adsabs/WIESP2022-NER huggingface dataset:
Paper: 2018ApJ...863L..30A (fulltext)
by the two profiles provided by the IceCube Collaboration, instead of an electromagnetic light curve. The first one (denoted in the following as “Gaussian flare’) is modeled by a Gaussian signal centered on MJD 57004 and with a standard deviation σ = 55.0 days. We considered a period ±5 σ wide, corresponding to 550 flaring days. The second one (referred to as “box flare”) assumes a box-shaped flare starting at MJD 56937.81 and ending at MJD 57096.21, corresponding to 158.40 flaring days. The time-dependent analysis first defines the values of selection cuts that, assuming a power-law neutrino spectrum for the signal, optimizes the Model Rejection Factor. Three different spectral indexes for the neutrino spectrum have been considered ( γ = 2.0, 2.1, and 2.2), and the cut parameters to be optimized are the maximum reconstructed neutrino zenith angle, θ , and the quality of reconstructed tracks, Λ. The resulting optimized set of cuts for the three considered spectral indexes are cos θ > −0.15 and Λ > −5.6. For comparison, the corresponding cuts for the time-integrated search reported in Section 3 are and Λ > −5.2. This means that the time-dependent analysis allows for more downward-going neutrino candidates and tracks with a lower reconstruction quality parameter; refer to Figure 2 of Albert et al. ( 2017a ). According to this set of cuts, the expected background during the box flare has been estimated as 0.04 (4) events within 05 (5°) from the source. This is a factor ∼4 larger than that determined for the time-integrated search. The background increases because a larger fraction of atmospheric muons is mis-reconstructed and because more low-energy atmospheric neutrinos are included. Thanks to the reduced observational time window, the method allows the presence of lower energy cosmic neutrinos and de facto corresponds to an analysis sensitive to a softer neutrino energy spectrum. After the optimization, the data have been unblinded. The results are compatible with the expectation from the atmospheric background, and no signal has been found during either of the considered flares. Within 2° from the source, 10 background events are expected during the analyzed period, while 13 events have been found in data. None of them lie within either of the two considered flaring periods. From these null results, 90% C.L. upper limits have been derived for the neutrino flux. For an E −2.0 ( E −2.1 ) [ E −2.2 ] spectrum they correspond to a normalization factor of GeV −1 cm −2 s −1 for the Gaussian-shaped period. The energy range containing the 5%–95% of the detectable flux is 2.0 (1.3) [1.0] TeV–3.2 (1.6) [1.0] PeV. For the box-shaped period, the flux normalization factors are a factor of 3.3 higher. 5. Conclusions The GCN circular (IceCube Collaboration 2017 ) on the EHE neutrino candidate event, IC170922A, promptly triggered a search for neutrino candidates in the ANTARES online data stream. The position of the IC170922A event at the location of the
Paper: 2017ApJ...836...94W (acknowledgments)
We are grateful to NASA and their SSERVI node, the Center for Lunar and Asteroid Surface Science at UCF. Special thanks go to our research collaborators: IGEP (Germany) and the Center for Microgravity Research at UCF. This material is based upon work supported by NASA under grant no. NNX09AB85G, issued through the Origins of Solar Systems Program, and grant no. NNA14AB05A, issued through the Solar System Exploration Research Virtual Institute, Space Florida, and by the National Science Foundation under grant no. AST-1413332.
Paper: 2018A&A...619A.115R (acknowledgments)
Acknowledgements We gratefully acknowledge the Programme National de Planétologie (telescope time attribution and financial support) of CNRS/INSU and the Swiss National Science Foundation for their support. We warmly thank the OHP staff for their support on the 1.93 m telescope. This work has been carried out in the frame of the National Centre for Competence in Research “PlanetS” supported by the Swiss National Science Foundation (SNSF). J.R. acknowledges support from CONICYT-Becas Chile (Grant No. 72140583). S.C.C.B. acknowledges support from Fundaçāo para a Ciência e a Tecnologia (FCT) through national funds and by FEDER through COMPETE2020 by these grants UID/FIS/04434/2013 POCI-01-0145-FEDER-007672 and PTDC/FIS-AST/1526/2014 POCI-01-0145-FEDER-016886; and also acknowledges support from FCT through Investigador FCT contracts IF/01312/2014/CP1215/CT0004. Finally, we thank the referee for their thorough review and highly appreciate the comments and suggestions, which contributed to improving this publication.
Paper: 2016MNRAS.462..395D (acknowledgments)
MDC acknowledges the contribution of the FP7 SPACE project ASTRODEEP (Ref. No. 312725), supported by the European Commission. DAGH was funded by the Ramón y Cajal fellowship number RYC-2013-14182 and he acknowledges support provided by the Spanish Ministry of Economy and Competitiveness (MINECO) under grant AYA-2014-58082-P. FD acknowledges support from the Observatory of Rome.
Paper: 2020MNRAS.493.2318B (fulltext)
photometric redshifts, we can constrain cosmology, in particular measure σ 8 at high redshifts. Since the galaxy power spectrum constrains σ 8 × b gal in the linear regime, we need to add at least another measurement. The possibilities include: galaxy overdensity–weak lensing correlations; constrain the bias itself using counts-in-cells distributions (Szapudi Pan 2004 ; Repp Szapudi 2019 ), and then fit σ 8 ; compute higher order statistics, going to three-point correlations instead of using two-point correlations only (e.g. Pan Szapudi 2005 ). In summary, we found that within the context of Planck concordance cosmology, a linear bias model adequately explains the clustering of galaxies at z ≃ 3–6.5. With 10 times more data in the near future and better redshift calibration, the H20 survey will produce high-redshift constraints on cosmological parameters and galaxy formation. APPENDIX A: AN ANALYSIS OF MAGNIFICATION BIAS The galaxy overdensity that is observed in magnitude-limited galaxy samples can be modulated by an effect known as magnification bias. Foreground matter density can enhance (or diminish) observed source counts via weak gravitational lensing, by pushing sources above (or below) the detection limit. Here, we provide an analysis of how magnification bias affects our results. First, we give a short summary of how the modelling described in Section 3 changes due to the inclusion of magnification bias. The formulae are based on the works of Hui, Gaztañaga Loverde ( 2007 ), Loverde et al. ( 2008 ), Ziour Hui ( 2008 ), and Joachimi Bridle ( 2010 ), refer to these for more theoretical details. Instead of measuring the galaxy overdensity directly, the overdensity we observe can be written as where O denotes observed, and μ denotes the magnification contribution. Accordingly, the empirical spherical autocorrelation power spectrum of this observed overdensity becomes , and the theoretical model for it is The g μ and μμ terms can be computed through the analogues of equation ( 6 ), and where we introduced the kernel function for lensing magnification Above, H 0 is the Hubble constant at present time, Ω m denotes the cosmological mass density parameter, c is the speed of light, is the slope of the galaxy number count function at the limiting magnitude m 0 , and finally is the lensing weight function. Again, we adopt the Limber approximation (Limber 1953 ) to speed up the computation, yielding and Examining the components of in equation ( A2 ), it is clear that scales with (see equation 9 ), scales with b gal , and does not depend on the galaxy bias. We evaluated the theoretical expressions via sphericosmo , and performed the linear galaxy bias fit on the dropout samples using our fiducial EAZY-new-sm Π ( z ) distribution. The results are b gal, g = 3.89 ± 0.29, b gal, r = 8.44 ± 0.55, and b gal, i = 11.93 ± 2.00 (statistical error only) for the three dropout galaxy samples. These values are indistinguishable from the original EAZY-new-sm
Paper: 2021MNRAS.502.3085G (fulltext)
minor mergers are common in the local Universe (e.g. see Frenk et al. 1988 ; Carlberg Couchman 1989 ; Lacey Cole 1993 ; Gao et al. 2004 ; Fakhouri Ma 2008 ; Jogee et al. 2009 ; Kaviraj et al. 2009 ), therefore the findings of this paper are in apparent tension with the high frequency of the bar incidence in nearby galaxies. In addition, a galaxy might undergo multiple minor merger events during their entire lifetime (e.g. see Hopkins et al. 2009 ); thus, making the bar weakening event more inevitable. However, in reality, the situation is different as a galaxy might be accreting cold gas (e.g. Birnboim Dekel 2003 ; Kereš et al. 2005 ; Dekel Birnboim 2006 ; Ocvirk, Pichon Teyssier 2008 ; Cornuault et al. 2018 ) either during the mergers or at a later stage and this, in turn, could rejuvenate the bar (e.g. see Semelin Combes 2002 ; Bournaud et al. 2005 ; Combes 2008 ; Marino et al. 2011 ). Indeed, recent observational studies have pointed out such indication of bar rejuvenation event (e.g. Barway Saha 2020 ). Hence, the bar-weakening scenario during a minor merger event as shown in this paper is more appropriate for early-type disc galaxies, which are in general gas poor (e.g. Young Scoville 1991 ). The fraction of galaxies hosting bars decreases from the late-type gas-rich disc galaxies to early-type gas-poor disc galaxies (e.g. see Nair Abraham 2010 ). The bar fraction in disc galaxies tends to reach their minimum values for the lenticular/S0 galaxies (e.g. see Aguerri et al. 2009 ; Buta et al. 2010 ; Nair Abraham 2010 ; Barway, Wadadekar Kembhavi 2011 ). Thus, the recent minor merger events which are dry to a large extent can be a plausible explanation for the absence of strong bars in the early-type galaxies. 6.3 Other issues Here, we discuss a few points relevant for this work. First of all, the bulges present in the GalMer simulations are spherical and initially non-rotating. Previously, Saha Gerhard ( 2013 ) investigated the impact of a rotating classical bulge on the bar growth and the kinematics of the resulting boxy/peanut bulge. Their simulation of isolated galaxies showed that the bar strength decreases in cases with rotating bulges but at the end of 3 Gyr (see their Fig. 2 ), the bar strength with a rotating bulge (one with the maximum rotation) are within 10 per cent of the non-rotating case. In Fig. 5 , we compared the isolated runs and the one with the minor mergers. It appears that the minor merger events can reduce the bar strength nearly by a factor of two (at the end of 3 Gyr) from what it was in the isolated case. Given our understanding of the impact of rotating bulges on the bar strength, it might not overwhelm the impact of minor mergers. However, a detailed analysis and new simulations need to be run to understand and quantify this effect in the context of minor mergers. Secondly, the GalMer suite of simulations make use of the ‘hybrid particle’ scheme to represent the interstellar gas. This approach of gradually
Paper: 2016A&A...590A..16D (acknowledgments)
We would like to thank A. L. Varri and D. Heggie for helpful suggestions and M. Trenti for useful conversations about the topics addressed in this paper. We are also grateful to S. Degl’Innocenti and to P. G. Prada Moroni for discussions related to the properties of stellar populations in globular clusters. A.Z. thanks M. Gieles and V. Hénault-Brunet for many interesting discussions. This work was partially supported by the Italian MIUR. A.Z. acknowledges financial support from the Royal Society (Newton International Fellowship).
Paper: 2015A&A...577A..86L (acknowledgments)
We want to thank Dr. William Grundy for his useful comments that helped to improve this manuscript. V.L. and J.L. gratefully acknowledge support from the Spanish “Ministerio de Economía y Competitividad” (MINECO) projects AYA2012-39115-C03-03 and ESP2013-47816-C4-2-P. Based on observations made with the William Herschel Telescope operated on the island of La Palma by the Isaac Newton Group in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofísica de Canarias.
Paper: 2019MNRAS.486.1424A (acknowledgments)
ACKNOWLEDGEMENTS We are grateful to Shane Davis, Kent Yagi, and Hang Yu for useful conversations. We thank the referee, Andreas Reisenegger, for a careful report, and specifically for pointing out an error in our original calculation of the absorption coefficient. This research was supported by NASA grant NNX14AB40G.
Paper: 2019A&A...627A.140A (fulltext)
components. We first performed a maximum likelihood analysis over the full time range considered here, that is 2008 Aug. 04 15:43:36.000 UTC to 2018 Sep 01 00:00:00.000 UTC (MET 1 239557417 to 557452805). In the initial fit, we modeled the ROI sources adopting the spectral shapes and parameters reported in the 3FGL catalog. We fit as free parameters the normalization and spectral index of the target source, and the normalization of all sources within 5° of the ROI center, in addition to the isotropic diffuse and Galactic components. Since our data set more than doubles the integration time with respect to the 3FGL catalog, we looked for potentially new sources, present in the ROI, with an iterative procedure. We produced a Test Statistic (TS) map. The TS is defined as 2log( L / L 0 ) where L is the likelihood of the model with a point source at a given position, and L 0 is the likelihood without the source. A value of TS = 25 corresponds to a significance of 4.2 σ , when accounting for two degrees of freedom ( Mattox et al. 1996 ). A TS map was produced by including a test source at each map pixel and evaluating its significance over the current model. We looked for significant (TS > 25) peaks in the TS map, with a minimum separation of 0.3°, and added a new point source to the model at the position of the most significant peak found, assuming a power-law spectrum. We then fit again the ROI, and produced a new TS map. This process was iterated until no more significant excesses were found 2 . We also performed a best-fit position localization for the target source and all new sources with TS > 25 found in the ROI. In order to investigate the temporal variability of PKS 0346−27, we computed several light curves with different time binning (see Sect. 3.1 ). In each light curve, we performed a full likelihood fit in each time bin. The number of free parameters in the fit depends on the statistics in each bin. We first attempted a fit leaving the normalisation and index of the target source and the normalization of all sources in the inner 3° of the ROI free to vary. If the fit did not converge or resulted in a non-detection (TS 25), we progressively restricted the number of free parameters in the fit in an iterative fashion. First we reduced the radius including sources with free normalization to 1°, then we left only the spectrum of the target source free to vary, and finally we fixed all parameters to their average values except for the target source’s normalization. We consider the target source to be detected if TS > 9 in the corresponding bin, and the signal-to-noise ratio (that is, flux over its error, or F /Δ F ) in that bin is larger than two. If this is not the case, we reported a 95% confidence upper limit. 2.2. Swift-XRT/UVOT PKS 0346−27 was observed with the Neil Gehrels Swift Observatory ( Gehrels et al. 2004 ) as a Target of Opportunity (ToO), following the detection of the first γ -ray flare ( Angioni 2018 ). The Swift data were
Paper: 2016MNRAS.456.3282E (fulltext)
perpendicular to the reconnection plane, and 0 ε 1 the peakedness parameter of the current concentration in the magnetic islands. Subsequent numerical simulations have shown that, for a large range of S δ , the coalescence growth rate is much greater than the tearing growth rate (Pritchett Wu 1979 ) and that it depends critically on the value for ε (Bhattacharjee, Brunel Tajima 1983 ), linear tearing corresponding to ε = 0. The coalescence instability is characterized by two time-scales associated with its distinct phases (Kliem 1995 ). During the ideal phase, the current loops approach each other on a hydromagnetic time-scale, whereby the length-scale is given by the separation distance of paired current loops λ C , with δ ≲ λ C ≲ λ max . In general, , yet no magnetic energy is dissipated in the process. During the resistive phase, when the current loops merge, ‘anti-reconnection’ occurs in between the approaching islands. The reconnection rate is enhanced by the external driving forces of the converging current loops, such that in general . Moreover, for strongly peaked current concentrations (ε → 1), we have . For cm, the coalescence time-scale becomes comparable to the magnetospheric light crossing time, i.e. ms. Coalescence following tearing converts the bulk of the free magnetic energy in the current sheet, such that the island growth phase may act as mere prelude to the explosive energy release of merging current loops (Leboeuf, Tajima Dawson 1982 ). Its rapid development and ability to convert a significant fraction of magnetic energy argue in favour of coalescence, rather than tearing, as an explanation for the impulsive phase of flares (Tajima, Brunel Sakai 1982 ; Sakai Ohsawa 1987 ; Tajima et al. 1987 ; Kliem 1995 ; Schumacher Kliem 1997 ). The observed giant flare emission may therefore be a proxy of the non-linear, rather than the linear, tearing phase. Furthermore, for higher values of and σ m , a non-linear process known as ‘impulsive bursty reconnection’ may occur, whereby a cycle of slow tearing, rapid coalescence, current sheet thinning, and further secondary tearing (i.e. the plasmoid instability) at an increased rate repeats successively (Leboeuf et al. 1982 ; Priest 1985 ; Uzdensky, Loureiro Schekochihin 2010 ; Takamoto 2013 ). Consequently, energy is released during separate coalescence events in a fragmentary and quasi-periodic manner. This process is advanced to explain the periodic temporal fine structure of HXR emission and coherent drifting radio bursts associated with discrete (bidirectional) electron beams observed during the impulsive phase of solar flares (Aschwanden et al. 1995 ; Kliem, Karlicky Benz 2000 ; Karlicky 2004 ). QPOs of ν ∼ 10 2 Hz, which might be associated with separate energy injections, have also been detected during the initial phases of the magnetar giant flares: see Section 2.2 . These distinct energy surges may be interpreted as quasi-periodic peaks in coalescence rates, 13 resulting from
Paper: 2021MNRAS.500..603G (fulltext)
6.4 Quadrupole artefacts In Fig. 13 , we display the quadrupole for all the multiple quality configuration for the GM and NN:Uniform algorithms, respectively. At the same time we show the samples including only the misidentification shift along the line of sight (dashed lines) and the samples including also the shift due to peculiar galaxy velocities (solid lines). In general, we find similar trends to the monopole. In particular, the lower the quality of a given set of line profiles, the larger is the clustering suppression along the line of sight (and the more positive the quadrupole becomes). However, the quadrupole appears to be more sensitive to the quality of the observed line profiles. In fact, for a given algorithm and quality configuration, similarly to the ideal case, the suppression in the quadrupole extents to larger scales than in the monopole. Figure 13. Quadrupole of LAE samples with (solid) and without (dashed) the peculiar motion of galaxies. The plot structure is the same as Fig. 12 . Here the black dashed line indicates zero. This corresponds to the analysis of the snapshot at redshift 3.0 using the Thin Shell. In detail, the quadrupole of the LAE samples for GM (Fig. 13 ) exhibits a significant suppression of the clustering along the line of sight, which increases its amplitude. As well as in the monopole, the quadrupole of LAE samples using the GM algorithm is only slightly affected by the quality of the line profile. In fact, for most of the {Δλ pix , , } combinations, the quadrupole remains almost unchanged. Although, for the worst combination of Δλ pix and studied here, the amplitude of the quadrupole correlates with on scales smaller than . Moreover, NN:Uniform (Fig. 13 ) exhibits the best performance over most of the {Δλ pix , , } volume covered here. In contrast to the monopole, where the NN:Uniform algorithm provided a measurement without a suppression above for , the quadrupole convergence scale for this is extended to in general. In agreement with the monopole, the clustering suppression along the line of sight in this algorithm depends strongly in the quality of the line. In this way, the suppression gets larger as the quality gets lower. 7 DISCUSSION 7.1 Relation between <inline-formula><tex-math id="TM0456" notation="LaTeX"></tex-math></inline-formula> stack line profile and galaxy properties As studied in Section 3 , our model reproduces the trends observed between Δλ and different galaxy properties. Observational studies have split the stacked line profile as function of several galaxy properties. For example, Guaita et al. ( 2017 ) studied the stacked spectrum of LAEs with spectroscopic observations from VIMOS ULTRA-DEEP SURVEY (VUDS; Le Fèvre et al. 2015 ; Tasca et al. 2017 ). Their sample consisted on 76 galaxies between redshifts z = 2 and z = 4, exhibiting both and C iii ] (1908 Å) as emission lines. The galaxy systemic redshifts were determined by the observed wavelength of the C iii ] lines. Then,