions were adopted. The numbers in the upper line are for equally luminous sources, while a lognormal distribution with a width of one order of magnitude was assumed for the second line to show the impact of large luminosity fluctuations between individual sources. This variation in luminosity has the same impact on our results as using the observed distribution of gamma-ray luminosities for Fermi /LAT blazars which stretches over five orders of magnitudes (see Fig. 2 in Ajello et al. 2014 ) and also reproduces fluctuations observed between individual gamma-ray burst by the Swift Burst Alert Telescope ( Wanderman Piran 2010 ). This simplistic treatment does not account for correlations between the source redshift and luminosity, which have been observed for example in galaxy clusters ( Gruppioni et al. 2013 ). We find that the effect of the different redshift distributions and luminosity functions can be taken into account by using an effective density. The effective density is the density of a population of equally luminous sources without cosmic evolution which reproduces an equally large bias, i.e., the same median value. A similar calculation is performed in Murase Waxman (2016) , where the effective density quantifies the effect of the luminosity function, but not of the source evolution. The last column of Table 1 shows that the rather large luminosity fluctuations between individual sources reduce the effective source density by about one order of magnitude since a large fraction of the total neutrino flux is produced by a smaller number of sources. A detector that is not equally sensitive to all parts of the sky can be considered in the same way as the luminosity fluctuations since sources in the favored direction appear to be brighter. For example, if the detector is only sensitive in half of the sky, the effective density is reduced by a factor of two and the bias is correspondingly smaller. Figure 2 shows the size of the bias for different source densities assuming equally luminous sources and no cosmic evolution. The red bands show the expected neutrino flux of the sources where the dotted line indicates the median flux and the shaded area contains 90% and 99% of the probability distribution. The effective densities of the populations listed in Table 1 are indicated in Fig. 2 ; the width of the inner red band approximately reproduces the size of the 90% regions that were obtained for the different redshift distributions (see Table 1 ). Especially for sources detected with just a
Paper: 2017MNRAS.464.2039M (fulltext)
the top panel of Fig. 2 , we show the case in which the apparent periodical variability of the spin period of LMC X-4 is tentatively described with the sinusoidal function: Here, is the mean value of the source spin period over the whole observational period (in seconds), P 3 is the characteristic time-scale of the variability, T 0 is the time of phase zero, and A the amplitude of the variability ( t , T 0 , and P 3 are all in units of days). Figure 2. Tentative description of the pulse period variations in LMC X-4 with two mathematical functions: the sinusoidal one described by equa-tion ( 1 ) (top panel) and the linear one described by equation ( 2 ) (the bottom panel). See the text for details. The equation given above describes the data reasonably well, and we obtained P 3 ≃ 5090, A = 5.3 × 10 −4 s, and s. Only the first two points obtained from the SAS observatory deviate from the qualitative periodical function. It is interesting to note that only these measurements together with the one based on the HEAO-2 data were obtained during episodes of the source flaring activity. All other spin period values were measured during quieter periods in the high superorbital phase. It is thus possible that some mechanism is at work during the flares to produce changes in the observed source pulsation period. As the available observational data (Fig. 1 ) are rather sparse, we cannot exclude that the different spin-up and spin-down episodes alternate in a more complicated fashion than a simple sinusoidal trend. Therefore, we show in the bottom panel of Fig. 2 an another example in which data are described by a sequence of power laws with identical absolute values of the slope but alternated signs: In the equation above, is the period derivative, T i is the time interval preceding when the pulse-period derivative changes sign, and P i is the pulse period in the time interval T i (in the equation all time intervals are expressed in days and all spin period values in seconds). According to this model, the characteristic rate of the pulse period change would be s −1 . 4 DISCUSSION The results presented in the previous section show that LMC X-4 undergoes cyclical transitions between spin-up and spin-down phases, with torque reversals occurring roughly every ∼6.8 yr. The spin period variability could be reasonably well described by using either a sinusoidal function, which would suggest the presence of smoothly periodical variations between two values of maximum and minimum spin periods, or a slightly more complicated function, relaxing the needs of assuming a strictly periodical phenomenon. At present, given the irregular and large spacing among all data available, it is not possible to firmly distinguish among these two possibilities. Nevertheless, below we discuss briefly several possible mechanisms that could give rise to quasi-periodical torque reversals in systems like LMC X-4. Third body. Concerning the strictly periodical case (i.e. equa-tion 1 ),
Paper: 2018A&A...618A..99M (fulltext)
11 shows the relation between the position of a meteor in the image and the estimated errors in radiant. Since the pointing of the camera and the radiant of the simulated shower was set to the local zenith, the angular separation between the radiant and position of a meteor in the image corresponds to the elevation angle of the meteor. The median for the estimated errors in the speed for these 161 meteors was 0.12 km s −1 . The median of the estimated errors for 192 un-shuttered synthetic meteors, for which the residuals in magnitude Δmag were 0.5 m, was +0.01 m with a median deviation of 0.02 m. Fig. 11. Plot showing the relation between the angular separation and the estimated uncertainties in right ascension (filled circles) and declination (open circles). 5.2. Real meteor data We applied our software to three hours of double-station SPOSH image data acquired during an observing campaign held in Greece (i.e. at ∼37 ∘ N latitude ) on 12 August 2015 from 23 to 02 UT. The cameras were pointing to the zenith with their x -axis orientated to the north. The baseline between the two sites was 51.5 km. We reduced 177 meteor image pairs and determined their trajectories, velocities and heliocentric orbits. We focus on a 20 × 20 ∘ area centred at RA = 46 ∘ , Dec = 58 ∘ , close to the nominal radiant position of the Perseids (Fig. 12 ). To distinguish between Perseid and non-Perseid meteors, we performed a classification based on radiant position and speed as follows: 132 meteors were found to radiate from within this area. We determine the radiant of the Perseid shower as the median value of these radiants: RA = 45.96 ∘ and Dec = 57.77 ∘ . We assume that most of the meteors are Perseids and we find the 1 σ uncertainty in RA and Dec to be 3.29 ∘ and 2.27 ∘ , respectively. The geocentric speeds V G have a median value at 58.97 km s −1 and a median propagated error of 1.21 km s −1 (Fig. 13 ). We classify all meteors with speeds closer to this median speed than four times the median propagated error, as Perseids. In this way, we identified 71 meteors belonging to the Perseids meteor shower (Fig. 12 ). Their median speed V G was found to be 59.58 km s −1 with a median deviation of the observed uncertainties of 0.48 km s −1 . Statistical properties are given in Table 2 . As the aim of our example is to demonstrate the successful usage of our software using real data, we neglected the effect of radiant drift. Fig. 12. Geocentric radiants of 132 meteors originating close to the Perseid radiant. The ellipse defines the area occupied by Perseids. Meteors classified as Perseids according to their speed are shown as filled circles (•). All other meteors not meeting the requirements to be classified as Perseids are shown as open circles (∘). Fig. 13. Speed distribution of 132 meteors originating from an area close to the Perseid radiant. The dashed line shows computed median speed of 59.58 km s −1 . The bars in dark grey show the distribution of speeds for
Paper: 2017A&A...600A.132S (fulltext)
over several campaigns (see Raiteri et al. 2013 , and references therein). The data used in this paper come from the WEBT archive. Fig. 2 Nightly averaged -band light curves (Aug. 2008–Jan. 2016) for 0716+714 and MRK 421 from Tuorla Observatory, and for BL Lac obtained combining nightly averaged data from Tuorla (black) and the WEBT observations (violet). The light curves are not host galaxy subtracted. The reported light curves match with the monitoring. Error bars are, in most cases, smaller than symbol size. The relevant nightly averaged light curves matching with the observation span are reported in Fig. 2 . The variability of MRK 421 and BL Lac is up to 5, and for 0716+714 up to a factor 10. The shapes of the light curves are grossly similar to the -ray ones, with fractional variability , 0.30, and 0.43 for 0716+714, MRK 421, and BL Lac, respectively. 3. Search for quasi-periodicities 3.1. Methods Identifying features in periodograms is a delicate task and requires several steps, i.e. a careful modeling of the noise and an analysis of the significance with respect to the noise model. Essentially, we followed the procedure described in Vaughan (2005 , 2010) and Guidorzi et al. (2016) . Power density spectra (PDS) were derived by means of generalized Lomb-Scargle periodograms (e.g. Scargle 1982 ) and, for a cross-check, by means of a completely independent technique ( Huijse et al. 2012 , 2014 ; Protopapas et al. 2015 ) based on a metric that combines the correntropy (generalized correlation) with a periodic kernel to measure similarity among samples separated by a given period. The results obtained with the two techniques are in excellent agreement. We restricted our study to long periods, i.e. in the days interval, where the limits are driven by the length of the considered light-curves, and to avoid the very noisy high-frequency part of the PDS. All the computations are carried out by procedures coded in python 4 (version 3.5.2) language. Statistical tests and functions, as well as optimization tools, are provided by the scipy 5 (V. 0.18.1) library. Lomb-Scargle periodograms are computed by means of the astropy 6 (version 1.2.1) implementation. The so-called correntropy periodograms are computed by the P4J 7 (version 0.13) python library. Leahy normalisation was adopted for the analyses of the PDS ( Leahy et al. 1983 ) while, in the figures, we normalized the resulting PDS to be within the range ( Zechmeister Kürster 2009 ). Fig. 3 From left to right , PDS of 0716+714, MRK 421, and BL Lac from the 100 MeV–300 GeV Fermi light curves, from Aug. 2008 to Jan. 2016. Lomb-Scargle spectrum of the input time-series data is given in blue and the best fit noise spectrum in red. Single frequency 95.0% and 99.7% false alarm levels are reported by purple and yellow lines. Solid and dashed lines refer to PL and AR1 models, respectively. Fig. 4 As above from the -band light curves. The noise was modeled as a simple power law (PL) or as an
Paper: 2017ApJ...844...58P (fulltext)
to the Jet Power for Multiple-Zone Modeling of the Multiwavelength Data of Quiescent-State Emission from AP Librae The contributions of disk, broadline region (BLR), and torus radiation to the IC emission by the blob and pc-scale jet of AP Librae are considered in earlier papers. The energy of the BLR photons is blueshifted in the rest frame of the blob. The reprocessed BLR emission has been parameterized in Equation (12) of Hervet et al. ( 2015 ). The parameter values presented in Table 3 of Hervet et al. ( 2015 ) have been chosen such that at HE, the EC emission of blob-BLR origin is significant. A similar discussion is given in Zacharias Wagner ( 2016 ). Direct emissions from the disk, BLR, and torus are low as they are not detected, however, EC emission from the blob due to the reprocessed emission could be significant (depending on the parameter values chosen and the distances of the blob and jet from the black hole) after Doppler boosting. Moreover, it is discussed that, due to the outward relativistic motion of the blob and its distance from the accretion disk, the EC emission of blob electrons due to disk radiation is negligible. From Equation (A.6) of Zacharias Wagner ( 2016 ), the energy density of reprocessed emission from the dusty torus in the reference frame of the blob is . Assuming the radius of the region within which the emission is confined to be r DT = 1 pc, luminosity L DT = 10 42 erg s −1 , the Lorentz factor of the blob Γ = 7, the efficiency of reprocessing τ DT = 0.001, and the energy density in radiation u DT = 2 × 10 −8 erg cm −3 , which is much lower than the synchrotron photon energy density inside the blob, 1.28 × 10 −5 erg cm −3 . By adjusting the parameter values suitably, the energy density in reprocessed external radiation could be made even lower. Moreover, the pc jet is assumed to be located beyond the BLR and torus region, which makes EC emission insignificant. 3. Results and Discussions As shown in Figures 2 – 4 , the observed quiescent-state SED of AP Librae has been fitted by the three-zone model adopted in the present work. The optical–UV frequency data in the SED are accounted for partially by the synchrotron emission of electrons from the blob and partially by the blackbody radiation from the accretion disk. The thermal emission from the accretion disk is reproduced by assuming a Shakura–Sunyaev type disk (Shakura Sunyaev 1973 ) with a luminosity of L D = 1.3 × 10 44 erg s −1 . Incidentally, Zacharias Wagner ( 2016 ) used the same estimate for disk luminosity,The jet power requirements from each region are calculated from the expression of P jet presented in Equation ( 11 ). For the parameters used in our model, we find that the required jet power in Zone-1 is 5.7 × 10 47 erg s −1 and in Zone-2 is 3.75 × 10 47 erg s −1 . The jet power requirement in Zone-3 is 4.86 × 10 48 erg s −1 . Assuming a simple power-law proton spectrum of spectral index 2 in the energy range of 10 15 –10 18 eV, we have
Paper: 2021MNRAS.505.3295W (fulltext)
field for the IC emission, the pionic γ-ray emission in our model is not able to account for both the observed γ-ray luminosities and the spectral shapes of galaxies with low SFRs such as the SMC alone. As diffusive losses become more relevant in these low-density galaxies, this leads to a steepening of the CR proton spectra (depending on the exact energy dependence incorporated in the diffusion time-scale). This necessarily results in steeper pion-decay γ-ray spectra, which would be in conflict with the observations if this were the only relevant emission channel. Hence, a leptonic contribution to the γ-ray emission from IC scattering is indispensable in our models. One of the arguments made in the literature in order to conclude a subdominant leptonic contribution to the total γ-ray emission depends on several underlying assumptions that are only valid in the nuclear regions of starbursts, i.e. for high gas densities, magnetic field strengths, and wind velocities. The magnetic field strength is either deduced from an energy equipartition argument, or from fitting several free parameters in one-zone models (see e.g. Torres 2004 ; Lacki et al. 2010 , 2011 ; Yoast-Hull et al. 2013 ) or axisymmetric two-dimensional models (Buckman et al. 2020 ) in order to simultaneously match the observations. The former approach assumes that the kinetic energy density is equal to the magnetic energy density, . Assuming turbulent velocities of and gas densities of yields in this approach magnetic field strengths of the order of . The fast cooling of CR electrons due to synchrotron emission in these strong magnetic fields has been proposed to be one of the reasons for suppressing the possible leptonic contribution to the γ-ray emission (Lacki et al. 2011 ). In contrast, γ-ray observations of the Galactic Centre suggest that CRs might not be able to penetrate into the densest regions and thus may be far from calorimetry (Crocker et al. 2011a , b ). However, those extreme properties are at most reached in the very central region of our simulated galaxies. Additionally, the energy density of the interstellar radiation field (or its equivalent magnetic field strength, see Fig. 1 , upper panels) persists throughout the whole galaxy. The cooling of the electrons is hence not fully dominated by synchrotron cooling, enabling IC losses to be non-negligible, except in the very central regions of starburst galaxies. In particular, in the low-density ISM of dwarfs, we find that the IC emission might be able to contribute up to 40 per cent of the total γ-ray luminosity, as can be seen in Fig. 4 (middle panels). Still, our models of starburst galaxies M82 and NGC 253 (that are chosen to match the observed SFRs and L γ ) also match their observed radio luminosities at 1.4 GHz. We will address the radio emission of our simulated galaxies in Paper III in more detail. Our findings suggest that SF galaxies are not reaching the calorimetric limit, and depart from this limit even
Paper: 2017MNRAS.469.2077O (fulltext)
worth noting. Among them, there was a sporadic 10mag fireball, visible in the evening on 2005 October 28 or a similar event on November 30, with an orbit equivalent to the 00241 OUI October Ursae Minorids stream (Molau Rendtel 2009 ). Figure 1. Fireballs from the Southern Taurid shower registered on 2005 October 30 (left) and on 2005 October 31 (right) by the PFN05 Pozna station using the PAVO5 camera. Figure 2. Fireball of 15mag registered on 2005 November 4 at 20:19:42 ut with the Pegasus constellation in the background. Photograph taken by D. Dorosz in PFN09 abikw station (Praktica L2, Vivitar 28mm f /2.5, Konica VX200). Figure 3. Selection of fireballs registered during the peak of activity of the Taurids in 2015. 2.2 Observations in 2015 As in 2005, our research included solar longitudes ranging from 206 to 228. This time, the Moons phases were not as perfect as in 2005 with full Moon on October 27 and new Moon on November 11. From October 20 to 22, our stations did not provide any data because of heavy clouds. Between October 24 and November 4, good weather allowed the PFN to work at its fullest; the nights of November 57 were partially or completely cloudy but the nights of November 8 and 9 were clear in most of the country. Compared to 2005, the number of stations and the quality of our equipment increased significantly. We were able to gather data from 25 stations covering a period of time from October 20 to November 10. We used diverse observation equipment, the sets of the highest speed based on the Mintrons 12V6, which allowed us to observe meteors as faint as 34mag. For the first time, we used the digital DMK23GX236 megapixel camera and the Tamron 2.4-6mm f /1.2 lens. Such a set allows us to observe a field of 1283802 with a limiting magnitude of about 1 to 2 and a resolution of 19201200pixels with 25 frames per second. Table 2 summarizes a list of our stations active in 2015. Table 2. Basic data on the PFN stations that observed the Taurid shower maximum in 2015. In a period from 2015 October 20 to November 10, all PFN cameras registered 6970 meteors out of which we determined 719 orbits. They corresponded to different meteor showers and sporadic centres. Out of that number, 215 orbits belonged to the Southern Taurids and 39 to the Northern Taurids. Fig. 3 shows four bright Southern Taurids captured during 2015 maximum. The first phenomena of high brightness were observed on October 27 (similarly to 2005). Three bright fireballs were also seen on October 30. The night of October 31 brought two other incredibly bright phenomena. At 18:05 ut , a fireball from the Southern Taurid shower appeared over north-western Poland and its absolute magnitude amounted to 16mag. The fireball was visible across Central Europe and was registered by the PFNs network cameras, car cameras and people coincidentally photographing the close fly-by of the asteroid 2015 TB145 (see Olech etal. 2016 for a detailed description). The phenomenon lasted 5.6s
Paper: 2021A&A...651A..25B (fulltext)
infrared yielding a water production rate in the range 3.2–3.0 × 10 28 molec. s −1 for the 7–11 September period, just before our IRAM observations. Faggi et al. (2019) , also from infrared observations of H 2 O, found water production rate in the range 2.6–2.8 × 10 28 molec. s −1 for the 8 September–7 October 2018 period. We thus assumed molec. s −1 at the time of IRAM observations ( Table 3 ). Comet 41P outgassing was monitored by several observatories during its 2017 approach, including the Solar Wind ANistropies (SWAN) instrument on the SOHO spacecraft (31 March to 20 April, Combi et al. 2019 ), TRAPPIST (11 March to 24 May for OH, Moulane et al. 2018 ) and with the Nançay radio telescope (4 March–5 June). The comet was not observed at Nançay at the time of the IRAM observations because of poor OH inversion. OH production rates of ≈ 1.1 × 10 28 and 0.6 × 10 28 molec. s −1 were obtained for the 28 March–3 April and 18–30 May, respectively. Most of the observations showed a plateau of activity ±20 days around perihelion with 4, 3 and 8 × 10 27 molec. s −1 respectively. We adopted a median value of molec. s −1 for the time of our observations. For comets 64P and 38P, around the time of our observations, water production rates were estimated from ground-based spectro-photometric observations of OH. D.G. Schleicher (priv. comm.) provided molec. s −1 for 64P on 16 December 2018. For comet 38P his values were molec. s −1 on 3 and 16 December 2018. We adopted water production rates of molec. s −1 for 64P and molec. s −1 for 38P, both to compute collision rates and determine molecular abundances. 64P and 38P were only observed in September and October at Nançay. 6 Production rates and abundances Line intensities have been converted to production rates using our time-dependent non-LTE code taking into account collisions with neutrals and electrons as well as solar radiative pumping via vibrational bands for HCN, CH 3 OH, CS, H 2 CO, CO, and CH 3 CN, as used in previous papers ( Biver et al. 2000 , 2006 , 2012 , 2014 , 2015 , 2016 ). Photolysis constants are also from previous papers and detailed in Biver et al. (2021) . Production rates or upper limits are provided in Table 4 . For species with multiple lines observed at the same time, the weighted average is provided. 6.1 Relative abundances Abundances relative to water were computed using the average water production rates provided in Table 3 . For 41P we assumed a lower water production rate (0.3 × 10 28 molec. s −1 ) for the second day (1.0 May 2017) because HCN and CH 3 OH showed a significant decrease in production rate and Moulane et al. (2018) also found a decrease in the water production rate between 26.1 April and the 28 April–3 May period. For 38P, abundances relative to water assume molec. s −1 . Although no line is detected with a signal-to-noise larger than 2, the combined signal of the 13 strongest methanol lines at 165–170 GHz and 242 GHz yields a formal signal-to-noise of 3.5 and a