adsabs/AstroCLIMB huggingface dataset:
Paper: Effects of Nonresonant Instability and Magnetic Reconnection on Ion Heating and Acceleration in Quasi-parallel Shock Waves
Authors: N. Bessho, J. Ng, L.-J. Chen, M. Hesse, L. B. Wilson, J. E. Stawarz
Figure caption: (a), (b) Ion temperature Ti and ion density ni at Ωit = 17.12. The red dot (inside the island) and the white dot (outside the island) are the positions where the ion distribution functions are taken. The magenta ovals are regions where waves with λ ∼ 2di are seen. (c), (d) Ti and ni at Ωit = 18.75. Black lines in (a)–(d) are magnetic field lines. (e), (f) Distribution functions at the white dot (outside the island) and at the red dot (inside the island) at Ωit = 17.12. (g), (h) Distribution functions at Ωit = 18.75.
Paper: Effects of Nonresonant Instability and Magnetic Reconnection on Ion Heating and Acceleration in Quasi-parallel Shock Waves
Authors: N. Bessho, J. Ng, L.-J. Chen, M. Hesse, L. B. Wilson, J. E. Stawarz
Figure caption: (a), (b) Current density Jz and ion temperature Ti at Ωit = 20.31. Black curves are magnetic field lines. Ion-scale magnetic islands are indicated by #1 and #2. (c) Ion energy distribution functions in five regions. Each color of curve corresponds to each x interval indicated in the panel. The dashed vertical line indicates the Lorentz factor at the half maximum of the black distribution, at γ = 1.007. (d) The energy distribution in 44 < x/di < 48 (red), the energy distribution in the region that includes island #2 (blue), and that in the region outside the island (green).
Paper: Effects of Nonresonant Instability and Magnetic Reconnection on Ion Heating and Acceleration in Quasi-parallel Shock Waves
Authors: N. Bessho, J. Ng, L.-J. Chen, M. Hesse, L. B. Wilson, J. E. Stawarz
Figure caption: (a) Ion temperature Ti at Ωit = 20.31. The black dot (inside the top ion-scale island) and the white dot (outside the island) are the positions where the ion distribution functions are taken. (b), (c), (d) Distribution functions at the white dot (outside the island). (e), (f), (g) Distribution functions at the black dot (inside the island).
Paper: Effects of Nonresonant Instability and Magnetic Reconnection on Ion Heating and Acceleration in Quasi-parallel Shock Waves
Authors: N. Bessho, J. Ng, L.-J. Chen, M. Hesse, L. B. Wilson, J. E. Stawarz
Figure caption: (a) Current density Jz at Ωit = 18.75. (b) Jz at Ωit = 20.31. Black curves are magnetic field lines. Ion-scale magnetic islands are indicated by #1 and #2. The 1D profiles of 〈By〉 (By averaged over y), By along y = 10di, and y = 20di are shown below each 2D plot. (c), (d) The ion fluid velocity Viy and the electron fluid velocity Vey around island #2. The blue X mark and the red X mark represent the X-lines in the ion-coupled reconnection region and an electron-only reconnection region, respectively.
Paper: Petrogenesis of Lunar Farside Highland Lithologies: Implications for Lunar Crust Formation
Authors: Jiaqi Zheng, Qi He, Zaicong Wang, Jun Huang, J. F. Pernet-Fisher, Yiheng Li, Xu Chen, Keqing Zong, Ioannis Baziotis, Long Xiao, Zhenbing She
Figure caption: Major element characteristics of plagioclases in CE-6 FANs compared with plagioclases in Apollo FANs and lunar anorthositic meteorites. (a) MgO contents (wt %) vs. anorthite content (An) in plagioclase (100 × Ca/(Ca + Na + K), in moles); (b) FeO contents (wt %) vs. An in plagioclase from FANs and other lunar meteorites. The major-element data of Apollo FANs and lunar anorthositic meteorites come from J. Gross et al. (2014), J. J. McGee (1993), J. J. Papike et al. (1997), J. F. Pernet-Fisher et al. (2019), S. E. Roberts et al. (2019), S. S. Russell et al. (2014) and D. Ji & N. Dygert (2023).
Paper: Petrogenesis of Lunar Farside Highland Lithologies: Implications for Lunar Crust Formation
Authors: Jiaqi Zheng, Qi He, Zaicong Wang, Jun Huang, J. F. Pernet-Fisher, Yiheng Li, Xu Chen, Keqing Zong, Ioannis Baziotis, Long Xiao, Zhenbing She
Figure caption: Variations in plagioclase chemistry measured in the clast (this study). Eu/Sm values are chondrite normalized, and scale is inverted to reflect Mg# vs. An# trends in Apollo highland rock suite mafic and felsic minerals (after S. S. Russell et al. 2014). Data from Apollo highland rock suites are from J. J. Papike et al. (1996, 1997), C. Floss et al. (1998).
Paper: Petrogenesis of Lunar Farside Highland Lithologies: Implications for Lunar Crust Formation
Authors: Jiaqi Zheng, Qi He, Zaicong Wang, Jun Huang, J. F. Pernet-Fisher, Yiheng Li, Xu Chen, Keqing Zong, Ioannis Baziotis, Long Xiao, Zhenbing She
Figure caption: Lithologic classification for the clasts. Modal mineralogy determined from CE-6 samples in this study from BSE images and TIMA maps.
Paper: Petrogenesis of Lunar Farside Highland Lithologies: Implications for Lunar Crust Formation
Authors: Jiaqi Zheng, Qi He, Zaicong Wang, Jun Huang, J. F. Pernet-Fisher, Yiheng Li, Xu Chen, Keqing Zong, Ioannis Baziotis, Long Xiao, Zhenbing She
Figure caption: Effects of mineral abundance variations on the measured bulk REE compositions for the noritic anorthosite clast 4–28.
Paper: Deep Potential: Recovering the Gravitational Potential and Local Pattern Speed in the Solar Neighborhood with GDR3 Using Normalizing Flows
Authors: Taavet Kalda, Gregory M. Green
Figure caption: Illustration of the various models trained for Deep Potential, which are used to both determine a best estimate of the potential and quantify its uncertainties. Squares indicate models, circles indicate data sets, nodes colored orange pertain to the distribution function, and nodes colored blue pertain to the gravitational potential and pattern speed Ωp. To obtain the best-fit value for the gravitational potential and Ωp, we first train 16 normalizing flows (with different random seeds) to approximate the distribution function. We obtain a best estimate of the distribution function and its gradients by taking the median of the 16 flows and of their gradients (ensemble {∂f/∂x, ∂f/∂v, f}). We use this best-fit distribution function to train 16 neural networks and pattern speeds (with different random seeds). The best-fit pattern speed and potential (or its derivatives) are defined by the median of these 16 pattern speeds and neural networks (or of their derivatives). We additionally estimate various sources of errors in the potential. For example, to quantify the effect of observational noise, we train eight distribution functions on Gaia data with added noise. For each distribution function, we train a potential. From this ensemble, we can calculate variance in the inferred pattern speed, potential, and derived quantities. From top to bottom in the figure, the following uncertainties are quantified: observational errors in x0 and v0 by resampling the respective quantities based on variations in the literature values, fluctuations in the random seed and convergence of the normalizing flow and neural network architecture, observational errors in the source data by resampling the six-dimensional positions of stars based on their quoted errors, and finally, statistical uncertainties from the finite size of the data set using bootstrap on the training data.
Paper: Deep Potential: Recovering the Gravitational Potential and Local Pattern Speed in the Solar Neighborhood with GDR3 Using Normalizing Flows
Authors: Taavet Kalda, Gregory M. Green
Figure caption: Distribution of scalar value quantities from different uncertainty estimation pipelines visualized in Figure 13. The scalar quantities are the average acceleration in the solar neighborhood a⊙,x, a⊙,y, a⊙,z; the pattern speed of the solar neighborhood within 1 kpc from the Sun; and the average total matter density ρ(R = R0) at the solar distance R = R0 from the Galactic center. The bottom right diagram visualizes which volumes the quantities are averaged over. The central 0.1 kpc and outer 0.8 kpc are excluded due to a lack of data and systematics from edge effects, respectively. In each subplot, the columns represent the spread of values due to the following five effects: adding observational noise to the observed six-dimensional kinematics of the training data (“added observational noise”), resampling the stars in the training data using bootstrap (“bootstrap”), variations in the starting seed of the normalizing flows representing the distribution function (“DF seed”), variations in the starting seed of the neural network representing the gravitational potential while using the best-fit distribution function (“potential seed”), and adding observational noise to the literature values of x0, v0 while using the best-fit distribution function (“x0, v0 noise”). The best-fit value is calculated using the median of the “potential seed.” For discussion, see Appendix C.
Paper: Deep Potential: Recovering the Gravitational Potential and Local Pattern Speed in the Solar Neighborhood with GDR3 Using Normalizing Flows
Authors: Taavet Kalda, Gregory M. Green
Figure caption: Two-dimensional projections of the model density ρ = ∇2Φ(x)/(4πG) within r < 0.8 kpc. The projections are in the x–y plane (left panel), y–z plane (middle panel), and x–z plane (right panel) and are averaged over the respective third spatial Cartesian coordinate, between −0.25 and 0.25 kpc. The resulting projections show the disk of the Milky Way but exhibit significant fluctuations, which may have a number of causes. Taking the second derivatives of our fully nonparametric potential will amplify small, spurious fluctuations (compared with the smoother acceleration fields in Figure 6, based on the first derivatives of the potential). Real nonstationarities in the Milky Way, which violate our assumptions, may manifest as spurious density fluctuations in our model. Some of the fluctuations may also trace real variations in the underlying density field. Note that the inner 100 pc (where we have no tracer stars) is excluded from our calculations of the average density, causing visible, sharp density changes in the left two panels.
Paper: Deep Potential: Recovering the Gravitational Potential and Local Pattern Speed in the Solar Neighborhood with GDR3 Using Normalizing Flows
Authors: Taavet Kalda, Gregory M. Green
Figure caption: Comparison of the residual nonstationarity ﹩\partial {\mathrm{ln}}f/\partial t﹩ near the solar neighborhood (averaged within 200 pc from the Sun) for two models for the gravitational potential, one assuming the best-fit rotation speed Ωp = 28.2 km s−1 kpc−1 and another without rotation Ωp = 0. The residue may represent inherent nonstationarity in the Milky Way, as well as imperfections in our models of the distribution function and gravitational potential. We observe that the characteristic timescale for changes within the phase-space density is in the order of 20 Myr and 5 Myr for the rotating and nonrotating cases, respectively.
Paper: Deep Potential: Recovering the Gravitational Potential and Local Pattern Speed in the Solar Neighborhood with GDR3 Using Normalizing Flows
Authors: Taavet Kalda, Gregory M. Green
Figure caption: Imprint of the phase spiral in the z–vz projection in the residual nonstationarity ﹩\langle \,\partial {\mathrm{ln}}f/\partial t\rangle ﹩ left over by the model (left panel) and a common tracer, 〈vΦ〉, used to highlight the phase spiral (right panel). The residual nonstationarity can be interpreted as the inverse of the characteristic timescale over which the phase-space density at a specific volume undergoes significant changes. Both panels are averaged over a volume near the Sun (averaged within ∣x∣, ∣z∣, ∣y∣ < 500 pc).
Paper: Deep Potential: Recovering the Gravitational Potential and Local Pattern Speed in the Solar Neighborhood with GDR3 Using Normalizing Flows
Authors: Taavet Kalda, Gregory M. Green
Figure caption: Comparisons between line-of-sight accelerations ﹩{a}_{{\rm{LOS}}}\equiv ({\boldsymbol{a}}-{{\boldsymbol{a}}}_{\odot })\cdot \hat{r}﹩ measured using binary pulsars (A. Moran et al. 2024), Deep Potential (this work), and MWPotential2014 (J. Bovy 2015). For binary pulsars and Deep Potential, we show 1σ uncertainties.
Paper: Spectral Properties of Globally Distributed ENA Fluxes across Diverse Regions of the Heliosphere
Authors: Thomas K. Kim, Daniel B. Reisenfeld, Eric J. Zirnstein, Paul H. Janzen, Asher S. Merrill, Sung Jun Noh, Nehpreet K. Walia, Yue Chen, Fan Guo, Dave Osthus, Lauren J. Beesley, Dae-Kyu Shin, Justyna M. Sokół, Maciej Bzowski, Marzena A. Kubiak, Czeslaw Porowski, Herbert O. Funsten
Figure caption: ENA spectral index ﹩{{\gamma }}_{{\rm{all}}}﹩ (using fluxes in all IBEX-Hi energy channels) inferred heliosheath proton partial temperature ﹩{T}_{\mathrm{HS}}﹩, and LOS-integrated heliosheath proton partial pressure ﹩P\cdot L﹩ at 1 au (left three columns) and at the heliosheath midpoint (right three columns). A higher spectral index, defined by ﹩j={j}_{0}\cdot {E}^{-\gamma }﹩, indicates a steeper energy spectra. All values are directly derived from ENA flux (or time-shifted ENA flux for the heliosheath) measured by IBEX-Hi. The solid black curves in the sky maps represent the B–V plane.
Paper: Spectral Properties of Globally Distributed ENA Fluxes across Diverse Regions of the Heliosphere
Authors: Thomas K. Kim, Daniel B. Reisenfeld, Eric J. Zirnstein, Paul H. Janzen, Asher S. Merrill, Sung Jun Noh, Nehpreet K. Walia, Yue Chen, Fan Guo, Dave Osthus, Lauren J. Beesley, Dae-Kyu Shin, Justyna M. Sokół, Maciej Bzowski, Marzena A. Kubiak, Czeslaw Porowski, Herbert O. Funsten
Figure caption: Yearly distribution of spectral indices from 0.71–4.29 keV. The vertical dashed lines represent overall median values, and the gray areas span the standard deviation for individual years. The black dots for individual years represent the distribution’s median value. Associated heliosheath proton temperature ﹩{T}_{\mathrm{HS}}﹩ and LOS-integrated proton pressure ﹩P\cdot L﹩ median values and standard deviations are denoted by red and blue dots and shades, respectively.
Paper: Spectral Properties of Globally Distributed ENA Fluxes across Diverse Regions of the Heliosphere
Authors: Thomas K. Kim, Daniel B. Reisenfeld, Eric J. Zirnstein, Paul H. Janzen, Asher S. Merrill, Sung Jun Noh, Nehpreet K. Walia, Yue Chen, Fan Guo, Dave Osthus, Lauren J. Beesley, Dae-Kyu Shin, Justyna M. Sokół, Maciej Bzowski, Marzena A. Kubiak, Czeslaw Porowski, Herbert O. Funsten
Figure caption: IPS-derived solar wind speed from C. Porowski et al. (2022), sunspot number, and 1 au solar wind dynamics pressure. ENA-derived latitudinal boundaries between fast and slow solar wind, determined using ﹩{{\gamma }}_{{\rm{all}}}=1.85﹩, are shown for both 1 au and the heliosheath, using the only upwind portion of the sky map (ecliptic longitudes spanning from −180° to 0°). These ENA boundaries are backward time-shifted by 2.4 (1 au) and 0.9 yr (heliosheath) as suggested by E. Zirnstein et al. (2017a).
Paper: Spectral Properties of Globally Distributed ENA Fluxes across Diverse Regions of the Heliosphere
Authors: Thomas K. Kim, Daniel B. Reisenfeld, Eric J. Zirnstein, Paul H. Janzen, Asher S. Merrill, Sung Jun Noh, Nehpreet K. Walia, Yue Chen, Fan Guo, Dave Osthus, Lauren J. Beesley, Dae-Kyu Shin, Justyna M. Sokół, Maciej Bzowski, Marzena A. Kubiak, Czeslaw Porowski, Herbert O. Funsten
Figure caption: Yearly distribution of spectral breaks (spectral index differences ﹩{{\gamma }}_{{\rm{high}}}-{{\gamma }}_{{\rm{low}}}﹩). Similar to Figure 10, heliosheath proton temperature ﹩{T}_{\mathrm{HS}}﹩ and LOS-integrated proton pressure ﹩{P}\cdot {L}﹩ are shown as red and blue dots and shaded regions, respectively. The solid black line at zero implies that the ENA energy spectra can be accurately represented by a single power law across IBEX-Hi energy channels.
Paper: Spectral Properties of Globally Distributed ENA Fluxes across Diverse Regions of the Heliosphere
Authors: Thomas K. Kim, Daniel B. Reisenfeld, Eric J. Zirnstein, Paul H. Janzen, Asher S. Merrill, Sung Jun Noh, Nehpreet K. Walia, Yue Chen, Fan Guo, Dave Osthus, Lauren J. Beesley, Dae-Kyu Shin, Justyna M. Sokół, Maciej Bzowski, Marzena A. Kubiak, Czeslaw Porowski, Herbert O. Funsten
Figure caption: Average ﹩{P}\cdot {L}﹩ maps derived from the heliosheath sky maps spanning 2009–2018, centered at upwind (top panel) and downwind (bottom panel). Upwind and downwind information was obtained from P. Swaczyna et al. (2022). Solid black lines represent GDF sky map boundaries defined with specific ﹩{P}\cdot {L}﹩ values for upwind, north pole, south pole, central tail, port lobe, and starboard lobe. This study investigates temporal evolution of spectral index, heliosheath proton temperature ﹩{T}_{\mathrm{HS}}﹩, and LOS-integrated heliosheath proton pressure ﹩P\cdot L﹩ in these six different regions. The B–V plane is indicated by thick black sinusoidal curves.
Paper: Spectral Properties of Globally Distributed ENA Fluxes across Diverse Regions of the Heliosphere
Authors: Thomas K. Kim, Daniel B. Reisenfeld, Eric J. Zirnstein, Paul H. Janzen, Asher S. Merrill, Sung Jun Noh, Nehpreet K. Walia, Yue Chen, Fan Guo, Dave Osthus, Lauren J. Beesley, Dae-Kyu Shin, Justyna M. Sokół, Maciej Bzowski, Marzena A. Kubiak, Czeslaw Porowski, Herbert O. Funsten
Figure caption: Average ENA energy spectra at six different regions for fluxes time-shifted to the heliosheath. Energy spectra are first computed for individual sky map pixels (see Figure 6) and then averaged over regions defined in Figure 8.
Paper: Spectral Properties of Globally Distributed ENA Fluxes across Diverse Regions of the Heliosphere
Authors: Thomas K. Kim, Daniel B. Reisenfeld, Eric J. Zirnstein, Paul H. Janzen, Asher S. Merrill, Sung Jun Noh, Nehpreet K. Walia, Yue Chen, Fan Guo, Dave Osthus, Lauren J. Beesley, Dae-Kyu Shin, Justyna M. Sokół, Maciej Bzowski, Marzena A. Kubiak, Czeslaw Porowski, Herbert O. Funsten
Figure caption: Sensitivity test of the ENA spectral index (0.71–4.29 keV) to varying number of sky map pixels in the defined boundary region, ranging from 70%–130%, in mid-latitude GDF regions (upwind, central tail, port lobe, and starboard lobe).
Paper: Spectral Properties of Globally Distributed ENA Fluxes across Diverse Regions of the Heliosphere
Authors: Thomas K. Kim, Daniel B. Reisenfeld, Eric J. Zirnstein, Paul H. Janzen, Asher S. Merrill, Sung Jun Noh, Nehpreet K. Walia, Yue Chen, Fan Guo, Dave Osthus, Lauren J. Beesley, Dae-Kyu Shin, Justyna M. Sokół, Maciej Bzowski, Marzena A. Kubiak, Czeslaw Porowski, Herbert O. Funsten
Figure caption: Left three columns: GDF maps for 1 au without time correction. Right three columns: GDF maps at the heliosheath with backward time-shift and time correction for 0.71, 1.74, and 4.29 keV. White empty pixels in the heliosheath represent regions where extrapolation is needed. Extrapolation is more prevalent for ENAs with lower energies, given their slower transit time. This study exclusively analyzes spectral indices when flux for all IBEX-Hi energy channels is present.
Paper: Spectral Properties of Globally Distributed ENA Fluxes across Diverse Regions of the Heliosphere
Authors: Thomas K. Kim, Daniel B. Reisenfeld, Eric J. Zirnstein, Paul H. Janzen, Asher S. Merrill, Sung Jun Noh, Nehpreet K. Walia, Yue Chen, Fan Guo, Dave Osthus, Lauren J. Beesley, Dae-Kyu Shin, Justyna M. Sokół, Maciej Bzowski, Marzena A. Kubiak, Czeslaw Porowski, Herbert O. Funsten
Figure caption: Similar to Figure 10 but showing a comparison between 1 au (left column) and heliosheath (right column) for upwind, north pole, and south pole.
Paper: Investigating Little Red Dots with UV Excess: Are They the High-redshift Siblings of Blue Hot Dust-obscured Galaxies?
Authors: Lulu Bao, Chao-Wei Tsai, Jingwen Wu, Tao Wang, Guodong Li, Roberto J. Assef, Tanio Diaz-Santos, Peter R. M. Eisenhardt, Daniel Stern, Andrew W. Blain
Figure caption: Comparison between the normalized SEDs of LRDs (upper panel) and BHDs (lower panel). The gray-shaded region represents the distribution of Hot DOGs photometry at z > 1.6. The orange lines show dust models with temperatures of 450 and 60 K and emissivity index β = 1.5, characterizing the radial slope of the dust density in the single power-law (SPL) model of C.-W. Tsai et al. (2015). The vertical green dashed lines mark the locations of NIRCam/F444W at the median redshifts of BHDs (z = 2.3) and LRDs (z = 6.5). Top: the black curve represents the stacked SED of LRDs from Akins25, while the dashed line extending beyond observed 18 μm shows multitemperature dust emission assumed by weak far-infrared observations. The blue and red curves with shaded regions correspond to AGN templates with dust attenuation values of AV ∼ 0.02 ± 0.01 mag and 2.5 ± 1.4 mag, respectively. The dark-blue points indicate the stacked photometry of the compiled 326 “V-shape” LRD sample from Akins25 and V. Kokorev et al. (2024). The black-blue triangle represents the stacked MIRI F1800W photometry of six MIRI-observed Akins25 sources in the “V-shape” subsample, shown as upper limits due to nondetection in all “V-shape” subsamples. Bottom: the black curve represents the median-stacked SED of BHDs. The blue and red curves, along with their shaded regions, correspond to AGN templates from R. J. Assef et al. (2010), modeled with dust attenuation values of AV ∼ 0.2 ± 0.2 mag and 18 ± 5 mag, respectively. The brown dashed line shows the contribution of the host galaxy.
Paper: Investigating Little Red Dots with UV Excess: Are They the High-redshift Siblings of Blue Hot Dust-obscured Galaxies?
Authors: Lulu Bao, Chao-Wei Tsai, Jingwen Wu, Tao Wang, Guodong Li, Roberto J. Assef, Tanio Diaz-Santos, Peter R. M. Eisenhardt, Daniel Stern, Andrew W. Blain
Figure caption: The observed-frame SEDs of LRDs and BHDs. The SEDs of BHDs at z = 2.3 (blue solid curve) are scaled to their median bolometric luminosity Lbol,med = 6.4 × 1013L⊙. The stacked SEDs of the Akins25 sample at z = 6.5 and scaled to Lbol,med = 2.3 × 1012L⊙ from H. B. Akins et al. (2025) are represented by the red curve. The orange curve shows the SED of BHDs at z = 6.5, scaled to the luminosity for the typical SMBH mass of ∼108M⊙ in LRDs (J. E. Greene et al. 2024) and assuming the median accretion rate of BHDs. The small dots in green, blue, and red represent the photometry at F444W, F770W, and F1800W, respectively, for the five LRDs detected in both MIRI bands by G. C. Leung et al. (2024). The red squares mark the photometry of Virgil (P. Rinaldi et al. 2025), a compact and MIRI-red AGN at z = 6.6, with photometry scaled to match the NIRCam/F444W luminosity of the stacked Akins25 LRDs.
Paper: Selection of Dwarf Galaxies Hosting Active Galactic Nuclei: A Measure of Bias and Contamination Using Unsupervised Machine Learning Techniques
Authors: Sogol Sanjaripour, Archana Aravindan, Gabriela Canalizo, Shoubaneh Hemmati, Bahram Mobasher, Alison L. Coil, Barry C. Barish
Figure caption: Color–mass diagrams of all training and test sample galaxies located within the left and right WISE clump cells of the SOM. The bluer u − r colors of the right clump galaxies suggest a population dominated by young starbursts, consistent with contamination in WISE-based AGN selection. The contours and the upper and lower boundaries of the green valley shown in the left panel are adopted from K. Schawinski et al. (2014), who define this transition region in the u − r vs. stellar mass plane based on a volume-limited sample of galaxies from SDSS. These reference lines help distinguish between blue cloud, green valley, and red sequence populations.
Paper: Selection of Dwarf Galaxies Hosting Active Galactic Nuclei: A Measure of Bias and Contamination Using Unsupervised Machine Learning Techniques
Authors: Sogol Sanjaripour, Archana Aravindan, Gabriela Canalizo, Shoubaneh Hemmati, Bahram Mobasher, Alison L. Coil, Barry C. Barish
Figure caption: Distribution of NSA dwarf AGNs and coronal-line emitters mapped onto the trained SOM, which is color coded by the logarithmic median stellar mass of the training galaxies in each cell. Cyan circles indicate the mapped AGNs, while red crosses show the coronal-line emitters.