adsabs/AstroCLIMB huggingface dataset:
Paper: Probing the Low-radio-frequency Emission in PG Quasars with the uGMRT. II
Authors: Sanna Gulati, S. Silpa, P. Kharb, Luis C. Ho, S. Ghosh, J. Baghel
Figure caption: uGMRT 685 MHz radio contour images of PG 0003+158 (RL), PG 0026+129, PG 0052+251, PG 0157+001, PG 0804+761, and PG 0838+770. The contour levels are 3σ × (−1, 1, 2, 4, 8, 16, 32, 64, 128, 256, 516).
Paper: Probing the Low-radio-frequency Emission in PG Quasars with the uGMRT. II
Authors: Sanna Gulati, S. Silpa, P. Kharb, Luis C. Ho, S. Ghosh, J. Baghel
Figure caption: uGMRT 685 MHz radio contour images of PG 1302−102 (RL), PG 1307+085, PG 1309+355 (RL), PG 1322+659, PG 1351+640, and PG 1352+183. The contour levels are 3σ × (−1, 1, 2, 4, 8, 16, 32, 64, 128, 256, 516).
Paper: Probing the Low-radio-frequency Emission in PG Quasars with the uGMRT. II
Authors: Sanna Gulati, S. Silpa, P. Kharb, Luis C. Ho, S. Ghosh, J. Baghel
Figure caption: uGMRT 685 MHz radio contour images of PG 1100+772 (RL), PG 1103-006 (RL), PG 1114+445, PG 1115+407, PG 1116+215, and PG 1126-041. The contour levels are 3σ × (−1, 1, 2, 4, 8, 16, 32, 64, 128, 256, 516).
Paper: Probing the Low-radio-frequency Emission in PG Quasars with the uGMRT. II
Authors: Sanna Gulati, S. Silpa, P. Kharb, Luis C. Ho, S. Ghosh, J. Baghel
Figure caption: uGMRT 685 MHz radio contour images of PG 1149-110, PG 1151+117, PG 1202+281, PG 1216+069, PG 1226+023 (RL), and PG 1259+593. The contour levels are 3σ × (−1, 1, 2, 4, 8, 16, 32, 64, 128, 256, 516).
Paper: Probing the Low-radio-frequency Emission in PG Quasars with the uGMRT. II
Authors: Sanna Gulati, S. Silpa, P. Kharb, Luis C. Ho, S. Ghosh, J. Baghel
Figure caption: uGMRT 685 MHz radio contour images of PG 1354+213, PG 1402+261, PG 1411+442, PG 1415+451, PG 1416−129, and PG 1425+267 (RL). The contour levels are 3σ × (−1, 1, 2, 4, 8, 16, 32, 64, 128, 256, 516).
Paper: Probing the Low-radio-frequency Emission in PG Quasars with the uGMRT. II
Authors: Sanna Gulati, S. Silpa, P. Kharb, Luis C. Ho, S. Ghosh, J. Baghel
Figure caption: uGMRT 685 MHz radio contour images of PG 1004+130 (RL), PG 1012+008, PG 1022+519, PG 1048-090 (RL), PG 1048+342, PG 1049-005. The contour levels are 3σ × (−1, 1, 2, 4, 8, 16, 32, 64, 128, 256, 516).
Paper: Probing the Low-radio-frequency Emission in PG Quasars with the uGMRT. II
Authors: Sanna Gulati, S. Silpa, P. Kharb, Luis C. Ho, S. Ghosh, J. Baghel
Figure caption: uGMRT 685 MHz radio contour images of PG 0844+349, PG 0921+525, PG 0923+201, PG 0947+396, PG 0953+414, and PG 1001+054. The contour levels are 3σ × (−1, 1, 2, 4, 8, 16, 32, 64, 128, 256, 516).
Paper: Probing the Low-radio-frequency Emission in PG Quasars with the uGMRT. II
Authors: Sanna Gulati, S. Silpa, P. Kharb, Luis C. Ho, S. Ghosh, J. Baghel
Figure caption: uGMRT 685 MHz radio contour images of PG 1617+175, PG 1626-554, PG 1700+518, PG 1704+068 (RL), PG 2112+059, and PG 2233+134. The contour levels are 3σ × (−1, 1, 2, 4, 8, 16, 32, 64, 128, 256, 516).
Paper: Probing the Low-radio-frequency Emission in PG Quasars with the uGMRT. II
Authors: Sanna Gulati, S. Silpa, P. Kharb, Luis C. Ho, S. Ghosh, J. Baghel
Figure caption: uGMRT 685 MHz radio contour images of PG 2251+113 (RL) and PG 2308+098 (RL). The contour levels are 3σ × (−1, 1, 2, 4, 8, 16, 32, 64, 128, 256, 516).
Paper: Probing the Low-radio-frequency Emission in PG Quasars with the uGMRT. II
Authors: Sanna Gulati, S. Silpa, P. Kharb, Luis C. Ho, S. Ghosh, J. Baghel
Figure caption: uGMRT 685 MHz radio contour images of PG 1543+580, PG 1535+547, PG 1543+489, PG 1545+210 (RL), PG 1612+261, and PG 1613+658. The contour levels are 3σ × (−1, 1, 2, 4, 8, 16, 32, 64, 128, 256, 516).
Paper: The Hawaii Infrared Supernova Study (HISS): Spectroscopic Data Release 1
Authors: K. Medler, C. Ashall, M. Shahbandeh, J. M. DerKacy, W. B. Hoogendam, D. O. Jones, B. J. Shappee, J. T. Hinkle, C. M. Pfeffer, E. Baron, P. Hoeflich, E. Hsiao
Figure caption: Comparison of SN 2021rhu, SN 2022hrs, and SN 2022ihz to the spectral template constructed using the data presented in (J. Lu et al. 2023). Spectra are shown in rest wavelength, and the gray regions denote telluric regions. The sBV values used in constructing were 0.85, 0.39, and 0.93 for SN 2022hrs, SN 2022ihz, and SN 2021rhu, respectively. As such, the strength features do not fully match between the template and observed spectra. However, the locations of most spectral features are consistent, and detailed spectral modeling is required to accurately reproduce the strength of spectral features.
Paper: The Hawaii Infrared Supernova Study (HISS): Spectroscopic Data Release 1
Authors: K. Medler, C. Ashall, M. Shahbandeh, J. M. DerKacy, W. B. Hoogendam, D. O. Jones, B. J. Shappee, J. T. Hinkle, C. M. Pfeffer, E. Baron, P. Hoeflich, E. Hsiao
Figure caption: NIR spectra of four SNe representing a typical evolution of SNe Ia, SNe II, and SNe IIb. These objects possess the largest temporal range in DR1. Strong features are labeled based on previous studies of NIR SNe (G. H. Marion et al. 2009; C. Ashall et al. 2019b; S. Davis et al. 2019; M. Shahbandeh et al. 2022), with the label located at the rest wavelength of the line. Highlighted gray areas denote regions of strong telluric absorption, with low S/N and have been removed for clarity.
Paper: The Hawaii Infrared Supernova Study (HISS): Spectroscopic Data Release 1
Authors: K. Medler, C. Ashall, M. Shahbandeh, J. M. DerKacy, W. B. Hoogendam, D. O. Jones, B. J. Shappee, J. T. Hinkle, C. M. Pfeffer, E. Baron, P. Hoeflich, E. Hsiao
Figure caption: Similar to Figure 4 but for SNe IIb. Comparison of SN 2022crv with the spectral template of He-rich SE-SNe presented in M. Shahbandeh et al. (2022). The area heavily affected by telluric absorption, the shaded gray regions, has been removed due to the lack of SN flux. The template spectra reproduce the spectral features present in the spectra of SN 2022crv. The initial spectra of SN 2022crv, taken at −15.24 days, are compared to the earliest template at −5 days, resulting in a difference in ejecta velocity.
Paper: The Hawaii Infrared Supernova Study (HISS): Spectroscopic Data Release 1
Authors: K. Medler, C. Ashall, M. Shahbandeh, J. M. DerKacy, W. B. Hoogendam, D. O. Jones, B. J. Shappee, J. T. Hinkle, C. M. Pfeffer, E. Baron, P. Hoeflich, E. Hsiao
Figure caption: Similar to Figure 4 but for SNe II. Comparison of SN 2021tiq, at three distinct epochs after explosion, with the spectral template of SN II obtained from S. Davis et al. (2019). The gray areas in the plot denote regions of strong telluric absorption and have been removed for clarity. All spectral features observed in the template spectra can be found within the spectra of SN 2021tiq.
Paper: The Hawaii Infrared Supernova Study (HISS): Spectroscopic Data Release 1
Authors: K. Medler, C. Ashall, M. Shahbandeh, J. M. DerKacy, W. B. Hoogendam, D. O. Jones, B. J. Shappee, J. T. Hinkle, C. M. Pfeffer, E. Baron, P. Hoeflich, E. Hsiao
Figure caption: Left: phase distribution relative to epoch of maximum light in the g/o bands for SNe Ia, SNe II, SE-SNe, interacting SNe, and other objects. Right: redshift distribution of the objects observed by HISS. Vertical dashed lines denote the median phase and redshift for the different classifications observed in this data release.
Paper: The Hawaii Infrared Supernova Study (HISS): Spectroscopic Data Release 1
Authors: K. Medler, C. Ashall, M. Shahbandeh, J. M. DerKacy, W. B. Hoogendam, D. O. Jones, B. J. Shappee, J. T. Hinkle, C. M. Pfeffer, E. Baron, P. Hoeflich, E. Hsiao
Figure caption: Left: the total number of objects observed in DR1 categorized by SN types and subtypes. Right: the number of spectra obtained for each spectral classification and subtype.
Paper: Smart Source Detection Framework for Lobster Eye Telescopes
Authors: Peng Jia, Yijun Zhang, Kaiyang Li, Yuan Liu, Haiwu Pan, Yushan Li
Figure caption: Concept of the method proposed in this paper, which detects celestial objects by a top-down, multiscale target detection architecture. The framework operates using a two-stage process. Initially, it detects and masks bright targets within 3D images that have a coarsely sampled energy dimension. Subsequently, it analyzes the remaining data to identify fainter sources, utilizing 3D photon points. This method ensures comprehensive source detection across a wide dynamic range.
Paper: Smart Source Detection Framework for Lobster Eye Telescopes
Authors: Peng Jia, Yijun Zhang, Kaiyang Li, Yuan Liu, Haiwu Pan, Yushan Li
Figure caption: The visualization of data obtained by the WXT. The left panel illustrates a 3D cube of the observational data, organized along two spatial dimensions and the energy dimension with different colors. The middle panel depicts a 2D image where all photons are projected onto the spatial dimension represented in the detector coordinate. The right panel depicts a 2D image where all photons are projected onto the spatial dimension represented in the celestial sphere coordinate. As shown in this figure, after the original data is composed into a 2D image, the cruciform arms display a variety of orientations, which will make it difficult for target detection.
Paper: Smart Source Detection Framework for Lobster Eye Telescopes
Authors: Peng Jia, Yijun Zhang, Kaiyang Li, Yuan Liu, Haiwu Pan, Yushan Li
Figure caption: Left: the structure of the ultrabright and bright sources detection neural network. Right: the specific structure of the Swin Transformer block, along with the detailed structure of the MSA mechanism, which appears in pairs. The Swin Transformer block extracts features from celestial object images that are relevant for source detection.
Paper: Smart Source Detection Framework for Lobster Eye Telescopes
Authors: Peng Jia, Yijun Zhang, Kaiyang Li, Yuan Liu, Haiwu Pan, Yushan Li
Figure caption: The diagram of the preprocessing step for ultrabright and bright sources. Original event files are reorganized as 3D images, which contain spatial coordinates and energy coordinates. Three channels are included in this figure for bright and ultrabright source detection, which contain photon counts of all energy bands, photon counts from 0.5 to 2.5 keV and photon counts from 2.5 to 4.0 keV. The threshold is adjustable for data obtained by different telescopes, according to their detection ability.
Paper: Smart Source Detection Framework for Lobster Eye Telescopes
Authors: Peng Jia, Yijun Zhang, Kaiyang Li, Yuan Liu, Haiwu Pan, Yushan Li
Figure caption: Diagram of the source detection framework. The entire process can be divided into four steps, using two different deep learning networks to detect sources from multienergy channel images and 3D photon points, respectively. The proposed framework utilizes two neural networks and applies the Soft-NMS algorithm to merge their respective detections, yielding results with adaptive confidence for images obtained by different telescopes.
Paper: The Hawaii Infrared Supernova Study (HISS): Spectroscopic Data Release 1
Authors: K. Medler, C. Ashall, M. Shahbandeh, J. M. DerKacy, W. B. Hoogendam, D. O. Jones, B. J. Shappee, J. T. Hinkle, C. M. Pfeffer, E. Baron, P. Hoeflich, E. Hsiao
Figure caption: Same as Figure B1 but for interacting SNe, including SNe Ia CSM, SNe IIn, and the SNe Icn SN 2021csp.
Paper: The Hawaii Infrared Supernova Study (HISS): Spectroscopic Data Release 1
Authors: K. Medler, C. Ashall, M. Shahbandeh, J. M. DerKacy, W. B. Hoogendam, D. O. Jones, B. J. Shappee, J. T. Hinkle, C. M. Pfeffer, E. Baron, P. Hoeflich, E. Hsiao
Figure caption: Same as Figure B1 but for the TDE ASASSN-20hx/AT 2020ohl. The spectra are blue and featureless throughout the evolution of ASASSN-20hx.
Paper: The Hawaii Infrared Supernova Study (HISS): Spectroscopic Data Release 1
Authors: K. Medler, C. Ashall, M. Shahbandeh, J. M. DerKacy, W. B. Hoogendam, D. O. Jones, B. J. Shappee, J. T. Hinkle, C. M. Pfeffer, E. Baron, P. Hoeflich, E. Hsiao
Figure caption: Same as Figure B1 but for the SLSN-I SN 2020fpl.
Paper: Smart Source Detection Framework for Lobster Eye Telescopes
Authors: Peng Jia, Yijun Zhang, Kaiyang Li, Yuan Liu, Haiwu Pan, Yushan Li
Figure caption: The figure illustrates the specific process of adaptive threshold generation. The threshold generation method is predicated on the assumption that an effective detection boundary can be established by differentiating between the features of reference targets and the background. The methodology assumes that a suitable detection threshold can be derived from the feature-space separability between reference targets and the background.
Paper: Smart Source Detection Framework for Lobster Eye Telescopes
Authors: Peng Jia, Yijun Zhang, Kaiyang Li, Yuan Liu, Haiwu Pan, Yushan Li
Figure caption: The figure illustrates the specific architecture of PointNet and the transformation of 3D point features. It should be noted that these projection effectively merges features from both the local point features and the global features.
Paper: Influence of Magnetic Complexity of Active Regions on Solar Wind Properties during Solar Cycles 23 and 24
Authors: Xinzheng Shi, Hui Fu, Zhenghua Huang, Limei Yan, Qi Liu, Lidong Xia
Figure caption: The top, middle, and bottom rows respectively present the statistical results of the latitudinal distributions of all α, β, and complex ARs from 1999 to 2020. The first column shows the temporal evolution of latitudes for the three types of ARs. The second column compares the latitudinal histograms of the three types of ARs during SC 23 (red) and SC 24 (blue). The third column presents the CDF curves of the latitudinal distributions for the same types of ARs between the two solar cycles, along with the results of the two-sample K-S test. The black vertical dotted line indicates the point where the maximum vertical deviation is between the two curves, and D is the difference between the two curves at that point. The results indicate that the latitudinal distributions of the three types of ARs are very similar, with no significant differences. Moreover, the latitudinal distributions of the same type of ARs are also similar between SC 23 and SC 24.
Paper: Influence of Magnetic Complexity of Active Regions on Solar Wind Properties during Solar Cycles 23 and 24
Authors: Xinzheng Shi, Hui Fu, Zhenghua Huang, Limei Yan, Qi Liu, Lidong Xia
Figure caption: The footpoint magnetic field strengths of α, β, and complex AR solar wind during SCs 23 and 24. The first row shows the histograms of footpoint magnetic field strengths of the three types of AR solar wind during SC 23 (red) and SC 24 (blue). The second row presents the CDF curves of footpoint magnetic field strengths for different types of AR solar wind, combining data from SC 23 and SC 24, along with the two-sample K-S test results between any two groups. The cyan, yellow, and black curves represent α, β, and complex AR solar wind, respectively. The third row compares the CDF curves of footpoint magnetic field strengths for α, β, and complex AR solar wind between SC 23 and SC 24. The footpoint magnetic field strength of complex AR solar wind is significantly higher than that of the other two types of AR solar wind.
Paper: Influence of Magnetic Complexity of Active Regions on Solar Wind Properties during Solar Cycles 23 and 24
Authors: Xinzheng Shi, Hui Fu, Zhenghua Huang, Limei Yan, Qi Liu, Lidong Xia
Figure caption: The yearly numbers and proportions of different types of ARs and AR solar wind. Panel (a) represents the annual number of three types of ARs, and panel (b) represents the yearly samples of the different types of AR solar wind. The bottom panels represent the annual proportions of each type of AR (panel (c)) and the annual proportions of three types of AR solar wind (panel (d)). The blue, orange, and green colors represent α, β, and complex ARs (panels (a) and (c)) and AR solar wind (panels (b) and (d)). The shaded area represents the annual sunspot numbers. The numbers and proportions of different types of ARs and AR solar wind show clear variations in response to solar activity.
Paper: Influence of Magnetic Complexity of Active Regions on Solar Wind Properties during Solar Cycles 23 and 24
Authors: Xinzheng Shi, Hui Fu, Zhenghua Huang, Limei Yan, Qi Liu, Lidong Xia
Figure caption: The O7+/O6+ of α, β, and complex AR solar wind during SCs 23 and 24. The first row shows the histograms of O7+/O6+ of the three types of AR solar wind during SC 23 (red) and SC 24 (blue). The second and third rows present the CDF curves of O7+/O6+ for different types of AR solar wind during SC 23 and SC 24, respectively. The cyan, yellow, and black curves represent α, β, and complex AR solar wind, respectively. The fourth row compares the CDF curves of O7+/O6+ for α, β, and complex AR solar wind between SC 23 and SC 24. The difference of O7+/O6+ in the three types of AR solar wind is obvious.
Paper: Influence of Magnetic Complexity of Active Regions on Solar Wind Properties during Solar Cycles 23 and 24
Authors: Xinzheng Shi, Hui Fu, Zhenghua Huang, Limei Yan, Qi Liu, Lidong Xia
Figure caption: The in situ magnetic field strengths of α, β, and complex AR solar wind. The layout is the same as that of Figure 3. The higher the magnetic complexity of the AR, the higher the in situ magnetic field strength of the solar wind from the AR.
Paper: Influence of Magnetic Complexity of Active Regions on Solar Wind Properties during Solar Cycles 23 and 24
Authors: Xinzheng Shi, Hui Fu, Zhenghua Huang, Limei Yan, Qi Liu, Lidong Xia
Figure caption: The speeds of α, β, and complex AR solar wind. The layout is the same as that of Figure 3. The speeds of the three types of AR solar wind are generally the same.
Paper: 1FLAT: A Firmamento-based Catalog of Active Galactic Nuclei in Fermi-LAT High-Galactic-latitude γ-Ray Sources
Authors: P. Giommi, M. Doro, M. Gouvêa, L. Fronte, F. Metruccio, F. Arneodo, U. Barres de Almeida, S. Di Pippo, T. Kerscher, A. Macciò, B. Mazzon, M. Morrone, E. Prandini, A. Rodríguez, A. Ruina, N. Sahakyan, L. Silveri, D. Tripathi
Figure caption: Example of  graphical view after a tentative association 4FGL J1825.1-5231. (Left) The candidate 4FGL J1825.1-5231 is picked from a list (ID 3972); ERCI returns three candidates out of which only one is a putative blazar, the third one, PKS1821-525. (Center) An Aladin sky map shows the multiwavelength morphology with all available catalog error circles. The yellow circle is a 4LAC source. Candidate 3, therefore, agrees with 4LAC. (Right) Multiwavelength SED generated by  on the selected association.
Paper: 1FLAT: A Firmamento-based Catalog of Active Galactic Nuclei in Fermi-LAT High-Galactic-latitude γ-Ray Sources
Authors: P. Giommi, M. Doro, M. Gouvêa, L. Fronte, F. Metruccio, F. Arneodo, U. Barres de Almeida, S. Di Pippo, T. Kerscher, A. Macciò, B. Mazzon, M. Morrone, E. Prandini, A. Rodríguez, A. Ruina, N. Sahakyan, L. Silveri, D. Tripathi
Figure caption: The log(νpeak) from the LAT 4LAC catalog is plotted against the log(νpeak) estimated with BLaST within . The diagonal red dashed line represents equal values. The two estimates generally agree within less than one decade; however, there are also large differences, highlighted by the dotted box areas labeled 1, 2, and 3 (see the text for details).
Paper: 1FLAT: A Firmamento-based Catalog of Active Galactic Nuclei in Fermi-LAT High-Galactic-latitude γ-Ray Sources
Authors: P. Giommi, M. Doro, M. Gouvêa, L. Fronte, F. Metruccio, F. Arneodo, U. Barres de Almeida, S. Di Pippo, T. Kerscher, A. Macciò, B. Mazzon, M. Morrone, E. Prandini, A. Rodríguez, A. Ruina, N. Sahakyan, L. Silveri, D. Tripathi
Figure caption: The distribution of 4LAC γ-ray spectral slopes in the subsamples of LSP and HSP blazars, including both confirmed and newly associated sources. Since nearly all newly associated blazars have a lower γ-ray flux than the confirmed blazars, we restrict the comparison to sources with Energy_Flux100 < 10−11 erg cm−2 s−1, for consistency.
Paper: 1FLAT: A Firmamento-based Catalog of Active Galactic Nuclei in Fermi-LAT High-Galactic-latitude γ-Ray Sources
Authors: P. Giommi, M. Doro, M. Gouvêa, L. Fronte, F. Metruccio, F. Arneodo, U. Barres de Almeida, S. Di Pippo, T. Kerscher, A. Macciò, B. Mazzon, M. Morrone, E. Prandini, A. Rodríguez, A. Ruina, N. Sahakyan, L. Silveri, D. Tripathi
Figure caption: The power-law photon index from the 4FGL catalog is plotted against the γ-ray flux for the cases of the confirmed associations, newly identified blazars, and with different associations. The new and different identifications are spread over the spectral index but are largely confined to γ-ray fluxes lower than ≈10−11 erg cm−2 s−1.
Paper: 1FLAT: A Firmamento-based Catalog of Active Galactic Nuclei in Fermi-LAT High-Galactic-latitude γ-Ray Sources
Authors: P. Giommi, M. Doro, M. Gouvêa, L. Fronte, F. Metruccio, F. Arneodo, U. Barres de Almeida, S. Di Pippo, T. Kerscher, A. Macciò, B. Mazzon, M. Morrone, E. Prandini, A. Rodríguez, A. Ruina, N. Sahakyan, L. Silveri, D. Tripathi
Figure caption: Table for 5063 LAT 4FGL catalog sources of interest, uploaded on the  platform for counterpart search.
Paper: Probing the Low-radio-frequency Emission in PG Quasars with the uGMRT. II
Authors: Sanna Gulati, S. Silpa, P. Kharb, Luis C. Ho, S. Ghosh, J. Baghel
Figure caption: (Left) The radio–IR correlation for PG sources. The solid black line is the radio–IR correlation for star-forming galaxies (E. F. Bell 2003). The black dashed lines mark the 1σ regions and the black dotted lines mark the 3σ regions. (Right) The mean core spectral index as a function of qIR. The black dashed vertical line represents qIR = 1.8 (which is about 2.1σ below the E. F. Bell (2003) relation, which discriminates the AGN versus star formation contributions. The black horizontal line is αR = −0.5, which we use to discriminate between steep (αR < −0.5) and flat (αR > −0.5) spectrum cores.
Paper: Probing the Low-radio-frequency Emission in PG Quasars with the uGMRT. II
Authors: Sanna Gulati, S. Silpa, P. Kharb, Luis C. Ho, S. Ghosh, J. Baghel
Figure caption: (Left) Total 685 MHz radio luminosities from the uGMRT versus the logarithm of black hole masses for the PG quasars. The errors in 685 MHz radio luminosity are smaller than or comparable to the symbol size. The RL sources do not show any significant correlation. (Right) Ratio of 685 MHz luminosities to Eddington ratios versus black hole masses.
Paper: Probing the Low-radio-frequency Emission in PG Quasars with the uGMRT. II
Authors: Sanna Gulati, S. Silpa, P. Kharb, Luis C. Ho, S. Ghosh, J. Baghel
Figure caption: Jet kinetic power versus black hole masses (left) and Eddington ratios (right) for the PG sample. The RL sources in both the plots do not show any significant correlation (see Table 2).
Paper: Probing the Low-radio-frequency Emission in PG Quasars with the uGMRT. II
Authors: Sanna Gulati, S. Silpa, P. Kharb, Luis C. Ho, S. Ghosh, J. Baghel
Figure caption: Distribution of “minimum-energy” magnetic field strength for RQ and RL sources for k = 1 (left panel) and k = 3 (right panel). The outlier source with the largest ﹩{B}_{{\rm{\min }}}﹩ value is PG 1226+023 or 3C 273.
Paper: Probing the Low-radio-frequency Emission in PG Quasars with the uGMRT. II
Authors: Sanna Gulati, S. Silpa, P. Kharb, Luis C. Ho, S. Ghosh, J. Baghel
Figure caption: Distribution of 685 MHz projected radio source sizes in kiloparsec for the PG quasar sample.
Paper: Probing the Low-radio-frequency Emission in PG Quasars with the uGMRT. II
Authors: Sanna Gulati, S. Silpa, P. Kharb, Luis C. Ho, S. Ghosh, J. Baghel
Figure caption: The 685 MHz–5 GHz spectral index versus Eddington ratios for the PQ sample. In this and following plots, red triangles denote the RL QSOs, the blue circles denote RQ QSOs, and the blue-dashed and red-dotted lines represent the best-fit lines for RQ and RL sources, respectively.
Paper: Probing the Low-radio-frequency Emission in PG Quasars with the uGMRT. II
Authors: Sanna Gulati, S. Silpa, P. Kharb, Luis C. Ho, S. Ghosh, J. Baghel
Figure caption: Distribution of the uGMRT 685 MHz luminosities for the PG quasar sample. The RL–RQ dichotomy persists in the low-frequency uGMRT data.