adsabs/AstroCLIMB huggingface dataset:
Paper: A Value-added COSMOS2020 Catalog of Physical Properties: Constraining Temperature-dependent Initial Mass Function
Authors: Vadim Rusakov, Charles L. Steinhardt, Albert Sneppen
Figure caption: Diagram of specific star formation rate (sSFR) as a function of IMF temperature T IMF for several redshift windows in the COSMOS2020 (top row; provided here) and COSMOS2015 surveys (bottom; from Sneppen et al. 2022). The bins are colored by median stellar mass ﹩\mathrm{log}({ \mathcal M })﹩. The black contours show the sample density for reference. Bins with ≥10 samples are filled, while the bins with 1–9 samples are shown as scatter. The gray-shaded area shows the range where the temperatures cannot be recovered. The densest regions are populated with the typical main-sequence galaxies, which appear to be driven by the coupling of sSFR and IMF temperature. The two tails with characteristically different sSFR − T IMF relationships from the typical main sequence appear to be quiescent (low T IMF) and active (high T IMF). These distinct galaxy regions show a smooth gradient of stellar mass, which together with the declining sSFR and T IMF suggests a clear evolutionary sequence. The sample shown at the top excludes outliers resulting from spurious SED fits (see Appendix A). According to the synthetic tests performed in Sneppen et al. (2022), the best-fit IMF properties cannot be trusted at T IMF ≲ 20 K. Therefore, subsamples at T IMF ≈ 10–20 K that are present in most panels here cannot be trusted.
Paper: A Value-added COSMOS2020 Catalog of Physical Properties: Constraining Temperature-dependent Initial Mass Function
Authors: Vadim Rusakov, Charles L. Steinhardt, Albert Sneppen
Figure caption: The observed fraction of the quiescent galaxies as a function of stellar mass defined as ﹩\mathrm{log}({{\rm{\Psi }}}_{\mathrm{QS}}/[{{\rm{\Psi }}}_{\mathrm{SF}}+{{\rm{\Psi }}}_{\mathrm{QS}}])﹩ for a stellar masses derived with a best-fit IMF (left) and standard stellar masses form the COSMOS2020 catalog (right). The uncertainties include Poisson error in star-forming and quiescent galaxy counts. Stellar mass ﹩\mathrm{log}({ \mathcal M })﹩ is limited according to completeness levels at different redshift for star-forming and quiescent galaxies (Weaver et al. 2022b). At every redshift, the most-massive galaxies complete their evolution first, and the lowest-mass galaxies take the longest time to evolve. The evolution from the high- to low-mass end progresses smoothly with redshift. Overall, this picture is consistent with the effect of downsizing. Notably, for the properties with the best-fit IMF, this effect appears consistently at all redshift bins—even at z > 1—unlike in some of the previous studies of stellar mass functions (see the text for details). These properties (left) show a systematically higher fraction of quiescent galaxies in the most-massive tail than inferred from the standard property set (right). This difference comes from the differential change in the stellar mass, where the star-forming functions are shifted toward lower masses, while the quiescent ones mostly remain unchanged (this comparison is shown in Appendix B).
Paper: A Value-added COSMOS2020 Catalog of Physical Properties: Constraining Temperature-dependent Initial Mass Function
Authors: Vadim Rusakov, Charles L. Steinhardt, Albert Sneppen
Figure caption: All galaxies are plotted in the space of the star-forming main sequence (SFMS) in redshift windows from z = 0.2–2.0. The color corresponds to the median IMF temperature in a bin of stellar mass and SFR (﹩\mathrm{log}({ \mathcal M })﹩, log(SFR)). Bins with ≥10 samples are filled, and bins with 1–9 samples are shown as scatter. Typical star-forming galaxies are found on the main sequence, showing a gradient of IMF temperatures correlated with the sSFR; quiescent galaxies turn off the sequence and extend to lower SFR at the highest stellar masses and lowest IMF temperatures; objects with the highest IMF temperatures at the low stellar mass end exhibit a distinct temperature relationship from the typical main sequence (the temperature relationship is shown in Figure 7).
Paper: A Value-added COSMOS2020 Catalog of Physical Properties: Constraining Temperature-dependent Initial Mass Function
Authors: Vadim Rusakov, Charles L. Steinhardt, Albert Sneppen
Figure caption: Density diagrams showing the validation of photometric redshifts z phot obtained with the best-fit IMFs. Top panel: comparison of redshifts obtained with the Chabrier IMF at 20 K (W22) against the spectroscopic redshifts z spec from Jin et al. (2023) (1773 objects). Middle panel: redshifts obtained with the Kroupa IMF at the best-fit temperature against the spectroscopic sample. Right panel: comparison of the photometric redshifts z phot obtained with the Kroupa IMF at the best-fit temperature and with the Chabrier IMF at 20 K (189,231 objects). The fraction of higher than 1σ outliers is indicated as η. Overall, the redshifts constrained with the new SED templates appear to be consistent with the standard COSMOS2020 templates and the spectroscopic sample.
Paper: A Value-added COSMOS2020 Catalog of Physical Properties: Constraining Temperature-dependent Initial Mass Function
Authors: Vadim Rusakov, Charles L. Steinhardt, Albert Sneppen
Figure caption: Counts of all galaxies in the bins of photometric redshift and stellar mass. The points show 95% mass completeness limits estimated by rescaling down to the detection limit in IRAC Channel 1 for star-forming and quiescent. These limits ﹩{\mathrm{log}}_{10}{ \mathcal M }(z)﹩ were fitted with a Schechter function and shown as solid lines. The uncertainties are taken as the median errors in bins of redshift and rescaled mass. The levels for star-forming galaxies are below the limits of quiescent galaxies by 0.2–0.3 dex at 0 < z < 2, which reflects the higher ﹩{ \mathcal M }/L﹩ ratio for the passive population.
Paper: A Value-added COSMOS2020 Catalog of Physical Properties: Constraining Temperature-dependent Initial Mass Function
Authors: Vadim Rusakov, Charles L. Steinhardt, Albert Sneppen
Figure caption: Diagram of sSFR as a function of temperature for galaxies at 0.2 < z < 2.0. The grid-like pattern with the vertical stripe at a temperature 47–48 K and the horizontal stripe at −12.5 < sSFR < −11.5 yr−1 was flagged as outliers. They likely originate due to the highest-mass IMF break being covariant with the star formation history.
Paper: A Value-added COSMOS2020 Catalog of Physical Properties: Constraining Temperature-dependent Initial Mass Function
Authors: Vadim Rusakov, Charles L. Steinhardt, Albert Sneppen
Figure caption: Stellar mass functions for star-forming (top) and quiescent (bottom) galaxies at different redshift windows in 0.2 < z < 2.0. The standard stellar mass has been converted from a Chabrier to Kroupa IMF to match the properties in the value-added catalog. Most masses of active galaxies have been shifted toward lower values as a result of using the best-fit IMF. On the other hand, the masses of the passive galaxies remain similar to the properties with the Milky Way IMF. This differential change in stellar mass results in a systematic change of the quiescent galaxy fraction as a function of the mass, which implies a sharper effect of mass downsizing (see Figure 6).
Paper: A Value-added COSMOS2020 Catalog of Physical Properties: Constraining Temperature-dependent Initial Mass Function
Authors: Vadim Rusakov, Charles L. Steinhardt, Albert Sneppen
Figure caption: Distributions showing K  S  magnitude number histogram (left) and density (right) of the data set after each selection or quality cut. The sizes of the data set are indicated in the brackets. The magnitude observable is used to cross-check that the selections do not introduce significant bias in the data.
Paper: Rubin Observatory’s Survey Strategy Performance for Tidal Disruption Events
Authors: K. Bučar Bricman, S. van Velzen, M. Nicholl, A. Gomboc
Figure caption: Recovered TDE light curves simulated with the observing strategy baseline_nexp2_v1.7_10yrs. Each column shows two examples of light curves, which passed the metric requirements of the prepeak (left), some_color (center), and some_color_pu (right) metrics. The upper row shows the “worst” light curve of all the recovered light curves, with the lowest light-curve quality score (i.e., the number of data points in a time interval between 30 days before and 100 days after the peak), while the bottom row shows the “best” example, with the highest light-curve quality score. For example, the light-curve quality scores of the “bad” light curve detected with the some_color_pu metric is 0.085, with an average time interval between two observations of 23 days, while the light-curve quality score of the “good” TDE detected with the some_color_pu metric is 0.67, with an average time between two subsequent observations of 3 days.
Paper: Rubin Observatory’s Survey Strategy Performance for Tidal Disruption Events
Authors: K. Bučar Bricman, S. van Velzen, M. Nicholl, A. Gomboc
Figure caption: Sample input TDE light curves for the TDEs_pop metric. Rubin LSST magnitudes are plotted for PS1-10jh (top panels) and iPTF16fnl (bottom panels), at redshifts of z = 0.05 (left), z = 0.1 (center), and z = 0.2 (right). The LSST single-epoch flux limit in the r band is ≈24.5. As such, each of these light curves can in principle be detected for a few months.
Paper: Rubin Observatory’s Survey Strategy Performance for Tidal Disruption Events
Authors: K. Bučar Bricman, S. van Velzen, M. Nicholl, A. Gomboc
Figure caption: Metric output values or the fraction of TDEs (out of injected ones) passing the metric requirements for two-color TDEspop metrics: some_color (purple), some_color_pu (pink), and the quality metric output for the some_color_quality (blue) and some_color_pu_quality (green) metrics are shown for a variety of observing strategies. We show results for survey strategy simulations that are part of the FBS v1.5 (baseline, var_expt, third_obs, wfd_scale, footprint, u60, alt, roll_mod2) and FBS v1.7 (baseline_nexp, pair_times, footprint1-8, u_long, rolling) releases. The metric outputs are normalized to the baseline observing strategy of the corresponding survey strategy release. The lines connecting the values of each metric are shown for representative purposes only and do not imply any trend from one strategy to another.
Paper: Rubin Observatory’s Survey Strategy Performance for Tidal Disruption Events
Authors: K. Bučar Bricman, S. van Velzen, M. Nicholl, A. Gomboc
Figure caption: Metric values for 12 science cases as a function of filterdist family simulations. In the left panel, we plot the metric values normalized to filterdist_indx2, to show the significant effect of a larger number of u-band observations on the TDE some_color_pu metric. In the right plot, we show the same values, but zoomed in, to make the variations between metrics more obvious (similar to Figure 19 in Jones et al. 2020). We see a very large increase of the detection efficiency for TDEs (and other blue transients), compared to a much smaller change for the metrics of the other science cases.
Paper: Rubin Observatory’s Survey Strategy Performance for Tidal Disruption Events
Authors: K. Bučar Bricman, S. van Velzen, M. Nicholl, A. Gomboc
Figure caption: Fraction of TDEs passing the some_color metrics in the baseline_nexp2_v1.7_10yrs observing strategy by their type (“fast and faint” TDE or iPTF16fnl and “normal” TDE or PS1-10jh) and redshift.
Paper: Rubin Observatory’s Survey Strategy Performance for Tidal Disruption Events
Authors: K. Bučar Bricman, S. van Velzen, M. Nicholl, A. Gomboc
Figure caption:  some_color_pu metric value as a function of number of u-band visits in the filterdist family of survey strategy simulations. The metric value scales linearly with the number of observations in u band.
Paper: Deciphering the Morphological Origins of X-shaped Radio Galaxies: Numerical Modeling of Backflow versus Jet Reorientation
Authors: Gourab Giri, Bhargav Vaidya, Christian Fendt
Figure caption: A schematic diagram representing the ambient medium (the elliptical shaded region) implemented in our simulations along with the line-of-sight visualization angle (θ, ϕ) and the adopted models for XRG formation. Panel (a) represents the ambient cluster medium configuration with respect to the Cartesian axes and has a size of 400 kpc3. Panel (b) depicts the backflow model, in which the jet only moves along the y-direction and the backflowing material moves in the other lateral directions. Panel (c) shows the jet-reorientation scenario, in which the jet flips to an angle of 90° from the y (major) to the x (minor) direction or the vice versa, depending on the case under consideration. Panel (d) represents the same situation as panel (c), but here, the jet reorients to an angle of 70°. For visualization simplicity, we confine the jet reorientation activity to the z = 0 plane.
Paper: Deciphering the Morphological Origins of X-shaped Radio Galaxies: Numerical Modeling of Backflow versus Jet Reorientation
Authors: Gourab Giri, Bhargav Vaidya, Christian Fendt
Figure caption: Representation of the evolution of a prominent XRG structure formed in the case qr90_YX (fast jet-reorientation scenario; Table 1). The top three rows represent an x − y slice of the density, pressure, and y-component velocity distribution of the evolving structure, respectively. The last row shows the 3D volume-rendered plot showing the intricate jetted structure as viewed along the z-axis. The corresponding evolution time is highlighted at the top of each column. The color bar for the diagrams is kept at the rightmost part and is the same for the associated row, presented in computational unit (multiply the unit density (0.01 amu cm−3), unit pressure (1.5 × 10−5 dyn cm−2), and velocity (c) for physical scales). See Section 3.1 for details.
Paper: Deciphering the Morphological Origins of X-shaped Radio Galaxies: Numerical Modeling of Backflow versus Jet Reorientation
Authors: Gourab Giri, Bhargav Vaidya, Christian Fendt
Figure caption: Representation of structures at 114 Myr that appear similar to the case qr90_YX (Figure 3). The associated simulation names are labeled in the leftmost part of the figure, referring to the backflow (top) and slow jet-reorientation cases (bottom; Table 1). The color bar for the plots is the same as in Figure 3. See Section 3.2 for details and observational relevance.
Paper: Deciphering the Morphological Origins of X-shaped Radio Galaxies: Numerical Modeling of Backflow versus Jet Reorientation
Authors: Gourab Giri, Bhargav Vaidya, Christian Fendt
Figure caption: We demonstrate here three scenarios of a jet reorientation at 114 Myr from the x-axis to the y-axis in the 3D configuration. The individual simulation labels are kept in the leftmost part of the diagram. All of these instances create an active lobe along the major axis and a pronounced wing along the minor axis of the ambient medium, typically observed in XRGs. The color bar for each map is kept at the same level as in Figure 3. See Section 3.2 for details and observational relevance.
Paper: Deciphering the Morphological Origins of X-shaped Radio Galaxies: Numerical Modeling of Backflow versus Jet Reorientation
Authors: Gourab Giri, Bhargav Vaidya, Christian Fendt
Figure caption: Quantitative representation of the structural evolution formed through various models (labeled at the top). Panel (a) depicts volume evolution of the growing cocoon in relation to a volume of sphere 200 kpc, demonstrating how much of the ambient cluster is perturbed. Panels (b) and (c) show the mass momentum and total energy evolution of the same emerging structure. All of the images near 78 Myr show a slightly distinct evolutionary tendency in reorientation scenarios that is due to a considerable turbulent mixing of the jet-ambient material (see Section 4 for details).
Paper: Deciphering the Morphological Origins of X-shaped Radio Galaxies: Numerical Modeling of Backflow versus Jet Reorientation
Authors: Gourab Giri, Bhargav Vaidya, Christian Fendt
Figure caption: Evolution of different components of momentum (ρ v  x , ρ v  y , and ρ v  z ;  absolute values), showing the dependence of the quantitative variation on different jet evolutionary processes (or formation models). See Section 4 for details.
Paper: Deciphering the Morphological Origins of X-shaped Radio Galaxies: Numerical Modeling of Backflow versus Jet Reorientation
Authors: Gourab Giri, Bhargav Vaidya, Christian Fendt
Figure caption: Distribution of the thermal emission coefficient χ over a range of temperatures T, as obtained for the chosen X-ray band of [0.5–5] keV and a metallicity of 0.3 solar.
Paper: Deciphering the Morphological Origins of X-shaped Radio Galaxies: Numerical Modeling of Backflow versus Jet Reorientation
Authors: Gourab Giri, Bhargav Vaidya, Christian Fendt
Figure caption: Thermal X-ray intensity map (in log[erg s−1 cm−2 sr−1]) in the [0.5–5] keV band for model qr90_YX. The sequence shows the formation and evolution of X-ray dip regions (cavities) in the midst of the cluster. The evolution is complementary to the cocoon geometry (Figure 3, viewing angle (0°, 0°)). Four distinct elongated cavities are generated, associated with wings and active lobes at 114 Myr, bearing signatures of the reorientation event. See Section 5.1 for details.
Paper: Deciphering the Morphological Origins of X-shaped Radio Galaxies: Numerical Modeling of Backflow versus Jet Reorientation
Authors: Gourab Giri, Bhargav Vaidya, Christian Fendt
Figure caption: Left column: Thermal X-ray intensity maps for six different cases that we have considered in Table 1, obtained at 114 Myr, plotted in the same color-bar interval as in Figure 8 in log[erg s−1 cm−2 sr−1]. Right column: Chart of the normalized intensity variation vs. azimuthal angle, as obtained by following an elliptical isophote over the X-ray intensity map in the immediate left, showing the detection and strength of X-ray cavities. The simulation names for each row are attached in the rightmost corner. To guide the eye in locating the position of X-ray cavities, we have labeled the cavities C1, C2, C3, and C4 in two of the above cases. All of these intensity maps are viewed along the z-axis, i.e., along the (0°, 0°) viewing angle.
Paper: Deciphering the Morphological Origins of X-shaped Radio Galaxies: Numerical Modeling of Backflow versus Jet Reorientation
Authors: Gourab Giri, Bhargav Vaidya, Christian Fendt
Figure caption: Numerous cavity morphologies (at 114 Myr) obtained for the case of qr90_YX (top panel) and qr90_XY (bottom panel) for three different line-of-sight angles (θ, ϕ) ≡ (20°, 70°), (45°, 45°), (70°, 0°) (denoted in the respective figures). The color bar complements the intensity distribution plotted in log[erg s−1 cm−2 sr−1]. This complex cavity geometry may look distinctive, but it is not uncommon. It is interesting to note the appearance of four (mostly elongated) X-ray cavities in the emission maps associated with the reorientation cases, regardless of the line-of-sight visualization angles.
Paper: The Chandra Source Catalog Normal Galaxy Sample
Authors: Dong-Woo Kim, Alyssa Cassity, Binod Bhatt, Giuseppina Fabbiano, Juan Rafael Martinez Galarza, Ewan O'Sullivan, Arnold Rots
Figure caption: (Left) The z distributions and (right) the log(L X) distributions of the spec-z sample (SDSS) and the photo-z samples (SDSS, Pan-STARRS, and Legacy).
Paper: A White Dwarf Search Model Based on a Deep Transfer-learning Method
Authors: Lei Tan, 磊 谈, Zhicun Liu, 志 存 柳, Feng Wang, 锋 王, Ying Mei, 盈 梅, Hui Deng, 辉 邓, Chao Liu, 超 刘
Figure caption: Comparison before and after removing cosmic lines by median filtering. (a) The spectrum before the removal of cosmic lines, and (b) the spectrum after the removal of cosmic lines.
Paper: A White Dwarf Search Model Based on a Deep Transfer-learning Method
Authors: Lei Tan, 磊 谈, Zhicun Liu, 志 存 柳, Feng Wang, 锋 王, Ying Mei, 盈 梅, Hui Deng, 辉 邓, Chao Liu, 超 刘
Figure caption: The structure of the classification model. The squares in the figure represent the convolutional and max-pooling layers, the value above the square is the size of the feature map, and the value below is the size of the convolutional kernel and the max-pooling kernel.
Paper: A White Dwarf Search Model Based on a Deep Transfer-learning Method
Authors: Lei Tan, 磊 谈, Zhicun Liu, 志 存 柳, Feng Wang, 锋 王, Ying Mei, 盈 梅, Hui Deng, 辉 邓, Chao Liu, 超 刘
Figure caption: Examples of spectra (a) containing broad cosmic lines and (b) containing missing values.
Paper: A White Dwarf Search Model Based on a Deep Transfer-learning Method
Authors: Lei Tan, 磊 谈, Zhicun Liu, 志 存 柳, Feng Wang, 锋 王, Ying Mei, 盈 梅, Hui Deng, 辉 邓, Chao Liu, 超 刘
Figure caption: Variation in the accuracy rate of the three strategies with different data set proportions, where X-axis is the proportion of data set and Y-axis is the accuracy rate.
Paper: A White Dwarf Search Model Based on a Deep Transfer-learning Method
Authors: Lei Tan, 磊 谈, Zhicun Liu, 志 存 柳, Feng Wang, 锋 王, Ying Mei, 盈 梅, Hui Deng, 辉 邓, Chao Liu, 超 刘
Figure caption: Variation in the accuracy rate of the three strategies during training process, where X-axis is the number of iteration and Y-axis is the accuracy rate.
Paper: A White Dwarf Search Model Based on a Deep Transfer-learning Method
Authors: Lei Tan, 磊 谈, Zhicun Liu, 志 存 柳, Feng Wang, 锋 王, Ying Mei, 盈 梅, Hui Deng, 辉 邓, Chao Liu, 超 刘
Figure caption: Confusion matrix of the seven-class classification model.
Paper: A White Dwarf Search Model Based on a Deep Transfer-learning Method
Authors: Lei Tan, 磊 谈, Zhicun Liu, 志 存 柳, Feng Wang, 锋 王, Ying Mei, 盈 梅, Hui Deng, 辉 邓, Chao Liu, 超 刘
Figure caption: Confusion matrix of the binary classification model.
Paper: Improvement of Pan-STARRS Photometric Calibration with LAMOST and Gaia
Authors: Kai Xiao, Haibo Yuan, Bowen Huang, Ruoyi Zhang, Lin Yang, Shuai Xu
Figure caption: 2D polynomial fitting of the intrinsic colors as functions of ﹩{({G}_{\mathrm{BP}}-{G}_{\mathrm{RP}})}_{0}﹩ and [Fe/H] for the SCR′ calibration stars. From top to bottom are the G BP − g, G RP − r, G RP − i, G RP − z, and G RP − y colors, respectively. The left column shows the fitting results after 3σ  clipping. The red and blue curves represent the results for [Fe/H] = 0 and −1, respectively, and the standard deviations of the fitting residuals are labeled. The second, third, fourth, and fifth columns plot residuals against ﹩{({G}_{\mathrm{BP}}-{G}_{\mathrm{RP}})}_{0}﹩, [Fe/H], magnitude, and E(B − V)SFD, respectively, with the medians overplotted in red.
Paper: Improvement of Pan-STARRS Photometric Calibration with LAMOST and Gaia
Authors: Kai Xiao, Haibo Yuan, Bowen Huang, Ruoyi Zhang, Lin Yang, Shuai Xu
Figure caption: HR diagram for stars in the g band after combining the PS1 data with the catalog of Xu et al. (2022). The color bar in the upper left corner indicates the stellar number densities. The blue dotted line at ﹩{m}_{{\rm{G}}}\,=-{({G}_{\mathrm{BP}}-{G}_{\mathrm{RP}})}^{2}\,+\,6.5\times ({G}_{\mathrm{BP}}-{G}_{\mathrm{RP}})-1.8﹩ given by Xu et al. (2022) marks the boundary between the dwarfs and the giants. Xu et al. (2022) applied color cuts of 0.5 < (G BP − G RP) < 1.5 for dwarfs and 0.7 <(G BP − G RP) < 1.4 for giants during the photometric metallicity construction.
Paper: Improvement of Pan-STARRS Photometric Calibration with LAMOST and Gaia
Authors: Kai Xiao, Haibo Yuan, Bowen Huang, Ruoyi Zhang, Lin Yang, Shuai Xu
Figure caption: Spatial variations of magnitude offsets obtained by the SCR′ method in the g, r, i, z, and y bands, and spatial distribution of the calibration sample stars for the g band in the Equatorial coordinate system. Color bars are overplotted to the bottom. The HEALPix index is level 8, corresponding to a physical scale of 14′.
Paper: Improvement of Pan-STARRS Photometric Calibration with LAMOST and Gaia
Authors: Kai Xiao, Haibo Yuan, Bowen Huang, Ruoyi Zhang, Lin Yang, Shuai Xu
Figure caption: The same as Figure 3, but for the XPSP method.