adsabs/AstroCLIMB huggingface dataset:
Paper: Rapid Quenching of Galaxies at Cosmic Noon
Authors: Minjung Park, Sirio Belli, Charlie Conroy, Sandro Tacchella, Joel Leja, Sam E. Cutler, Benjamin D. Johnson, Erica J. Nelson, Razieh Emami
Figure caption: Formation timescale and SFH of UVISTA galaxies in different parts of the UVJ diagram. Left: UVISTA galaxies (﹩\mathrm{log}({M}_{\mathrm{stellar}}/{M}_{\odot })\gt 10.6﹩ and 1.25 < z < 1.75) in the UVJ plane, color-coded by their formation timescales. The parent sample is shown as gray circles in the background. Note that the number of galaxies where formation timescales are measured (thus, color-coded) in each selected region is arbitrary, for the purpose of finding a qualitative trend. Right: the SFHs of example galaxies in different regions (highlighted as diamonds in the left panel) derived from SED fitting on photometry using Prospector. The number at the top of each SFH panel corresponds to the diamond number on the left panel. The MAP distribution is shown in a solid line, with shades including 95% posterior distribution. The horizontal bar in each panel represents the formation timescale of each galaxy. We show that galaxies in different parts of the UVJ plane have different SFHs and thus different formation timescales.
Paper: Rapid Quenching of Galaxies at Cosmic Noon
Authors: Minjung Park, Sirio Belli, Charlie Conroy, Sandro Tacchella, Joel Leja, Sam E. Cutler, Benjamin D. Johnson, Erica J. Nelson, Razieh Emami
Figure caption: Left: rest-frame UVJ color evolution of UVISTA 174150. Right: SFH from Prospector fitting. In both panels, the results of 1000 random posteriors are shown as gray lines, and the black solid line shows the MAP for visual purposes only (not used for further calculations). Each colored point in both panels indicates a time step, including time steps that go beyond the observed epoch (the redder points show the future color evolution for the next 1 Gyr). The point with a thicker edge in the left panel shows the calculated UVJ colors at the observed epoch (lookback time = 0), and the magenta star shows the UVJ colors provided by the UVISTA catalog. From the Prospector fitting results (SFH and other stellar population parameters), we measure the time it takes for a galaxy to cross the rapid quenching region and define it as the rapid quenching transition time. For UVISTA 174150, the gray histogram in the inset panel on the left shows the distribution of the transition time measured with 1000 random posteriors, and the median time for this galaxy is ﹩{t}_{\mathrm{transition}}={308}_{-45}^{+41}\,\mathrm{Myr}﹩.
Paper: Rapid Quenching of Galaxies at Cosmic Noon
Authors: Minjung Park, Sirio Belli, Charlie Conroy, Sandro Tacchella, Joel Leja, Sam E. Cutler, Benjamin D. Johnson, Erica J. Nelson, Razieh Emami
Figure caption: Top: number density of quiescent (red), PSB (orange), and rapidly quenched (blue) galaxies as a function of redshift. Bottom: growth rates of the quiescent population in red circles. The growth rates of the quiescent galaxies that can be explained by transitions from the PSB and the rapid quenching phases are plotted in orange diamonds and blue stars, respectively (percentages are displayed next to the symbols). We find that the fraction of quiescent galaxies that have gone through the rapid quenching phase increases with redshift: 4% at z ∼ 1.4, 10% at z ∼ 1.8, and 23% at z ∼ 2.2.
Paper: Rapid Quenching of Galaxies at Cosmic Noon
Authors: Minjung Park, Sirio Belli, Charlie Conroy, Sandro Tacchella, Joel Leja, Sam E. Cutler, Benjamin D. Johnson, Erica J. Nelson, Razieh Emami
Figure caption: Schematic diagram summarizing different evolutionary tracks from star-forming to quiescence in the UVJ diagram, depending on the SFHs (blue: galaxies that have a starburst followed by rapid quenching; green: galaxies having less bursty SFH; orange: galaxies that do not have a starburst but simply have rapid quenching; and red: galaxies that are slowly quenched without a starburst). The numbers in each of the colored circles in the UVJ space correspond to the stage of SFH of the numbers in the right panel. The tracks for passive evolution after quenching are plotted by dashed lines. The blue and red contours represent the distributions of massive (﹩\mathrm{log}({M}_{\mathrm{stellar}}/{M}_{\odot })\gt 10.6﹩) star-forming (C  Q  < 0.49) and quiescent (C  Q  > 0.49) galaxies, and the gray contour shows the distribution of lower-mass (﹩9.8\lt \mathrm{log}({M}_{\mathrm{stellar}}/{M}_{\odot })\lt 10.6﹩) galaxies.
Paper: Rapid Quenching of Galaxies at Cosmic Noon
Authors: Minjung Park, Sirio Belli, Charlie Conroy, Sandro Tacchella, Joel Leja, Sam E. Cutler, Benjamin D. Johnson, Erica J. Nelson, Razieh Emami
Figure caption: (a) Rapid quenching transition times of 12 UVISTA galaxies. The gray histogram shows the normalized PDF of the transition time of 1000 random posterior samples. The median timescales of 1000 random posterior samples are shown by the blue vertical lines, with a sky blue bar indicating the range from the 16th to 84th percentile. (b) Comparison between the transition time and the formation timescale (﹩{t}_{50}^{90}﹩). (c) Comparison between the quenching timescale and the formation timescale. We find that the average rapid quenching transition time of our samples is t transition ≈ 300 Myr and the transition time does not appear to be correlated with the formation or quenching timescales.
Paper: Rapid Quenching of Galaxies at Cosmic Noon
Authors: Minjung Park, Sirio Belli, Charlie Conroy, Sandro Tacchella, Joel Leja, Sam E. Cutler, Benjamin D. Johnson, Erica J. Nelson, Razieh Emami
Figure caption:  UVJ color-based identification of quiescent (red), PSB (orange), and burstier PSB/rapidly quenched (blue) galaxies in four redshift bins of equal comoving volumes. The color key on top of the figure describes how the three populations of galaxies are divided and named. The redshift range is shown at the bottom of each panel. The total number of galaxies in each redshift bin is shown in the upper left corner of each panel, and the number of galaxies in each population is displayed inside each selected region.
Paper: BatAnalysis: A Comprehensive Python Pipeline for Swift BAT Survey Analysis
Authors: Tyler Parsotan, Sibasish Laha, David M. Palmer, Amy Lien, S. Bradley Cenko, Hans Krimm, Craig Markwardt
Figure caption: The time-varying properties of the Crab Nebula. Panel (a) shows the count rate of the survey snapshots in gray, the weekly mosaic rates in blue, and the monthly mosaic calculated with the BatAnalysis code, shown in green. The survey snapshot data were obtained by querying HEASARC for data spanning the time interval of the BAT 22 month survey (Tueller et al. 2010). Panel (b) shows the detected S/N of the Crab Nebula in each binning (snapshot, weekly, and monthly). Panel (c) shows the measured flux of the Crab Nebula in each binning as well, where 5σ upper limits due to poor spectral fits are shown with downward pointing arrows. The horizontal line shows the measured flux of the Crab Nebula, 2.33 × 10−8 erg s−1 cm−2, from the BAT 157 month survey (A. Lien et. al. 2023, in preparation). Panel (d) shows the photon index, Γ, of the spectral fit, where we freeze Γ = 2.15 in the calculation of the upper limits of the flux when the spectrum is not well fit. The horizontal line shows Γ = 2.15. The vertical dashed lines in each panel denotes the start of 2005 and 2006 for reference.
Paper: BatAnalysis: A Comprehensive Python Pipeline for Swift BAT Survey Analysis
Authors: Tyler Parsotan, Sibasish Laha, David M. Palmer, Amy Lien, S. Bradley Cenko, Hans Krimm, Craig Markwardt
Figure caption: The light curve of the AGN NGC 2992 for individual survey observations (gray dots) and monthly averaged/mosaiced (green dots). Panels (a), (b), and (c) are similar to the corresponding panels of Figure 3. The vertical dashed lines in each panel denotes the start of the years 2005 and 2006 for reference. We note that we do not detect the source at a daily or monthly cadence and hence we have upper limits on the respective quantities. The upper limits were derived using a power-law spectrum with a frozen photon index of Γ = 1.9.
Paper: BatAnalysis: A Comprehensive Python Pipeline for Swift BAT Survey Analysis
Authors: Tyler Parsotan, Sibasish Laha, David M. Palmer, Amy Lien, S. Bradley Cenko, Hans Krimm, Craig Markwardt
Figure caption: Here, we show an example analysis of a BAT survey data set that contains the Crab Nebula within the BAT field of view. The survey data start as a DPH, which has units of counts for each detector in the plane. Then GTI filtering and energy corrections are made, leading to the energy-dependent DPI, which has units of count rate for each detector. The DPI is further processed to remove additional sources of noise and then it is convolved with the BAT coded mask and the detector mask to produce a sky image with units of count rate in each of the eight energy bands. Then, using batcelldetect, the sky image is processed and sources are identified. The source count rate in each energy band is calculated as well as the background count rate, allowing for spectra to be produced, energy-dependent light curves, and the S/N of the detection in each energy band.
Paper: BatAnalysis: A Comprehensive Python Pipeline for Swift BAT Survey Analysis
Authors: Tyler Parsotan, Sibasish Laha, David M. Palmer, Amy Lien, S. Bradley Cenko, Hans Krimm, Craig Markwardt
Figure caption: The comparison between the Crab Nebula monthly mosaic light curve from the 22 month analysis done by Tueller et al. (2010), shown in red, and the rates light curve for the 22 month analysis conducted with the BatAnalysis code shown in green. The vertical dashed lines denotes the start of 2005 and 2006 for reference. The maximum percentage difference between the two analyses is 2% for the month 2005 February, showing the ability of the BatAnalysis code to recover prior BAT survey analyses.
Paper: Methanol Formation through Reaction of Low-energy CH3 + Ions with an Amorphous Solid Water Surface at Low Temperature
Authors: Y. Nakai, W. M. C. Sameera, K. Furuya, H. Hidaka, A. Ishibashi, N. Watanabe
Figure caption: Schematic of the experimental setup. A: Cs+ ion source. B: Electron-impact ionizer of the low-energy molecular ion source. C: Wien filter used for mass selection. D: Deceleration-lens system. E: Quadrupole mass analyzer (QMA). F: Aluminum substrate on which the ASW film is formed. The vacuum chambers of the electron-impact ionizer, of the Wien filter, and of the main chamber were differentially evacuated through beam slits with 2 mm diameter apertures.
Paper: Methanol Formation through Reaction of Low-energy CH3 + Ions with an Amorphous Solid Water Surface at Low Temperature
Authors: Y. Nakai, W. M. C. Sameera, K. Furuya, H. Hidaka, A. Ishibashi, N. Watanabe
Figure caption: (a) Mass spectra of picking-up ions in the mass range 145–173 amu after a 3 hr irradiation of the H2O-ASW surface by ﹩{{\mathrm{CH}}_{3}}^{+}﹩ ions (solid black line), before the irradiation (dashed red line), and for the blank experiment (dashed blue line). (b) Mass spectra of picking-up ions from the D2O-ASW surface by the RIS method in the mass range 147–175 amu after a 3 hr irradiation (solid black line), before the irradiation (dashed red line), and for the blank experiment (dashed blue line).
Paper: The Advanced Spectral Library: The Evolution of Chromospheric Wind Characteristics from Noncoronal to Hybrid Giant Stars 
Authors: Krister E. Nielsen, Vladimir S. Airapetian, Kenneth G. Carpenter, Gioia Rau
Figure caption: Top: comparison of Mg II λ2796 in the GHRS and STIS spectra of γ Cru. The line profile is stronger in the ASTRAL spectrum (solid) compared with the GHRS (dashed). Bottom: asymmetric line profile in Mg II that is a characteristic of hybrid objects. Note the high-velocity wind at ∼−70 km s−1 in the GHRS spectrum (dashed) that is extending out to higher velocity in the ASTRAL spectrum (solid).
Paper: The Advanced Spectral Library: The Evolution of Chromospheric Wind Characteristics from Noncoronal to Hybrid Giant Stars 
Authors: Krister E. Nielsen, Vladimir S. Airapetian, Kenneth G. Carpenter, Gioia Rau
Figure caption: Search for evidence of hot plasma in hybrid star γ Dra (green) and the noncoronal γ Cru (red). Spectral lines indicative of temperatures from 2 × 104 to 2 × 105 K are plotted as a comparison to the hotter hybrid star α Aqr (black) with a stronger coronal signature. The hottest line observed in the M star is the C II doublet, suggesting a maximum temperature in the chromosphere of just above 2 × 104 K, while γ Dra shows signatures of a hotter chromosphere. The narrow lines in the γ Cru spectra are fluorescent lines pumped by a strong H I Lyα.
Paper: Formulating Compressive Strength of Dust Aggregates from Low to High Volume Filling Factors with Numerical Simulations
Authors: Misako Tatsuuma, Akimasa Kataoka, Satoshi Okuzumi, Hidekazu Tanaka
Figure caption: Compressive strength against volume filling factor of dust aggregates that contain ice monomers of 0.1 μm radius with different damping coefficients k n. The damping coefficients are k n = 0, 0.01, and 0.1 from dark to light colors. We adopt C v = 3 × 10−7, for k n = 0 and 0.1 because of the calculation cost. The other parameters are fiducial values in Table 1. The solid, dashed, and dotted lines show the result of a run, our corrected analytical formula (Equation (11)), and the analytical formula (Equation (9)) of Kataoka et al. (2013), respectively.
Paper: Formulating Compressive Strength of Dust Aggregates from Low to High Volume Filling Factors with Numerical Simulations
Authors: Misako Tatsuuma, Akimasa Kataoka, Satoshi Okuzumi, Hidekazu Tanaka
Figure caption: Compressive strength against volume filling factor of dust aggregates that contain ice monomers of 0.1 μm radius with different strain-rate parameters C v. The strain-rate parameters are C v = 1 × 10−7, 3 × 10−7, and 1 × 10−6 from dark to light colors. The other parameters are fiducial values in Table 1. The solid, dashed, and dotted lines show the averages of the 10 runs, our corrected analytical formula (Equation (11)), and the analytical formula (Equation (9)) of Kataoka et al. (2013), respectively.
Paper: Formulating Compressive Strength of Dust Aggregates from Low to High Volume Filling Factors with Numerical Simulations
Authors: Misako Tatsuuma, Akimasa Kataoka, Satoshi Okuzumi, Hidekazu Tanaka
Figure caption: Compressive strength against volume filling factor of dust aggregates that contain ice monomers of 0.1 μm (blue) and 1.0 μm (green) radius with different time steps. The other parameters are fiducial values in Table 1. The solid, dashed–dotted, dashed, and dotted lines show the averages of the 10 runs, the averages of the 10 runs with two times longer time steps, our corrected analytical formula (Equation (11)), and the analytical formula (Equation (9)) of Kataoka et al. (2013), respectively. We adopt C v = 3 × 10−7 for two times longer time steps because of the calculation cost.
Paper: Formulating Compressive Strength of Dust Aggregates from Low to High Volume Filling Factors with Numerical Simulations
Authors: Misako Tatsuuma, Akimasa Kataoka, Satoshi Okuzumi, Hidekazu Tanaka
Figure caption: Compressive strength against volume filling factor of dust aggregates that contain ice monomers of 0.1 μm radius with different numbers of monomers N. The numbers of monomers are N = 1024, 4096, 16,384, and 65,536 from dark to light colors. We adopt C v = 3 × 10−7, for N = 1024, 4096, and 65,536 because of the calculation cost. The other parameters are fiducial values in Table 1. The solid, dashed, and dotted lines show the averages of the 10 runs, our corrected analytical formula (Equation (11)), and the analytical formula (Equation (9)) of Kataoka et al. (2013), respectively.
Paper: Impact of Anomalous Active Regions on the Large-scale Magnetic Field of the Sun
Authors: Shaonwita Pal, Prantika Bhowmik, Sushant S. Mahajan, Dibyendu Nandy
Figure caption: Variation in the time series of the dipole moment (only for the second cycle in the simulation), with different distributions of anomalous regions (AH–J) shown in Figure 2. Panel (a) represents the dipole moment evolution of different phase distributions. The effect of AH–J regions having more density near the start of the cycle, middle of the cycle, and the end of the cycle on the dipole moment is denoted by violet, gray, and red curves, respectively. In panel (b), the pink, light blue, and dark green curves depict the dipole moment evolution for AH–J regions emerging in high-, mid-, and low-latitudinal positions, respectively. In all of these plots, green and orange curves represent H–J and AH–J, respectively, as per our standard convention. Panels (a1) and (b1), and (a2) and (b2) are the cut-out regions of panels (a) and (b), highlighted in cyan and violet, respectively. These subpanels portray the delay in time reversal near the cycle maximum and the decrement of dipole moment/polar flux at the minimum of the same cycle.
Paper: Impact of Anomalous Active Regions on the Large-scale Magnetic Field of the Sun
Authors: Shaonwita Pal, Prantika Bhowmik, Sushant S. Mahajan, Dibyendu Nandy
Figure caption: Butterfly diagrams reflecting the spatiotemporal variability of the distribution of anomalous regions. Histograms of phase distributions and latitude distributions are plotted on the top axis and right axis, respectively, in each subplot. The top panel reflects the distribution where 5% of anomalous regions are spread all over the cycle. The next panel constitutes the diversity in the emergence phase of the anomalous active regions: (1), (2), and (3) dictate the population dispersed at the initial phase, middle phase, and declining phase, respectively. The third panel denotes the high-, mid-, and low-latitude distributions (4, 5, and 6, respectively).
Paper: Impact of Anomalous Active Regions on the Large-scale Magnetic Field of the Sun
Authors: Shaonwita Pal, Prantika Bhowmik, Sushant S. Mahajan, Dibyendu Nandy
Figure caption: Time evolution of axial dipole moment over 22 yr incorporating a variation in the number and flux content of the AH–J regions only in the second cycle. Here the variation in number and flux is mentioned as percent number–percent flux. Orange (5%–5%) and dark-orange (10%–5%) curves depict the time evolution of dipole moment, with AH–J regions having 5% flux but 5% and 10% in number, respectively. Violet (10%–10%) and dark-violet (5%–10%) curves represent AH–J regions having 10% flux but 5% and 10% in number, respectively. The green curve denotes the corresponding result for 100% H–J regions. Panel (a) represents the simulation with a single realization. Panel (b) depicts the simulations with 20 random realizations for 10%–10% and 5%–10% cases.
Paper: Impact of Anomalous Active Regions on the Large-scale Magnetic Field of the Sun
Authors: Shaonwita Pal, Prantika Bhowmik, Sushant S. Mahajan, Dibyendu Nandy
Figure caption: Panel (a) shows a cartoon of a bipolar magnetic region (BMR) with different orientations and polarities in each hemisphere. Here the BMR of configuration 1 follows both Hale’s and Joy’s law (H–J region); configuration 2 represents regions that follow Joy’s law but violate Hale’s polarity law (AH–J region); configuration 3 does not follow Joy’s tilt law but obeys Hale’s law (H–AJ region); and lastly, configuration 4 disobeys both Hale’s and Joy’s law (AH–AJ region). Panel (b) shows a surface magnetic field distribution observed in a line-of-sight magnetogram from the Helioseismic and Magnetic Imager (HMI) instrument on board the Solar Dynamics Observatory (SDO). It denotes four full-disk HMI magnetograms observed in solar cycle 24. Panel (c) shows cutouts of the green box (in panel (b)) that serve as examples of the four configurations (H–J, AH–J, H–AJ, and AH–AJ regions).
Paper: Impact of Anomalous Active Regions on the Large-scale Magnetic Field of the Sun
Authors: Shaonwita Pal, Prantika Bhowmik, Sushant S. Mahajan, Dibyendu Nandy
Figure caption: Time evolution of the surface magnetic field (B  r ) for different configurations. Here configuration 1 stands for H–J bipolar magnetic region (BMR), and configurations 2, 3, and 4 indicate the anomalous regions (mentioned in Figure 1). SFT simulations are performed for these four initial configurations, and the evolved magnetic field configuration on days 1, 25, and 50 are shown in the left, middle, and right panels, respectively.
Paper: Impact of Anomalous Active Regions on the Large-scale Magnetic Field of the Sun
Authors: Shaonwita Pal, Prantika Bhowmik, Sushant S. Mahajan, Dibyendu Nandy
Figure caption: Butterfly diagram of the surface magnetic field (B  r ) for configuration 1 and configuration 2. Panel (a) represents the butterfly diagram of H–J regions. Panel (b) shows the butterfly diagram of 95% H–J regions with 5% AH–J regions. Cyan circles denote the AH–J regions distributed all over the solar cycle. The yellow, black solid, and black dashed lines in both plots depict the reversal timing of the polar field, and the positive and negative polarities of magnetic field, respectively. The blue arrow in panel (a) denotes the newly built-up polar field region, whereas the blue box indicates the opposite polarity surges for H–J BMRs. On the other hand, the shorter blue arrow in panel (b) reflects the accumulation of a lesser amount of negative flux during the end phase of the cycle. The blue box indicates the missing opposite polarity surges in the same panel.
Paper: Impact of Anomalous Active Regions on the Large-scale Magnetic Field of the Sun
Authors: Shaonwita Pal, Prantika Bhowmik, Sushant S. Mahajan, Dibyendu Nandy
Figure caption: Time variation of axial dipole moment over two solar cycles including 5% of four differently configured BMRs. Green and pink curves denote the axial dipole moment evolution of configurations 1 (H–J) and 4 (AH–AJ), respectively. Orange and blue curves represent the time evolution of dipole moment for configurations 2 (AH–J) and 3 (H–AJ), respectively.
Paper: Cosmological Constraints from the BOSS DR12 Void Size Function
Authors: Sofia Contarini, Alice Pisani, Nico Hamaus, Federico Marulli, Lauro Moscardini, Marco Baldi
Figure caption: Left panel: number density of the BOSS DR12 and MultiDark PATCHY galaxies as a function of redshift. The BOSS data are represented with blue markers, and a spline fit is depicted by the dashed line. We report with a green band the galaxy number density of the 100 PATCHY mock realizations, distinguishing with different shades the 68% and 95% confidence levels. We represent the same distribution, but for mock galaxies weighted to mimic the BOSS survey completeness in gray. Central and right panels: number counts measured from the full sample of voids identified with VIDE, computed as a function of their effective radius and with a cut R eff > 30 h −1 Mpc. The two panels represent the void size function in the redshift ranges 0.2 < z ≤ 0.45 and 0.45 < z < 0.65, in red circles with error bars for the BOSS DR12 data and in orange bands for the MultiDark PATCHY mocks. The differently shaded bands show the 68% and 95% confidence levels around the mean value of the PATCHY void number counts. Gray bands represent PATCHY void counts after rescaling to the slightly smaller volume of the BOSS catalog.
Paper: Cosmological Constraints from the BOSS DR12 Void Size Function
Authors: Sofia Contarini, Alice Pisani, Nico Hamaus, Federico Marulli, Lauro Moscardini, Marco Baldi
Figure caption: Left: covariance matrix (normalized by its diagonal components) of MultiDark PATCHY void counts, measured after the application of the cleaning procedure. The different radius bins (i, j) are divided by vertical and horizontal white lines to separate the two redshift intervals covered by the void number counts. Right: 68% and 95% 2D confidence contours obtained from the void size function model calibration. The maximum posterior values found for C slope and C offset are indicated with orange dotted lines and reported at the top of the upper and the right subpanels. The latter show the projected 1D posterior distribution of these parameters and the relative 68% uncertainty with a green shaded band.