adsabs/AstroCLIMB huggingface dataset:
Paper: Bondi-like Accretion Flow Dynamics: The Role of Gravitational Potential
Authors: Razieh Ranjbar, Héctor R. Olivares-Sánchez, Shahram Abbassi
Figure caption: Global solutions of SRAFs for different halo-to-BH mass ratios. The blue line represents the case without DM. The red, yellow, violet, and green lines correspond to a0 = 100 and halo-to-BH mass ratios of Mhalo/MBH = 1, 100, 1000, and 10,000, respectively.
Paper: Bondi-like Accretion Flow Dynamics: The Role of Gravitational Potential
Authors: Razieh Ranjbar, Héctor R. Olivares-Sánchez, Shahram Abbassi
Figure caption: Global numerical solutions for the Mach number with (﹩{a}_{0}=100,\,{ \mathcal L }=85﹩). The black, pink, and blue lines are for Mhalo/MBH = 1, 100 and 1000, respectively.
Paper: HAWC Performance Enhanced by Machine Learning in Gamma-hadron Separation
Authors: R. Alfaro, C. Alvarez, A. Andrés, E. Anita-Rangel, M. Araya, J. C. Arteaga-Velázquez, D. Avila Rojas, H. A. Ayala Solares, R. Babu, P. Bangale, E. Belmont-Moreno, A. Bernal, T. Capistrán, A. Carramiñana, F. Carreón, S. Casanova, U. Cotti, E. De la Fuente, D. Depaoli, P. Desiati, N. Di Lalla, R. Diaz Hernandez, M. A. DuVernois, J. C. Díaz-Vélez, K. Engel, T. Ergin, C. Espinoza, K. L. Fan, N. Fraija, S. Fraija, J. A. García-González, F. Garfias, N. Ghosh, A. Gonzalez Muñoz, M. M. González, J. A. González, J. A. Goodman, S. Groetsch, J. P. Harding, S. Hernández-Cadena, I. Herzog, D. Huang, P. Hüntemeyer, A. Iriarte, S. Kaufmann, D. Kieda, K. Leavitt, J. Lee, H. León Vargas, J. T. Linnemann, A. L. Longinotti, G. Luis-Raya, K. Malone, O. Martinez, J. Martínez-Castro, J. A. Matthews, P. Miranda-Romagnoli, P. E. Mirón-Enriquez, J. A. Montes, J. A. Morales-Soto, E. Moreno, M. Najafi, A. Nayerhoda, L. Nellen, N. Omodei, M. Osorio, E. Ponce, Y. Pérez Araujo, E. G. Pérez-Pérez, C. D. Rho, A. Rodriguez Parra, D. Rosa-González, M. Roth, H. Salazar, A. Sandoval, J. Serna-Franco, A. J. Smith, Y. Son, R. W. Springer, O. Tibolla, K. Tollefson, I. Torres, R. Torres-Escobedo, E. Varela, L. Villaseñor, X. Wang, Z. Wang, I. J. Watson, H. Wu, S. Yu, H. Zhou, C. de León
Figure caption: Q-factor as a function of reconstructed gamma-ray energy for different classification methods. Left: results for on-array events. Right: results for off-array events. The comparison includes MLP models with different architectures and input feature sets—MLP (20 features, 128:64:1), MLP (20 features, 10:10:1), and MLP (7 features, 10:10:1)—as well as BDT using 7 or 20 features. The notation (128:64:1) and (10:10:1) indicate the structure of hidden layers in the MLP models. The MLP model with a 10:10:1 architecture corresponds to that used in prior HAWC analyses (R. Alfaro et al. 2022). Similarly, the BDT models employ the same configuration as described in that work.
Paper: HAWC Performance Enhanced by Machine Learning in Gamma-hadron Separation
Authors: R. Alfaro, C. Alvarez, A. Andrés, E. Anita-Rangel, M. Araya, J. C. Arteaga-Velázquez, D. Avila Rojas, H. A. Ayala Solares, R. Babu, P. Bangale, E. Belmont-Moreno, A. Bernal, T. Capistrán, A. Carramiñana, F. Carreón, S. Casanova, U. Cotti, E. De la Fuente, D. Depaoli, P. Desiati, N. Di Lalla, R. Diaz Hernandez, M. A. DuVernois, J. C. Díaz-Vélez, K. Engel, T. Ergin, C. Espinoza, K. L. Fan, N. Fraija, S. Fraija, J. A. García-González, F. Garfias, N. Ghosh, A. Gonzalez Muñoz, M. M. González, J. A. González, J. A. Goodman, S. Groetsch, J. P. Harding, S. Hernández-Cadena, I. Herzog, D. Huang, P. Hüntemeyer, A. Iriarte, S. Kaufmann, D. Kieda, K. Leavitt, J. Lee, H. León Vargas, J. T. Linnemann, A. L. Longinotti, G. Luis-Raya, K. Malone, O. Martinez, J. Martínez-Castro, J. A. Matthews, P. Miranda-Romagnoli, P. E. Mirón-Enriquez, J. A. Montes, J. A. Morales-Soto, E. Moreno, M. Najafi, A. Nayerhoda, L. Nellen, N. Omodei, M. Osorio, E. Ponce, Y. Pérez Araujo, E. G. Pérez-Pérez, C. D. Rho, A. Rodriguez Parra, D. Rosa-González, M. Roth, H. Salazar, A. Sandoval, J. Serna-Franco, A. J. Smith, Y. Son, R. W. Springer, O. Tibolla, K. Tollefson, I. Torres, R. Torres-Escobedo, E. Varela, L. Villaseñor, X. Wang, Z. Wang, I. J. Watson, H. Wu, S. Yu, H. Zhou, C. de León
Figure caption: Receiver operating characteristic (ROC) curves and area under the curve (AUC) values for MLP model performance across different fHit bins. Left panel (a): results for on-array events. Right panel (b): results for off-array events. Each curve corresponds to a specific fHit bin, labeled with reconstructed median energy of each bin. The MLP model exhibits progressively better classification performance with increasing fHit, as indicated by higher AUC values.
Paper: HAWC Performance Enhanced by Machine Learning in Gamma-hadron Separation
Authors: R. Alfaro, C. Alvarez, A. Andrés, E. Anita-Rangel, M. Araya, J. C. Arteaga-Velázquez, D. Avila Rojas, H. A. Ayala Solares, R. Babu, P. Bangale, E. Belmont-Moreno, A. Bernal, T. Capistrán, A. Carramiñana, F. Carreón, S. Casanova, U. Cotti, E. De la Fuente, D. Depaoli, P. Desiati, N. Di Lalla, R. Diaz Hernandez, M. A. DuVernois, J. C. Díaz-Vélez, K. Engel, T. Ergin, C. Espinoza, K. L. Fan, N. Fraija, S. Fraija, J. A. García-González, F. Garfias, N. Ghosh, A. Gonzalez Muñoz, M. M. González, J. A. González, J. A. Goodman, S. Groetsch, J. P. Harding, S. Hernández-Cadena, I. Herzog, D. Huang, P. Hüntemeyer, A. Iriarte, S. Kaufmann, D. Kieda, K. Leavitt, J. Lee, H. León Vargas, J. T. Linnemann, A. L. Longinotti, G. Luis-Raya, K. Malone, O. Martinez, J. Martínez-Castro, J. A. Matthews, P. Miranda-Romagnoli, P. E. Mirón-Enriquez, J. A. Montes, J. A. Morales-Soto, E. Moreno, M. Najafi, A. Nayerhoda, L. Nellen, N. Omodei, M. Osorio, E. Ponce, Y. Pérez Araujo, E. G. Pérez-Pérez, C. D. Rho, A. Rodriguez Parra, D. Rosa-González, M. Roth, H. Salazar, A. Sandoval, J. Serna-Franco, A. J. Smith, Y. Son, R. W. Springer, O. Tibolla, K. Tollefson, I. Torres, R. Torres-Escobedo, E. Varela, L. Villaseñor, X. Wang, Z. Wang, I. J. Watson, H. Wu, S. Yu, H. Zhou, C. de León
Figure caption: HAWC 10 yr sensitivity comparison between MLP and SC methods. Top: comparison of differential sensitivity curves between the MLP-based event selection (solid lines) and the SC method (dashed lines) as a function of reconstructed gamma-ray energy. Bottom: relative improvement in sensitivity, calculated as (Fluxsc − Fluxmlp)/Fluxsc.
Paper: HAWC Performance Enhanced by Machine Learning in Gamma-hadron Separation
Authors: R. Alfaro, C. Alvarez, A. Andrés, E. Anita-Rangel, M. Araya, J. C. Arteaga-Velázquez, D. Avila Rojas, H. A. Ayala Solares, R. Babu, P. Bangale, E. Belmont-Moreno, A. Bernal, T. Capistrán, A. Carramiñana, F. Carreón, S. Casanova, U. Cotti, E. De la Fuente, D. Depaoli, P. Desiati, N. Di Lalla, R. Diaz Hernandez, M. A. DuVernois, J. C. Díaz-Vélez, K. Engel, T. Ergin, C. Espinoza, K. L. Fan, N. Fraija, S. Fraija, J. A. García-González, F. Garfias, N. Ghosh, A. Gonzalez Muñoz, M. M. González, J. A. González, J. A. Goodman, S. Groetsch, J. P. Harding, S. Hernández-Cadena, I. Herzog, D. Huang, P. Hüntemeyer, A. Iriarte, S. Kaufmann, D. Kieda, K. Leavitt, J. Lee, H. León Vargas, J. T. Linnemann, A. L. Longinotti, G. Luis-Raya, K. Malone, O. Martinez, J. Martínez-Castro, J. A. Matthews, P. Miranda-Romagnoli, P. E. Mirón-Enriquez, J. A. Montes, J. A. Morales-Soto, E. Moreno, M. Najafi, A. Nayerhoda, L. Nellen, N. Omodei, M. Osorio, E. Ponce, Y. Pérez Araujo, E. G. Pérez-Pérez, C. D. Rho, A. Rodriguez Parra, D. Rosa-González, M. Roth, H. Salazar, A. Sandoval, J. Serna-Franco, A. J. Smith, Y. Son, R. W. Springer, O. Tibolla, K. Tollefson, I. Torres, R. Torres-Escobedo, E. Varela, L. Villaseñor, X. Wang, Z. Wang, I. J. Watson, H. Wu, S. Yu, H. Zhou, C. de León
Figure caption: Left: sensitivities comparison between MLP, CNN, and BDT models. Right: sensitivities comparison between MLP models with different training configuration.
Paper: HAWC Performance Enhanced by Machine Learning in Gamma-hadron Separation
Authors: R. Alfaro, C. Alvarez, A. Andrés, E. Anita-Rangel, M. Araya, J. C. Arteaga-Velázquez, D. Avila Rojas, H. A. Ayala Solares, R. Babu, P. Bangale, E. Belmont-Moreno, A. Bernal, T. Capistrán, A. Carramiñana, F. Carreón, S. Casanova, U. Cotti, E. De la Fuente, D. Depaoli, P. Desiati, N. Di Lalla, R. Diaz Hernandez, M. A. DuVernois, J. C. Díaz-Vélez, K. Engel, T. Ergin, C. Espinoza, K. L. Fan, N. Fraija, S. Fraija, J. A. García-González, F. Garfias, N. Ghosh, A. Gonzalez Muñoz, M. M. González, J. A. González, J. A. Goodman, S. Groetsch, J. P. Harding, S. Hernández-Cadena, I. Herzog, D. Huang, P. Hüntemeyer, A. Iriarte, S. Kaufmann, D. Kieda, K. Leavitt, J. Lee, H. León Vargas, J. T. Linnemann, A. L. Longinotti, G. Luis-Raya, K. Malone, O. Martinez, J. Martínez-Castro, J. A. Matthews, P. Miranda-Romagnoli, P. E. Mirón-Enriquez, J. A. Montes, J. A. Morales-Soto, E. Moreno, M. Najafi, A. Nayerhoda, L. Nellen, N. Omodei, M. Osorio, E. Ponce, Y. Pérez Araujo, E. G. Pérez-Pérez, C. D. Rho, A. Rodriguez Parra, D. Rosa-González, M. Roth, H. Salazar, A. Sandoval, J. Serna-Franco, A. J. Smith, Y. Son, R. W. Springer, O. Tibolla, K. Tollefson, I. Torres, R. Torres-Escobedo, E. Varela, L. Villaseñor, X. Wang, Z. Wang, I. J. Watson, H. Wu, S. Yu, H. Zhou, C. de León
Figure caption: Q-factor as a function of reconstructed energy for different classification methods. (a) Results for on-array events. (b) Results for off-array events. Classification methods compared include SC, MLP, CNN, and BDT. Q-factors improve as energy increasing, with notable differences across methods, especially in high-energy bins.
Paper: HAWC Performance Enhanced by Machine Learning in Gamma-hadron Separation
Authors: R. Alfaro, C. Alvarez, A. Andrés, E. Anita-Rangel, M. Araya, J. C. Arteaga-Velázquez, D. Avila Rojas, H. A. Ayala Solares, R. Babu, P. Bangale, E. Belmont-Moreno, A. Bernal, T. Capistrán, A. Carramiñana, F. Carreón, S. Casanova, U. Cotti, E. De la Fuente, D. Depaoli, P. Desiati, N. Di Lalla, R. Diaz Hernandez, M. A. DuVernois, J. C. Díaz-Vélez, K. Engel, T. Ergin, C. Espinoza, K. L. Fan, N. Fraija, S. Fraija, J. A. García-González, F. Garfias, N. Ghosh, A. Gonzalez Muñoz, M. M. González, J. A. González, J. A. Goodman, S. Groetsch, J. P. Harding, S. Hernández-Cadena, I. Herzog, D. Huang, P. Hüntemeyer, A. Iriarte, S. Kaufmann, D. Kieda, K. Leavitt, J. Lee, H. León Vargas, J. T. Linnemann, A. L. Longinotti, G. Luis-Raya, K. Malone, O. Martinez, J. Martínez-Castro, J. A. Matthews, P. Miranda-Romagnoli, P. E. Mirón-Enriquez, J. A. Montes, J. A. Morales-Soto, E. Moreno, M. Najafi, A. Nayerhoda, L. Nellen, N. Omodei, M. Osorio, E. Ponce, Y. Pérez Araujo, E. G. Pérez-Pérez, C. D. Rho, A. Rodriguez Parra, D. Rosa-González, M. Roth, H. Salazar, A. Sandoval, J. Serna-Franco, A. J. Smith, Y. Son, R. W. Springer, O. Tibolla, K. Tollefson, I. Torres, R. Torres-Escobedo, E. Varela, L. Villaseñor, X. Wang, Z. Wang, I. J. Watson, H. Wu, S. Yu, H. Zhou, C. de León
Figure caption: Gamma-ray efficiencies ξγ (dashed lines) and hadron efficiencies ξh (solid lines) as a function of reconstructed energy for different classification methods. (a) Results for on-array events. (b) Results for off-array events. Classification methods compared include SC, MLP, CNN, and BDT.
Paper: HAWC Performance Enhanced by Machine Learning in Gamma-hadron Separation
Authors: R. Alfaro, C. Alvarez, A. Andrés, E. Anita-Rangel, M. Araya, J. C. Arteaga-Velázquez, D. Avila Rojas, H. A. Ayala Solares, R. Babu, P. Bangale, E. Belmont-Moreno, A. Bernal, T. Capistrán, A. Carramiñana, F. Carreón, S. Casanova, U. Cotti, E. De la Fuente, D. Depaoli, P. Desiati, N. Di Lalla, R. Diaz Hernandez, M. A. DuVernois, J. C. Díaz-Vélez, K. Engel, T. Ergin, C. Espinoza, K. L. Fan, N. Fraija, S. Fraija, J. A. García-González, F. Garfias, N. Ghosh, A. Gonzalez Muñoz, M. M. González, J. A. González, J. A. Goodman, S. Groetsch, J. P. Harding, S. Hernández-Cadena, I. Herzog, D. Huang, P. Hüntemeyer, A. Iriarte, S. Kaufmann, D. Kieda, K. Leavitt, J. Lee, H. León Vargas, J. T. Linnemann, A. L. Longinotti, G. Luis-Raya, K. Malone, O. Martinez, J. Martínez-Castro, J. A. Matthews, P. Miranda-Romagnoli, P. E. Mirón-Enriquez, J. A. Montes, J. A. Morales-Soto, E. Moreno, M. Najafi, A. Nayerhoda, L. Nellen, N. Omodei, M. Osorio, E. Ponce, Y. Pérez Araujo, E. G. Pérez-Pérez, C. D. Rho, A. Rodriguez Parra, D. Rosa-González, M. Roth, H. Salazar, A. Sandoval, J. Serna-Franco, A. J. Smith, Y. Son, R. W. Springer, O. Tibolla, K. Tollefson, I. Torres, R. Torres-Escobedo, E. Varela, L. Villaseñor, X. Wang, Z. Wang, I. J. Watson, H. Wu, S. Yu, H. Zhou, C. de León
Figure caption: pγ comparison between the MC and Crab region in Bin 3 and Bin 7.
Paper: HAWC Performance Enhanced by Machine Learning in Gamma-hadron Separation
Authors: R. Alfaro, C. Alvarez, A. Andrés, E. Anita-Rangel, M. Araya, J. C. Arteaga-Velázquez, D. Avila Rojas, H. A. Ayala Solares, R. Babu, P. Bangale, E. Belmont-Moreno, A. Bernal, T. Capistrán, A. Carramiñana, F. Carreón, S. Casanova, U. Cotti, E. De la Fuente, D. Depaoli, P. Desiati, N. Di Lalla, R. Diaz Hernandez, M. A. DuVernois, J. C. Díaz-Vélez, K. Engel, T. Ergin, C. Espinoza, K. L. Fan, N. Fraija, S. Fraija, J. A. García-González, F. Garfias, N. Ghosh, A. Gonzalez Muñoz, M. M. González, J. A. González, J. A. Goodman, S. Groetsch, J. P. Harding, S. Hernández-Cadena, I. Herzog, D. Huang, P. Hüntemeyer, A. Iriarte, S. Kaufmann, D. Kieda, K. Leavitt, J. Lee, H. León Vargas, J. T. Linnemann, A. L. Longinotti, G. Luis-Raya, K. Malone, O. Martinez, J. Martínez-Castro, J. A. Matthews, P. Miranda-Romagnoli, P. E. Mirón-Enriquez, J. A. Montes, J. A. Morales-Soto, E. Moreno, M. Najafi, A. Nayerhoda, L. Nellen, N. Omodei, M. Osorio, E. Ponce, Y. Pérez Araujo, E. G. Pérez-Pérez, C. D. Rho, A. Rodriguez Parra, D. Rosa-González, M. Roth, H. Salazar, A. Sandoval, J. Serna-Franco, A. J. Smith, Y. Son, R. W. Springer, O. Tibolla, K. Tollefson, I. Torres, R. Torres-Escobedo, E. Varela, L. Villaseñor, X. Wang, Z. Wang, I. J. Watson, H. Wu, S. Yu, H. Zhou, C. de León
Figure caption: Feature importance plot: the input 20 features are listed sorted by the importance value.
Paper: Breaking Gyrochronology through the Collapse of Coronal Winds
Authors: Michaël Lévesque, Paul Charbonneau
Figure caption: Break of the Skumanich law from our rotational evolution model using different values of σ in Equation (13). The figures show the evolution of (A) the ratio PRef/Pσ of rotation periods, (B) ﹩\dot{J}﹩, (C) ﹩\dot{M}﹩, and (D) S as a function of time. Here PRef is the rotation period for a model for which the temperature is fixed, Pσ is the period for models where the temperature evolves as described by Equation (13), and S is the integrated volumetric heating rates as described by Equation (14). The same color coding applies to all panels.
Paper: Breaking Gyrochronology through the Collapse of Coronal Winds
Authors: Michaël Lévesque, Paul Charbonneau
Figure caption: Correlation between integrated volumetric heating rates and mass-loss rate (top panel) and angular momentum loss rate (bottom panel) for WD solutions of various α and T0. All wind solutions are computed at the solar rotation rate and surface magnetic field strength, and mass-loss rates are computed for a base density ρ0 = 1014mp m−3. The base temperature T0 ranges from 0.85 to 2.94 MK and increases upward along each sequence of colored dots. The lines in panel (B) are isotherms for the values T0 = [1.21, 1.4, 1.55, 2.03, 2.94] MK, from bottom to top.
Paper: On the Formation of Star-forming Galaxies Having Anomalously Low-metallicity Regions: The Role of Galaxy Interaction and Circumgalactic Medium/Intergalactic Medium Accretion
Authors: Yibo Cao, Zhizheng Pan
Figure caption: Distribution of the metallicity deviation of SF spaxels in the late-type galaxies with stellar mass >109M⊙ (green histogram). The blue dashed curve is a Gaussian-fitted profile for “normal” spaxels, with mean value at 0 and a standard deviation of σZ = 0.04. The orange solid curve is the residual of the fitted Gaussian profile subtracted from the negative side of the histogram. The vertical black dotted line marks where ﹩{\rm{\Delta }}{\mathrm{log}}\,({\rm{O}}/{\rm{H}})=-0.08﹩. ALM spaxels are selected as those with ﹩{\rm{\Delta }}{\mathrm{log}}\,({\rm{O}}/{\rm{H}})\lt -0.08﹩.
Paper: Breaking Gyrochronology through the Collapse of Coronal Winds
Authors: Michaël Lévesque, Paul Charbonneau
Figure caption: Integrated volumetric heating rates for WD solutions of various α and T0, expressed as energy flux at the base of the wind, r0/R⊙ = 1.15. As in Figure 5, all wind solutions are computed at the solar rotation rate and surface magnetic field strength.
Paper: Breaking Gyrochronology through the Collapse of Coronal Winds
Authors: Michaël Lévesque, Paul Charbonneau
Figure caption: Panel (A) shows radial profiles of volumetric power input in a sample of polytropic WD wind solutions, reconstructed a posteriori via Equation (14). Panel (B) shows the corresponding flow profiles. All solutions are computed for solar rotation and surface magnetic field strength. The filled dots indicate the position of the Alfvén radius rA in each solution.
Paper: On the Formation of Star-forming Galaxies Having Anomalously Low-metallicity Regions: The Role of Galaxy Interaction and Circumgalactic Medium/Intergalactic Medium Accretion
Authors: Yibo Cao, Zhizheng Pan
Figure caption: The relation between stellar mass and H I gas fraction log(MH I/M*) for ALM galaxies and normal SFGs. ALM galaxies and normal SFGs are shown in orange and blue symbols, respectively. For each sample, the running median value is shown in solid line, and the shaded region indicates the 16–84 percentile.
Paper: On the Formation of Star-forming Galaxies Having Anomalously Low-metallicity Regions: The Role of Galaxy Interaction and Circumgalactic Medium/Intergalactic Medium Accretion
Authors: Yibo Cao, Zhizheng Pan
Figure caption: The relation between metallicity and stellar surface mass density, i.e., the local Σ*–Z relation. The circles represent the peaks of metallicity distributions in each stellar mass and surface mass density bin. The colorful background shows the distribution of all pure SF spaxels in late-type galaxies with stellar mass >108M⊙. The bin of <109M⊙ is only used for interpolation, and we do not further analyze the galaxies in this bin.
Paper: On the Formation of Star-forming Galaxies Having Anomalously Low-metallicity Regions: The Role of Galaxy Interaction and Circumgalactic Medium/Intergalactic Medium Accretion
Authors: Yibo Cao, Zhizheng Pan
Figure caption: Histograms of the ratio of the number of ALM spaxels to the number of SF spaxels in each SFG. The blue histogram represents all SFGs, while the orange one indicates those SFGs having more than 20 ALM spaxels. The dashed line located at 0.04 is the fraction threshold used as the second criterion to define galaxies having ALM regions.
Paper: Long-term Simultaneous 2.25/8.60 GHz Monitoring of the Newly Discovered Repeating FRB 20240114A
Authors: Xiao-Wei Wang,  啸 威 王, Zhen Yan,  振 闫, Zhi-Qiang Shen,  志 强 沈, Ke-Jia Lee,  柯 伽 李, Ya-Jun Wu,  亚 军 吴, Rong-Bing Zhao,  融 冰 赵, Jie Liu,  杰 刘, Rui Wang,  睿 王, Kuo Liu,  阔 刘, Yuan-Chuan Zou,  远 川 邹, Zhi-Peng Huang, 志 鹏 黄, Chu-Yuan Zhang,  楚 原 张, Fan Yang,  帆 杨, Zhen-Long Liao,  振 龙 廖, Yang-Yang Lin,  扬 洋 林
Figure caption: Dynamic spectra of a subset of bursts detected at 2.25 GHz. Each burst was incoherently dedispersed to a DM of 527.7 pc cm−3. In each panel, the bottom subpanel shows the dynamic spectrum with a frequency resolution of 0.977 MHz and a time resolution shown in the top right corner of the top subpanel. Each top subpanel also contains the burst profile (averaging over the entire frequency band), with the burst ID in the top left corner and a shaded area representing the on-burst region (see Section 3.2 for the definition of the on-burst region). The time-averaged spectrum of the on-burst region is displayed in each right subpanel. The horizontal white lines in the dynamic spectrum, with red markers on the left side, represent masked frequency channels contaminated by RFI. For visual purposes, the intensity values of each dynamic spectrum are saturated at the 2nd and 98th percentiles.
Paper: Long-term Simultaneous 2.25/8.60 GHz Monitoring of the Newly Discovered Repeating FRB 20240114A
Authors: Xiao-Wei Wang,  啸 威 王, Zhen Yan,  振 闫, Zhi-Qiang Shen,  志 强 沈, Ke-Jia Lee,  柯 伽 李, Ya-Jun Wu,  亚 军 吴, Rong-Bing Zhao,  融 冰 赵, Jie Liu,  杰 刘, Rui Wang,  睿 王, Kuo Liu,  阔 刘, Yuan-Chuan Zou,  远 川 邹, Zhi-Peng Huang, 志 鹏 黄, Chu-Yuan Zhang,  楚 原 张, Fan Yang,  帆 杨, Zhen-Long Liao,  振 龙 廖, Yang-Yang Lin,  扬 洋 林
Figure caption: Top panel: average burst rate per TMRT 2.25 GHz observation (red circles), with 1σ upper limits indicated for nondetections (gray arrows). Middle panel: cumulative number of bursts detected by TMRT (black solid line) and the duration of each observation (blue bars). Bottom panel: cumulative burst counts reported by various telescopes at different central frequencies: <1 GHz (cyan), 1−2 GHz (blue), and 2−3 GHz (violet). Except for our detections, other data are quoted from D. M. Hewitt et al. (2024a), P. Joshi et al. (2024), A. Kumar et al. (2024b), P. Limaye & L. Spitler (2024), O. S. Ould-Boukattine et al. (2024a, 2024b, 2025), D. Pelliciari et al. (2024b, 2024c), J. Tian et al. (2024b), J.-T. Xie et al. (2024), M. Bhardwaj et al. (2025), F. Eppel et al. (2025), Y.-X. Huang et al. (2025), U. Panda et al. (2025), K. Shin et al. (2025), and J.-S. Zhang et al. (2025a). The gray shaded region in all panels represents the TMRT observing gap between 2024 February 24 and June 29.
Paper: Evolution of Low Surface Brightness Ultrathin Galaxies: The Role of Dark Matter Halo and Bar Formation on Disk Thickness
Authors: K. Aditya, Arunima Banerjee
Figure caption: The time evolution of hz/RD and the amplitude of the m = 2 modes for different models presented in our work. In the top two panels, we show the models c2, c6, and c12, and in the following panels, we show their massive counterparts with a higher stellar mass fraction. We indicate the typical extent of the stellar disk with a vertical dashed line at 4RD. The horizontal line indicates the thin-disk criterion: hz/RD = 0.1.
Paper: Evolution of Low Surface Brightness Ultrathin Galaxies: The Role of Dark Matter Halo and Bar Formation on Disk Thickness
Authors: K. Aditya, Arunima Banerjee
Figure caption: Initial conditions corresponding to various thin-disk models in our study. All the models have a total mass equal to 1011M⊙ and hz/RD ≤ 0.1. The models in the first row with labels c2, c6, and c12 have a stellar mass fraction equal to 0.008 and DM concentration equal to 2, 6, and 12, respectively. The model c6 matches the observed properties of the thinnest known galaxy, FGC 2366. The models in the second row with labels c2-M1, c6-M1, and c12-M1 have a higher stellar mass fraction equal to 0.02 and a DM halo concentration indicated by their labels. The models in the third row, labeled as c2-M2, c6-M2, and c12-M2, have the highest stellar mass fraction among our models, equal to 0.04.
Paper: Evolution of Low Surface Brightness Ultrathin Galaxies: The Role of Dark Matter Halo and Bar Formation on Disk Thickness
Authors: K. Aditya, Arunima Banerjee
Figure caption: The face-on and the edge-on orientation of the snapshots at T = 4.5 Gyr. We show the face-on and the edge-on view corresponding to c2, c6, and c12 in the top two panels, respectively, and their massive counterparts with a higher stellar mass fraction in the following panels.
Paper: Evolution of Low Surface Brightness Ultrathin Galaxies: The Role of Dark Matter Halo and Bar Formation on Disk Thickness
Authors: K. Aditya, Arunima Banerjee
Figure caption: The values of hz/RD and the Fourier amplitude of the m = 2 mode at T = 5 Gyr for different models. The solid lines represent the models c2-M1 and c2-M2, which undergo significant disk heating concurrent with the growth of stellar bars. The other models in the study resist bar formation, thereby evading bar-driven disk thickening. The horizontal line indicates the thin-disk criterion (hz/RD) = 0.1, and the vertical line represents 4RD, beyond which the surface density of the thin disk becomes negligible.