adsabs/AstroCLIMB huggingface dataset:
Paper: Identification of Galaxy–Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning
Authors: E. A. Zaborowski, A. Drlica-Wagner, F. Ashmead, J. F. Wu, R. Morgan, C. R. Bom, A. J. Shajib, S. Birrer, W. Cerny, E. J. Buckley-Geer, B. Mutlu-Pakdil, P. S. Ferguson, K. Glazebrook, S. J. Gonzalez Lozano, Y. Gordon, M. Martinez, V. Manwadkar, J. O'Donnell, J. Poh, A. Riley, J. D. Sakowska, L. Santana-Silva, B. X. Santiago, D. Sluse, C. Y. Tan, E. J. Tollerud, A. Verma, J. A. Carballo-Bello, Y. Choi, D. J. James, N. Kuropatkin, C. E. Martínez-Vázquez, D. L. Nidever, J. L. Nilo Castellon, N. E. D. Noël, K. A. G. Olsen, A. B. Pace, S. Mau, B. Yanny, A. Zenteno, T. M. C. Abbott, M. Aguena, O. Alves, F. Andrade-Oliveira, S. Bocquet, D. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, F. J. Castander, C. J. Conselice, M. Costanzi, M. E. S. Pereira, J. De Vicente, S. Desai, J. P. Dietrich, P. Doel, S. Everett, I. Ferrero, B. Flaugher, D. Friedel, J. Frieman, J. García-Bellido, D. Gruen, R. A. Gruendl, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, K. Kuehn, H. Lin, J. L. Marshall, P. Melchior, J. Mena-Fernández, F. Menanteau, R. Miquel, A. Palmese, F. Paz-Chinchón, A. Pieres, A. A. Plazas Malagón, J. Prat, M. Rodriguez-Monroy, A. K. Romer, E. Sanchez, V. Scarpine, I. Sevilla-Noarbe, M. Smith, E. Suchyta, C. To, N. Weaverdyck
Figure caption: Examples of false-positive classes that were used to augment the network training sample. Columns correspond to (1) monocolor streaks from moving objects, (2) crowded stellar fields, (3) puffy blue galaxies near red sources, (4) disturbed/merging galaxies, (5) spirals with active star-forming regions, and (6) edge-on blue galaxies near red objects. Cutouts are approximately 11.″8 × 11.″8.
Paper: Identification of Galaxy–Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning
Authors: E. A. Zaborowski, A. Drlica-Wagner, F. Ashmead, J. F. Wu, R. Morgan, C. R. Bom, A. J. Shajib, S. Birrer, W. Cerny, E. J. Buckley-Geer, B. Mutlu-Pakdil, P. S. Ferguson, K. Glazebrook, S. J. Gonzalez Lozano, Y. Gordon, M. Martinez, V. Manwadkar, J. O'Donnell, J. Poh, A. Riley, J. D. Sakowska, L. Santana-Silva, B. X. Santiago, D. Sluse, C. Y. Tan, E. J. Tollerud, A. Verma, J. A. Carballo-Bello, Y. Choi, D. J. James, N. Kuropatkin, C. E. Martínez-Vázquez, D. L. Nidever, J. L. Nilo Castellon, N. E. D. Noël, K. A. G. Olsen, A. B. Pace, S. Mau, B. Yanny, A. Zenteno, T. M. C. Abbott, M. Aguena, O. Alves, F. Andrade-Oliveira, S. Bocquet, D. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, F. J. Castander, C. J. Conselice, M. Costanzi, M. E. S. Pereira, J. De Vicente, S. Desai, J. P. Dietrich, P. Doel, S. Everett, I. Ferrero, B. Flaugher, D. Friedel, J. Frieman, J. García-Bellido, D. Gruen, R. A. Gruendl, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, K. Kuehn, H. Lin, J. L. Marshall, P. Melchior, J. Mena-Fernández, F. Menanteau, R. Miquel, A. Palmese, F. Paz-Chinchón, A. Pieres, A. A. Plazas Malagón, J. Prat, M. Rodriguez-Monroy, A. K. Romer, E. Sanchez, V. Scarpine, I. Sevilla-Noarbe, M. Smith, E. Suchyta, C. To, N. Weaverdyck
Figure caption: Composite PNG image, shown in the two scalings used in this work (Section 2). Left: scaling 1, useful to visualize features near bright objects. Right: scaling 2, useful to visualize very faint features. Both scalings were shown side-by-side during visual inspection of the candidates. Scaling 2 is generally shown in this paper.
Paper: Identification of Galaxy–Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning
Authors: E. A. Zaborowski, A. Drlica-Wagner, F. Ashmead, J. F. Wu, R. Morgan, C. R. Bom, A. J. Shajib, S. Birrer, W. Cerny, E. J. Buckley-Geer, B. Mutlu-Pakdil, P. S. Ferguson, K. Glazebrook, S. J. Gonzalez Lozano, Y. Gordon, M. Martinez, V. Manwadkar, J. O'Donnell, J. Poh, A. Riley, J. D. Sakowska, L. Santana-Silva, B. X. Santiago, D. Sluse, C. Y. Tan, E. J. Tollerud, A. Verma, J. A. Carballo-Bello, Y. Choi, D. J. James, N. Kuropatkin, C. E. Martínez-Vázquez, D. L. Nidever, J. L. Nilo Castellon, N. E. D. Noël, K. A. G. Olsen, A. B. Pace, S. Mau, B. Yanny, A. Zenteno, T. M. C. Abbott, M. Aguena, O. Alves, F. Andrade-Oliveira, S. Bocquet, D. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, F. J. Castander, C. J. Conselice, M. Costanzi, M. E. S. Pereira, J. De Vicente, S. Desai, J. P. Dietrich, P. Doel, S. Everett, I. Ferrero, B. Flaugher, D. Friedel, J. Frieman, J. García-Bellido, D. Gruen, R. A. Gruendl, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, K. Kuehn, H. Lin, J. L. Marshall, P. Melchior, J. Mena-Fernández, F. Menanteau, R. Miquel, A. Palmese, F. Paz-Chinchón, A. Pieres, A. A. Plazas Malagón, J. Prat, M. Rodriguez-Monroy, A. K. Romer, E. Sanchez, V. Scarpine, I. Sevilla-Noarbe, M. Smith, E. Suchyta, C. To, N. Weaverdyck
Figure caption: Network training and performance diagnostics. Left: cross-entropy loss as a function of training epoch for the training and validation samples. Center: network accuracy as a function of training epoch for training and validation samples. Right: receiver operator characteristic curve for the final trained network using the test sample. The area under the curve (AUC) is 0.994.
Paper: Identification of Galaxy–Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning
Authors: E. A. Zaborowski, A. Drlica-Wagner, F. Ashmead, J. F. Wu, R. Morgan, C. R. Bom, A. J. Shajib, S. Birrer, W. Cerny, E. J. Buckley-Geer, B. Mutlu-Pakdil, P. S. Ferguson, K. Glazebrook, S. J. Gonzalez Lozano, Y. Gordon, M. Martinez, V. Manwadkar, J. O'Donnell, J. Poh, A. Riley, J. D. Sakowska, L. Santana-Silva, B. X. Santiago, D. Sluse, C. Y. Tan, E. J. Tollerud, A. Verma, J. A. Carballo-Bello, Y. Choi, D. J. James, N. Kuropatkin, C. E. Martínez-Vázquez, D. L. Nidever, J. L. Nilo Castellon, N. E. D. Noël, K. A. G. Olsen, A. B. Pace, S. Mau, B. Yanny, A. Zenteno, T. M. C. Abbott, M. Aguena, O. Alves, F. Andrade-Oliveira, S. Bocquet, D. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, F. J. Castander, C. J. Conselice, M. Costanzi, M. E. S. Pereira, J. De Vicente, S. Desai, J. P. Dietrich, P. Doel, S. Everett, I. Ferrero, B. Flaugher, D. Friedel, J. Frieman, J. García-Bellido, D. Gruen, R. A. Gruendl, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, K. Kuehn, H. Lin, J. L. Marshall, P. Melchior, J. Mena-Fernández, F. Menanteau, R. Miquel, A. Palmese, F. Paz-Chinchón, A. Pieres, A. A. Plazas Malagón, J. Prat, M. Rodriguez-Monroy, A. K. Romer, E. Sanchez, V. Scarpine, I. Sevilla-Noarbe, M. Smith, E. Suchyta, C. To, N. Weaverdyck
Figure caption: Relation between the samples of DELVE DR1 galaxies that pass the z-band magnitude cut applied by Huang et al. (2020, 2021) and the galaxy sample that passes our color–magnitude cuts adapted from Jacobs et al. (2019a).
Paper: Identification of Galaxy–Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning
Authors: E. A. Zaborowski, A. Drlica-Wagner, F. Ashmead, J. F. Wu, R. Morgan, C. R. Bom, A. J. Shajib, S. Birrer, W. Cerny, E. J. Buckley-Geer, B. Mutlu-Pakdil, P. S. Ferguson, K. Glazebrook, S. J. Gonzalez Lozano, Y. Gordon, M. Martinez, V. Manwadkar, J. O'Donnell, J. Poh, A. Riley, J. D. Sakowska, L. Santana-Silva, B. X. Santiago, D. Sluse, C. Y. Tan, E. J. Tollerud, A. Verma, J. A. Carballo-Bello, Y. Choi, D. J. James, N. Kuropatkin, C. E. Martínez-Vázquez, D. L. Nidever, J. L. Nilo Castellon, N. E. D. Noël, K. A. G. Olsen, A. B. Pace, S. Mau, B. Yanny, A. Zenteno, T. M. C. Abbott, M. Aguena, O. Alves, F. Andrade-Oliveira, S. Bocquet, D. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, F. J. Castander, C. J. Conselice, M. Costanzi, M. E. S. Pereira, J. De Vicente, S. Desai, J. P. Dietrich, P. Doel, S. Everett, I. Ferrero, B. Flaugher, D. Friedel, J. Frieman, J. García-Bellido, D. Gruen, R. A. Gruendl, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, K. Kuehn, H. Lin, J. L. Marshall, P. Melchior, J. Mena-Fernández, F. Menanteau, R. Miquel, A. Palmese, F. Paz-Chinchón, A. Pieres, A. A. Plazas Malagón, J. Prat, M. Rodriguez-Monroy, A. K. Romer, E. Sanchez, V. Scarpine, I. Sevilla-Noarbe, M. Smith, E. Suchyta, C. To, N. Weaverdyck
Figure caption: Architecture of the CNN used for initial classification of strong lens candidates. The CNN consists of three convolutional layers and two densely connected layers, with dropout layers before each dense layer and max pooling before the latter two convolutional layers. The final dense layer utilizes a softmax activation to normalize the output to the range.
Paper: Neutrino-driven Winds in Three-dimensional Core-collapse Supernova Simulations
Authors: Tianshu Wang, Adam Burrows
Figure caption: 3D volume rendering of the 17 M ⊙ model at 1.5 s and the 23 M ⊙ model at 4.0 s after bounce. Colors render the radial velocities (red, higher). The lower-velocity regions are made more translucent to show the inner structures. The black circle is the position of the PNS. Left: the 17 M ⊙ model. At this time, the shock radius on this plot is about 12,000 km and the explosive shock velocity is around 10,000 km s−1. The secondary shocks, due to the interaction of the wind with the blast ejecta, can be seen clearly on this plot where color changes from dark red to green, meaning that the radial velocity changes from above 40,000 km s−1 to below 20,000 km s−1. The secondary wind structure is cone-like in this model. Right: the 23 M ⊙ model. At this time, the shock radius is about 25,000 km from the center and the explosion shock velocity is around 7000 km s−1. The opening angle of the secondary structure in this model is significantly smaller, and it is more tube-like and distorted.
Paper: Neutrino-driven Winds in Three-dimensional Core-collapse Supernova Simulations
Authors: Tianshu Wang, Adam Burrows
Figure caption: Radial velocity vs. radius histogram of the tracer particles in the acceleration stage. Color shows the mass fraction of all wind material remaining during the acceleration phase at a given radius. Only wind matter is shown here. The tracer trajectories are plotted until they reach their maximum radial velocity, i.e., until they leave the acceleration phase. All models show clear acceleration phases with a similar shape. It can be seen that the termination speed of the wind is 15,000–40,000 km s−1, which is significantly faster than the speed of the shock. There is a second band at around zero velocity in some of the models. This is caused by wind matter interacting with infalling matter and pausing its motion for a short period of time, later to be pushed away by the winds that emerge later. The band shape in the 17 M ⊙ model looks a bit different from the others because it is a very aspherical explosion; there are two typical wind termination radii along the explosion and the accretion plume directions.
Paper: Neutrino-driven Winds in Three-dimensional Core-collapse Supernova Simulations
Authors: Tianshu Wang, Adam Burrows
Figure caption: Electron fraction evolution of the tracer particles during the acceleration phase. Color shows the mass at a given time and Y  e . Only wind matter is shown here. There is no clear trend in the evolution of Y  e . Most wind matter is ejected with Y  e  > 0.5, but there are some neutron-rich (Y  e  < 0.5) phases. This preserves the possibility of weak r-processes. The 9(a) model (not shown here) in particular, featuring imposed initial perturbations and a quick explosion launch, has fractionally more neutron-rich ejecta. It is interesting that the neutron-rich phases in the 11 and 17 M ⊙ models also manifest a fast decrease in entropy. It is possible that strong infall triggers some mixing in the PNS, which might inject more neutron-rich material into the wind-forming region.
Paper: Neutrino-driven Winds in Three-dimensional Core-collapse Supernova Simulations
Authors: Tianshu Wang, Adam Burrows
Figure caption: Mach number vs. radius histogram of the tracer particles during the acceleration phase. Color shows the mass fraction of all wind matter still in the acceleration phase at a given radius. Only wind matter is shown here. The tracer trajectories are plotted until they reach their maximum radial velocity. We see transonic bands in all the models, which is a clear signature of a wind. The bands are wider in more massive models owing to the stronger accretion plumes they experience. The longer-lasting infall contributes to further turbulence above the PNS and results in outflows having nonzero positive/negative initial radial velocities. The longer-lasting accretion plumes also decelerate the wind outflows and result in wider bands at larger radii.
Paper: Neutrino-driven Winds in Three-dimensional Core-collapse Supernova Simulations
Authors: Tianshu Wang, Adam Burrows
Figure caption: Entropy evolution of the tracer particles during the wind acceleration phase. Color depicts the mass at a given time with that entropy. Both the winds and the early ejecta are included here. The vertical white dashed line indicates the time when the minimum shock radius has reached 3000 km (the fourth wind condition we use in Section 3.2). Less massive models such as the 9(b), 9.25, and 9.5 M ⊙ models show clear increases in the wind entropy. More massive progenitor models do not show such a clear trend. This behavior can be explained by the character of late-time/post-explosion infall. Since these components increase the density in the wind region, the entropy decreases roughly as S ∝ T 3/ρ. However, none of our models have wind entropies above 80 k B per baryon, even if the infall accretion is minimal (e.g., as in the 9(b) model).
Paper: Neutrino-driven Winds in Three-dimensional Core-collapse Supernova Simulations
Authors: Tianshu Wang, Adam Burrows
Figure caption: The wind mass flow rates at 3000 km calculated by the tracer method as a function of time (left) and vs. luminosity (right) at 10,000 km for all 12 models of this study. The mass flow rates and luminosities are both averaged over 25 ms. Left: the wind mass outflow rates as a function of time. It is seen that the outflow rates for different models follow a similar decay pattern, even though their accretion rates (see Figure 6) are very different. Right: wind mass outflow rate vs. luminosity. All models evolve from right to left (high luminosity to low luminosity). The black dashed lines provide the slope of the ﹩{\dot{M}}_{\mathrm{wind}}\propto {L}^{2.5}﹩ relation. It is clear that all models approximately follow the L 2.5 relation predicted by the spherical stationary wind solutions (e.g., Duncan et al. 1986; Burrows 1987; with the extra assumption that ﹩L\propto {T}_{\nu }^{4}﹩). However, the other predicted relation ﹩{\dot{M}}_{\mathrm{wind}}\propto {M}_{\mathrm{pns}}^{-2}﹩ is not seen here, since models with higher PNS masses (such as the 23 M ⊙ model) have stronger wind outflow rates than those with lower PNS masses (such as both 9 M ⊙ models).
Paper: Neutrino-driven Winds in Three-dimensional Core-collapse Supernova Simulations
Authors: Tianshu Wang, Adam Burrows
Figure caption: The angle-averaged evolution of the 9(b), 9.25, 9.5, 11, 17, and 23 M ⊙ models. The lines are radii of constant interior mass, and the background color shows the angle-averaged entropy. In the 9(b), 9.25, 11, and 23 M ⊙ models, it is clear that the matter falls onto the PNS and stays there for a while before later being ejected; this is a clear behavior of a wind. The 9.5 and 17 M ⊙ models do not show such clear behavior, but from Figure 2 we see that there are clear wind regions in the 17 M ⊙ model. This is because the explosions are asymmetrical, so the winds and infall can happen simultaneously. The angle-averaged plot can show the wind behavior only when the mass flow rates in winds are stronger than those in accretion, which happens much later, after the emergence of winds. This is the downside of the angle-average method, and it may explain why previous works failed to see spherical winds (Müller et al. 2017; Bollig et al. 2021). Other evidence for winds on these angle-averaged plots is found in the high-entropy outflows in the background. These high-entropy outflows, starting roughly from the PNS surface, are clearly interpreted as a wind.
Paper: Neutrino-driven Winds in Three-dimensional Core-collapse Supernova Simulations
Authors: Tianshu Wang, Adam Burrows
Figure caption: The PNS features, the total luminosities, and the accretion rates for all 12 models. Left panel: average PNS radii and baryonic masses. The PNS surface is defined here to be the 1011 g cm−3 density isosurface. The PNS radii of all models evolve in a similar way, and the PNS masses stop changing after approximately the first second. Therefore, the PNS properties depicted here do not change much during the wind phase, and they are only secondary factors in the temporal evolution of winds. Right panel: the electron and antielectron neutrino luminosities measured at 10,000 km and the mass inflow rate at 100 km. These are the primary factors that influence the evolution of winds.
Paper: Neutrino-driven Winds in Three-dimensional Core-collapse Supernova Simulations
Authors: Tianshu Wang, Adam Burrows
Figure caption: Angle-averaged temperature and heating rate profiles at different times for the 9(b), 11, 23, and 60 M ⊙ models. Left: temperature profiles. At early times, the temperature profile has a peak at around 10 km. This peak moves inward as the PNS shrinks, and at the same time the central PNS temperature increases. Right: neutrino heating rate profiles. As time evolves, the gain region moves to smaller radii. At around ﹩\dot{q}=2.5\times {10}^{20}﹩ erg s−1, the slope changes owing to recombination of free nucleons into α-particles. The change of slope can be seen more clearly in the 9(b) model because it is more spherical, while in the other models the smooth profiles seen are in part due to the angle averaging.
Paper: Near-Sun In Situ and Remote-sensing Observations of a Coronal Mass Ejection and its Effect on the Heliospheric Current Sheet
Authors: O. M. Romeo, C. R. Braga, S. T. Badman, D. E. Larson, M. L. Stevens, J. Huang, T. Phan, A. Rahmati, R. Livi, S. T. Alnussirat, P. L. Whittlesey, A. Szabo, K. G. Klein, T. Niembro-Hernandez, K. Paulson, J. L. Verniero, D. Lario, N. E. Raouafi, T. Ervin, J. Kasper, M. Pulupa, S. D. Bale, M. G. Linton
Figure caption: Time series of the encounter 13 ICME event observed by PSP. (a) QTN electron (black), proton (blue), and core electron (red) density, (b) proton (blue) and alpha particle (green) bulk speed, (c) proton (blue), alpha particle (green), core electron (red), and expected proton (magenta) temperature, (d) alpha particle to proton number density ratio, (e) Alfvén Mach number, (f) proton (blue) and electron (red) plasma β, (g) proton differential energy flux spectrum with units of eV s−1 cm−2 ster eV−1, (h) RTN magnetic field with magnitude (black), (i) normalized electron pitch-angle distribution at 433 eV. The heliocentric distance of PSP in R  S  is shown at the bottom along the time axis. The light and dark orange shaded regions depict intervals when ions or alpha particles are outside of the SPAN-I FOV or energy range, respectively. The vertical dashed lines represent different time intervals when the plasma parameters display significant changes or interesting features, corresponding to the intervals in Table 1. The subinterval labels are not shown.
Paper: Near-Sun In Situ and Remote-sensing Observations of a Coronal Mass Ejection and its Effect on the Heliospheric Current Sheet
Authors: O. M. Romeo, C. R. Braga, S. T. Badman, D. E. Larson, M. L. Stevens, J. Huang, T. Phan, A. Rahmati, R. Livi, S. T. Alnussirat, P. L. Whittlesey, A. Szabo, K. G. Klein, T. Niembro-Hernandez, K. Paulson, J. L. Verniero, D. Lario, N. E. Raouafi, T. Ervin, J. Kasper, M. Pulupa, S. D. Bale, M. G. Linton
Figure caption: Time series from 17:20 to 20:15 UTC on 2022 September 5 as observed by PSP. (a) QTN (black) and core (red) electron density, (b) RTN proton velocity with magnitude (black), (c) proton (blue), alpha particle (green), core electron (red), and expected proton (magenta) temperature, (d) magnetic field elevation angle, (e) magnetic field azimuthal angle, (f) RTN magnetic field with magnitude (black), and normalized ePADs measured at (g) 433 eV and (h) 1152 eV. The magnetic field angles have been smoothed using a moving average of 5.5 minutes. The heliocentric distance of PSP in R  S  is shown at the bottom along the time axis. The vertical dashed lines represent different time intervals, corresponding to the intervals in Table 1, from t 1 to t 4a .
Paper: Near-Sun In Situ and Remote-sensing Observations of a Coronal Mass Ejection and its Effect on the Heliospheric Current Sheet
Authors: O. M. Romeo, C. R. Braga, S. T. Badman, D. E. Larson, M. L. Stevens, J. Huang, T. Phan, A. Rahmati, R. Livi, S. T. Alnussirat, P. L. Whittlesey, A. Szabo, K. G. Klein, T. Niembro-Hernandez, K. Paulson, J. L. Verniero, D. Lario, N. E. Raouafi, T. Ervin, J. Kasper, M. Pulupa, S. D. Bale, M. G. Linton
Figure caption: Polar view of the encounter 13 PSP orbit in HEEQ coordinates, where the trajectory follows a counterclockwise direction around the Sun (red) with time. The position of PSP at time t 1 is shown with the spacecraft directed toward the Sun, where the purple dashed line represents the Sun-PSP line. The portion of the orbit highlighted in orange signifies the ICME event duration from time t 1 to t 9. The eruption axis of the event is shown by the black dashed line, aligned with active region AR 13102. The shock plane and normal are shown with a red line segment and arrow, respectively. In addition, the orbital positions of SOLO, STA, and the Earth are indicated by black arrows.
Paper: Flare Duty Cycle of Gamma-Ray Blazars and Implications for High-energy Neutrino Emission
Authors: Kenji Yoshida, Maria Petropoulou, Kohta Murase, Foteini Oikonomou
Figure caption: Upper limits on the normalization parameter A  γ  of the analyzed blazars in gray as a function of the indices γ based on the nondetection of neutrino events, compared to the IceCube 90% sensitivity for scenario (1). The left and right panels show the normalization parameters with 1 week binning and 10 yr binning, respectively. When sources whose flares are all less than the sensitivity flux, the normalization parameters of A  γ  are set to 1.0. The normalization parameter of the source is adjusted so that the maximum neutrino flux of the source is equal to the sensitivity flux if the flux of at least one flare exceeds the sensitivity. Several sources with the lowest normalization parameters are plotted with red, orange, brown, and purple lines.
Paper: Flare Duty Cycle of Gamma-Ray Blazars and Implications for High-energy Neutrino Emission
Authors: Kenji Yoshida, Maria Petropoulou, Kohta Murase, Foteini Oikonomou
Figure caption: Estimated muon neutrino flare fluxes of 136 blazars (102 FSRQs, 26 BL Lac objects, and 8 BCUs) for a 1 week bin (a) and a 10 yr bin (b) of scenario (1) with A  γ  = 1.0, and γ = 1.5 as a function of ﹩\sin (\delta )﹩ where δ is the source decl. The IceCube 90% sensitivities (red solid lines) for a point source with an ﹩{E}_{\nu }^{-2}﹩ neutrino spectrum for 1 week and 10 yr are taken from Vandenbroucke et al. (2019), Aartsen et al. (2020b), respectively.