adsabs/AstroCLIMB huggingface dataset:
Paper: The Green Chile Project. I. A New Catalog of Low-redshift Extended Emission-line Nebulae
Authors: Audrey F. Dijeau, Moire K. M. Prescott, Kelly N. Sanderson
Figure caption: SDSS spectra of the four newly identified candidate AGN-powered EELN. The GCs are labeled according to their GC Spec ID indicated in Table 4. The spectrum of GC02 is normalized to the mean flux in the region between 4595 and 4605 Å, while the spectra of GC05, GC08, and GC11 are normalized to the region between 5995 and 6005 Å. Overplotted are the stacked low-resolution spectrum of the GBs (dark blue), the stacked spectra of star-forming Green Peas (dark green), and Type 2 AGN (dark magenta; M. K. M. Prescott & K. N. Sanderson 2019). The stacked spectra are normalized to the region around 5995–6005 Å.
Paper: The Green Chile Project. I. A New Catalog of Low-redshift Extended Emission-line Nebulae
Authors: Audrey F. Dijeau, Moire K. M. Prescott, Kelly N. Sanderson
Figure caption: Physical properties of simulated EELN that are selected with the magnitude/color-based selection criteria from M. Schirmer et al. (2016). As in Figure 2, the green squares represent at least one generated object with those properties being selected by the magnitude/color-based (M. Schirmer et al. 2016) selection criteria. The left panel shows the continuum slope vs. redshift; the middle panel shows the [O III] rest-frame equivalent width vs. continuum slope; and the right panel shows the [O III] rest-frame equivalent width vs. redshift.
Paper: The Green Chile Project. I. A New Catalog of Low-redshift Extended Emission-line Nebulae
Authors: Audrey F. Dijeau, Moire K. M. Prescott, Kelly N. Sanderson
Figure caption: Physical properties of simulated EELN that are selected with the magnitude/color-based selection criteria from M. Schirmer et al. (2013). The green squares represent at least one generated object with the given physical properties being selected by the magnitude/color-based selection criteria from M. Schirmer et al. (2013). The left panel shows the continuum slope vs. redshift; the middle panel shows the [O III] rest-frame equivalent width vs. continuum slope; and the right panel shows the [O III] rest-frame equivalent width vs. redshift.
Paper: The Green Chile Project. I. A New Catalog of Low-redshift Extended Emission-line Nebulae
Authors: Audrey F. Dijeau, Moire K. M. Prescott, Kelly N. Sanderson
Figure caption: Color–color plot of simulated galaxies with surrounding EELNs. Overlaid are the color cuts from M. Schirmer et al. (2013; black dashed lines). Our new cuts are represented by the pink solid lines; they select objects with [O III] rest-frame equivalent widths greater than or equal to 400 Å and a range of continuum slopes −2 < n < 2.
Paper: The Green Chile Project. I. A New Catalog of Low-redshift Extended Emission-line Nebulae
Authors: Audrey F. Dijeau, Moire K. M. Prescott, Kelly N. Sanderson
Figure caption: Physical properties of EELN that are selected with our new magnitude/color-based selection criteria. As in Figure 3, the green squares represent at least one generated object with those properties being selected by the new magnitude/color-based selection criteria. The left panel shows the continuum slope vs. redshift; the middle panel shows the [O III] rest-frame equivalent width vs. continuum slope; and the right panel shows the [O III] rest-frame equivalent width vs. redshift.
Paper: The Green Chile Project. I. A New Catalog of Low-redshift Extended Emission-line Nebulae
Authors: Audrey F. Dijeau, Moire K. M. Prescott, Kelly N. Sanderson
Figure caption: Simulated SDSS images of a model GB at z ∼ 0.3 with a continuum slope of n = 0 and an [O III]λ5007 rest-frame equivalent width of 600 Å. Each image is 79﹩\mathop{.}\limits^{\unicode{x02033}}﹩2 on each side (a 200 × 200 pixel image with a pixel scale of 0﹩\mathop{.}\limits^{\unicode{x02033}}﹩396 per pixel to match SDSS; D. G. York et al. 2000).
Paper: Fast Catalog Matching for Improved Posteriors via Constrained Clustering
Authors: Jacob Feitelberg, Tamás Budavári, Amitabh Basu
Figure caption: Runtimes for methods on simulated data. In the top left plot, we include all four methods including the exact MIQCP one. We also include the runtime of finding an upper bound on the number of objects in for KMeans and COP-KMeans. In the top right plot, we show the approximate method runtimes with the theoretical maximum number of objects set to 2. Note that KMeans and COP-KMeans run faster than Chainbreaker by several orders of magnitude. In the bottom plot, we show how reducing the upper bound on the number of objects improves the runtimes for KMeans and COP-KMeans. All simulations were run with two simulated objects with centers 0﹩\mathop{.}\limits^{\unicode{x02033}}﹩13 from each other. Each source is drawn from its respective object’s distribution. Each distribution is a two-dimensional Gaussian distribution with a standard deviation of 0﹩\mathop{.}\limits^{\unicode{x02033}}﹩04. For each catalog, we draw 20 sources per object. We repeat each simulation 20 times. Error bars are 1 standard deviation. Note that there is little variance in the runtime of all methods.
Paper: Fast Catalog Matching for Improved Posteriors via Constrained Clustering
Authors: Jacob Feitelberg, Tamás Budavári, Amitabh Basu
Figure caption: HSC group with 425 sources and 84 catalogs. This collection of sources from the HSC is particularly difficult to match because the sources are close together in the shape of a line. We represent the uncertainty (σ) in a source’s location with gray circles around its location. Unlike in our simulations, each source has a different estimated σ, which is estimated based on which telescope captured the source. Chainbreaker, COP-KMeans, and KMeans achieve a log-Bayes factor of −36303, −10537, and −8683, respectively. While this might suggest that KMeans is the better algorithm to use here, the KMeans solution violates constraints by assigning sources from the same catalogs to the same object. All methods have a runtime on our computing setup under 20 s. Chainbreaker, COP-KMeans, and KMeans estimate 16, 15, and 12 objects, respectively.
Paper: Fast Catalog Matching for Improved Posteriors via Constrained Clustering
Authors: Jacob Feitelberg, Tamás Budavári, Amitabh Basu
Figure caption: Three simulated close objects. Objects are simulated with true locations on the perimeter of a circle of radius 0﹩\mathop{.}\limits^{\unicode{x02033}}﹩1. Their centers are denoted with large red cross symbols. We represent the uncertainty (σ) in a source’s location with gray circles around its location. Here, sources are drawn with σ = 0﹩\mathop{.}\limits^{\unicode{x02033}}﹩04. COP-KMeans is the only method that achieves perfect accuracy. Both regular KMeans and Chainbreaker both fail to accurately match sources. The normalized mutual information scores for COP-KMeans, KMeans, and Chainbreaker are 1.00, 0.54, and 0.00, respectively.
Paper: Fast Catalog Matching for Improved Posteriors via Constrained Clustering
Authors: Jacob Feitelberg, Tamás Budavári, Amitabh Basu
Figure caption: Unadjusted Bayes factor incorrectly favors more clusters even where this is only one simulated object. We simulate 200 sources from one true object. We represent the uncertainty (σ) in a source’s location with gray circles around its location. Each source is drawn with σ = 0﹩\mathop{.}\limits^{\unicode{x02033}}﹩04. The true matching is shown on the left, and an incorrect matching is shown on the right. Without our uniform prior adjustment, the log-Bayes factors for the true matching and incorrect matching are 6089 and 6129, respectively, thus favoring the incorrect matching. With the uniform prior adjustment, the log-Bayes factors are 6089 and 5475, respectively. Thus, the correct matching is now favored.
Paper: Fast Catalog Matching for Improved Posteriors via Constrained Clustering
Authors: Jacob Feitelberg, Tamás Budavári, Amitabh Basu
Figure caption: Performance of algorithms vs. distance between objects. Three objects are simulated with centers on the perimeter of circles of varying radii. All methods perform well when the objects are far enough from each other that no groups of sources overlap. COP-KMeans clearly performs the best when objects are closer together. Even when objects overlap completely, COP-KMeans can match some of the sources correctly.
Paper: Utilizing Machine Learning to Predict Host Stars and the Key Elemental Abundances of Small Planets
Authors: Amílcar R. Torres-Quijano, Natalie R. Hinkel, Caleb H. Wheeler, Patrick A. Young, Luan Ghezzi, Augusto P. Baldo
Figure caption: The Mg/Si ratios of stars plotted against their corresponding [Fe/H] abundance. The overlap stars are shown in blue, while the prediction sample stars are plotted in red. The one-dimensional histograms of their [Fe/H] relative distributions are shown at the top of the scatterplot; the Mg/Si relative distributions are shown to the right. We note that the relative distribution of Mg/Si of the small planets is concentrated at a ratio of ≈0.9. Once the subdivision of sub-Neptunes and super-Earths (panels (b) and (c), respectively) was performed, the relative distribution of the Mg/Si of the overlap stars is ≈1.0. For all experiments, this implies that the Mg and Si throughout their systems are mostly present through olivines and pyroxenes (A. Thiabaud et al. 2015b).
Paper: Utilizing Machine Learning to Predict Host Stars and the Key Elemental Abundances of Small Planets
Authors: Amílcar R. Torres-Quijano, Natalie R. Hinkel, Caleb H. Wheeler, Patrick A. Young, Luan Ghezzi, Augusto P. Baldo
Figure caption: Same as Figure 1, but for the abundance feature importance plots obtained for the Experiment 3 (super-Earth) ensembles. While C becomes the second most important feature in Ensemble 2 (panel (b)), Ensemble 3 (panel (c)), and Ensemble 4 (panel (d)) and the third most important feature in Ensemble 1 (panel (a)), this result is due to the use of null values within the ensembles as discussed in Section 3.3. Therefore, the feature importance of C is not heavily considered. We note that Na remains the most important feature for Ensembles 3 and 4, while V remains near the top (although alternating) and Ni is now much farther down. The elements V and Ti vary in importance for Ensemble 3, while Al remains the fourth most important. A similar situation occurs where Al and Ti vary in importance for Ensemble 4, while V remains the third most important.
Paper: Utilizing Machine Learning to Predict Host Stars and the Key Elemental Abundances of Small Planets
Authors: Amílcar R. Torres-Quijano, Natalie R. Hinkel, Caleb H. Wheeler, Patrick A. Young, Luan Ghezzi, Augusto P. Baldo
Figure caption: Similar to Figure 1, but for the molar ratio feature importance plots obtained for the Experiment 3 (super-Earth) ensembles. As explained in Figure 5, the feature importance for C was disregarded because of the use of null values. We note that most of the molar ratios included in Ensemble 5 (panel (a)) varied in terms of importance.
Paper: Utilizing Machine Learning to Predict Host Stars and the Key Elemental Abundances of Small Planets
Authors: Amílcar R. Torres-Quijano, Natalie R. Hinkel, Caleb H. Wheeler, Patrick A. Young, Luan Ghezzi, Augusto P. Baldo
Figure caption: Similar to Figure 1, but for the molar ratios tested in Experiment 1. Given that Fe/Mg is the most important feature for Ensemble 5, this implies that the size of a small planet’s core (Fe/Mg) could be an important factor in determining the presence of a small planet (see Section 2). The molar ratios of Mg/Si and Fe/Si are often the most important features in Ensemble 6, while Mg/O is the most significant in determining the presence of a small planet in Ensemble 7.
Paper: Utilizing Machine Learning to Predict Host Stars and the Key Elemental Abundances of Small Planets
Authors: Amílcar R. Torres-Quijano, Natalie R. Hinkel, Caleb H. Wheeler, Patrick A. Young, Luan Ghezzi, Augusto P. Baldo
Figure caption: The [Na/Fe] vs. [Fe/H] abundance plots (Ensemble 4) showing the stars that are most likely to host a planet (green), the known planet hosts (blue), and the stars less likely to host a planet (orange). Histograms are also included that show the [Na/Fe] relative distribution to the right of the scatterplot and [Fe/H] relative distribution at the top. We note the clear subsolar Na abundances for each experiment’s known and predicted planet hosts (see Section 5).
Paper: Utilizing Machine Learning to Predict Host Stars and the Key Elemental Abundances of Small Planets
Authors: Amílcar R. Torres-Quijano, Natalie R. Hinkel, Caleb H. Wheeler, Patrick A. Young, Luan Ghezzi, Augusto P. Baldo
Figure caption: Same as Figure 1, but for the abundance feature importance plots obtained for the Experiment 2 (sub-Neptune) ensembles. Ensemble 1 (panel (a)) shows Mg as the most important feature for the sub-Neptunes, while Mg and Fe alternate as the most important feature in Ensemble 2 (panel (b)). For Ensemble 3 (panel (c)) and Ensemble 4 (panel (d)), Na and V—and to a lesser extent Ni—are the most important elements for this particular class of planets, similar to Experiment 1.
Paper: Utilizing Machine Learning to Predict Host Stars and the Key Elemental Abundances of Small Planets
Authors: Amílcar R. Torres-Quijano, Natalie R. Hinkel, Caleb H. Wheeler, Patrick A. Young, Luan Ghezzi, Augusto P. Baldo
Figure caption: Similar to Figure 1, but for the molar ratio feature importance plots obtained for the Experiment 2 (sub-Neptune) ensembles. Ensemble 5 (panel (a)) shows variations in the molar ratios of Ti/Mg and Ca/Mg, as well as C/Mg and Si/Mg. Ensemble 6 (panel (b)) exhibits variations among multiple molar ratios (Fe/Si, Ti/Si, Mg/Si, and Ca/Si) to determine the most significant molar ratio in assessing the presence of a sub-Neptune.
Paper: Utilizing Machine Learning to Predict Host Stars and the Key Elemental Abundances of Small Planets
Authors: Amílcar R. Torres-Quijano, Natalie R. Hinkel, Caleb H. Wheeler, Patrick A. Young, Luan Ghezzi, Augusto P. Baldo
Figure caption: Similar to Figure 7, but for [V/Fe] vs. [Fe/H] abundance plots (Ensemble 4).
Paper: Real-time Collision-free Motion Planning for Next-generation Fiber Positioners in LAMOST
Authors: Guifeng Chen, Zhen Zhang, Chunhui Su, YongGang Gu, Chao Zhai
Figure caption: Experiment results: in the first simulation, RFPs move directly toward their targets. In the second simulation, Retract-Rotate-Extend method is used for positioner motion planning. In the third simulation, the proposed real-time method is used for motion planning. In the last simulation, two methods are used simultaneously for motion planning. The distinction between the blue and yellow groups is intended to demonstrate that real-time control can enhance other algorithms.
Paper: Real-time Collision-free Motion Planning for Next-generation Fiber Positioners in LAMOST
Authors: Guifeng Chen, Zhen Zhang, Chunhui Su, YongGang Gu, Chao Zhai
Figure caption: Collision classification diagram: Si represents the starting collision position in trajectory, Ei represents the ending collision positions in trajectory (i = 1, 2), triangle represents moving direction of positioner. H represents the collision at the head of trajectory, T represent collision at the tail of trajectory, M represents collision at the midway of trajectory. The other is a combination of these three situations.
Paper: Real-time Collision-free Motion Planning for Next-generation Fiber Positioners in LAMOST
Authors: Guifeng Chen, Zhen Zhang, Chunhui Su, YongGang Gu, Chao Zhai
Figure caption: Simulation results: in the first simulation, RFPs move directly toward their targets. In the second simulation, Retract-Rotate-Extend method is used for positioner motion planning. In the third simulation, the proposed real-time method is used for motion planning. In the last simulation, two methods are used simultaneously for motion planning. The distinction between the blue and yellow groups is intended to demonstrate that real-time control can enhance other algorithms.
Paper: Real-time Collision-free Motion Planning for Next-generation Fiber Positioners in LAMOST
Authors: Guifeng Chen, Zhen Zhang, Chunhui Su, YongGang Gu, Chao Zhai
Figure caption: Panel (a): positioners have not started moving. Panel (b): the retracting robot retracts its eccentric arm within a safety angle. Panel (c): the retracting robot stays until the other positioner pass through the ending collision position. Panel (d): the retracting robot extends its eccentric arm to its home position. Panel (e): both positioners reach their targets.
Paper: Real-time Collision-free Motion Planning for Next-generation Fiber Positioners in LAMOST
Authors: Guifeng Chen, Zhen Zhang, Chunhui Su, YongGang Gu, Chao Zhai
Figure caption: Panel (a): positioners have not started moving. Panel (b): the extending robot reach its target. Panel (c): the extending robot retracts its eccentric arm within a safety angle. Panel (d): the other robot moves toward its target. Panel (e): the extending robot extends its eccentric arm to its target, so both positioners reach their target.
Paper: Real-time Collision-free Motion Planning for Next-generation Fiber Positioners in LAMOST
Authors: Guifeng Chen, Zhen Zhang, Chunhui Su, YongGang Gu, Chao Zhai
Figure caption: This diagram shows the layout of fibers, RFPs, electronics, MCS and the support structure of the prototype measurement platform.
Paper: Real-time Collision-free Motion Planning for Next-generation Fiber Positioners in LAMOST
Authors: Guifeng Chen, Zhen Zhang, Chunhui Su, YongGang Gu, Chao Zhai
Figure caption: Panel (a): positioners have not started moving. Panel (b): the waiting robot remains at its home position until the other positioner passes through the ending collision position. Panel (c): both positioners reach their targets.
Paper: Real-time Collision-free Motion Planning for Next-generation Fiber Positioners in LAMOST
Authors: Guifeng Chen, Zhen Zhang, Chunhui Su, YongGang Gu, Chao Zhai
Figure caption: Overall flow chart of the real-time collision-free motion planning algorithm.
Paper: Photobombing for the Large Interferometer For Exoplanets (LIFE). A New Criterion for Target Confusion and Application to a Mid-infrared Rotating Nulling Interferometer
Authors: Drinor Cacaj, Daniel Angerhausen, Prabal Saxena, Romain Laugier, Jens Kammerer, Eleonora Alei, Sascha P. Quanz
Figure caption: Shown in blue over Earth’s gray line is when Earth is detected in a case with ϕ ≈ 0.9π/2 rad ≈ 80° and a star distance of d = 10 pc over the period 2020–2030. The colored bars above the gray lines are indicator functions. When above the gray line, they indicate that, at that time, the planet is contaminating Earth locally. This means that at that moment, the planet and Earth satisfy the contamination criterion D<0, appear in close proximity (local contamination), and that Earth is detected (detection is defined in the third line of the above paragraph). However, when the bars are below the gray line, it means that D<0 while the planets are not in close proximity (nonlocal contamination) and that Earth is detected. The next two figures show two examples: one with local contamination and one with nonlocal contamination. Mercury does not contaminate Earth’s spectrum at any time. Total contamination of 4.98% and a contamination probability of 0.0498/0.7188 ≈ 7%.
Paper: Photobombing for the Large Interferometer For Exoplanets (LIFE). A New Criterion for Target Confusion and Application to a Mid-infrared Rotating Nulling Interferometer
Authors: Drinor Cacaj, Daniel Angerhausen, Prabal Saxena, Romain Laugier, Jens Kammerer, Eleonora Alei, Sascha P. Quanz
Figure caption: The Earth’s detection probability for multiple distances and three different inclination angles of the orbit plane for the period 2020–2030. The detection probability is the probability that the integrated S/N of Earth is above the threshold of 7 after an observation of 100 hr, where we assume that planets do not move. In the face-on case, the angular separation between Earth and the Sun is constant; thus, Earth has the same S/N no matter its location in its orbit. When the system is at a far enough distance, the detection probability drops immediately from 1 to 0 because the S/N will not satisfy the detection criterion for the entire orbit at once.
Paper: Photobombing for the Large Interferometer For Exoplanets (LIFE). A New Criterion for Target Confusion and Application to a Mid-infrared Rotating Nulling Interferometer
Authors: Drinor Cacaj, Daniel Angerhausen, Prabal Saxena, Romain Laugier, Jens Kammerer, Eleonora Alei, Sascha P. Quanz
Figure caption: Left: resolution ﹩{ \mathcal R }({p}_{1},{{\boldsymbol{F}}}_{{p}_{2}}={F}_{2}{\bf{1}},\sigma (\lambda )=\sigma )﹩ (Equation (26)) for point sources with homogeneous luminosity fluxes ﹩{{\boldsymbol{F}}}_{{p}_{i}}={F}_{{p}_{i}}{\bf{1}},\ i=1,2﹩. We give the spatial resolution calculated for different angular separation δr It corresponds to the length given by the square root of 1/π of the area of contamination evaluated at P = {p1, p2} where ﹩{\delta }_{r,{p}_{1}}={\delta }_{r}﹩ and σP = const. For example, at an angular separation delta_r = 0.6, and luminosity ratio l_1/l_2=0.5 it corresponds to the angular radius of a disk with the same area than the red area found in . ﹩{ \mathcal R }﹩ weakly depends on the luminosity flux ratio Fr = F1/F2. Right: comparison between LIFE’s δ1 resolution with JWST and the Extremely Large Telescope. In this case, bim = 6bnull.
Paper: Photobombing for the Large Interferometer For Exoplanets (LIFE). A New Criterion for Target Confusion and Application to a Mid-infrared Rotating Nulling Interferometer
Authors: Drinor Cacaj, Daniel Angerhausen, Prabal Saxena, Romain Laugier, Jens Kammerer, Eleonora Alei, Sascha P. Quanz
Figure caption: Top: normalized target cancellation map ﹩{ \mathcal C }﹩ (Equation (21)) for ﹩{p}_{1}=((0.6\cdot {\delta }_{\,\rm{null}\,},0),{{\boldsymbol{F}}}_{{p}_{1}})﹩ (black dot) with homogeneous luminosity flux ﹩{{\boldsymbol{F}}}_{{p}_{1}}={F}_{{p}_{1}}{\bf{1}}﹩ across the full wavelength range, λ from 4 μm to 18.5 μm, and a spectral resolution of 20. At each point where (δx, δy) is computed, the target cancellation map is evaluated at P = {p1, p2}, where ﹩{p}_{2}=(({\delta }_{x},{\delta }_{y}),{{\boldsymbol{F}}}_{{p}_{2}}={F}_{{p}_{1}}{\bf{1}})﹩ with constant noise independent from P, i.e., σ(λ) = σ. When p2 is outside the red region, cancellation does not occur. The red region is independent from ﹩{F}_{{p}_{1}}﹩ and σ. In this case, bim = 6bnull and δ0 ≈ 0.13 δnull. Middle: zoom in the right region of the top plot. Bottom: zoom in the left region of the top plot.
Paper: Photobombing for the Large Interferometer For Exoplanets (LIFE). A New Criterion for Target Confusion and Application to a Mid-infrared Rotating Nulling Interferometer
Authors: Drinor Cacaj, Daniel Angerhausen, Prabal Saxena, Romain Laugier, Jens Kammerer, Eleonora Alei, Sascha P. Quanz
Figure caption: Top: inner solar system seen from a near edge-on angle ϕ ≈ 0.9π/2 rad = 81°. At each point, p = (δx, δy) is the maximum of the target confusion map between Earth at p and another planet. The red line is ﹩{ \mathcal D }=0﹩. Earth is inside a region where ﹩{ \mathcal D }\gt 0﹩; i.e., Earth is photobombed. In this case, Earth is photobombed by Venus nonlocally. The white dot represents the planet detected in that case. Bottom: flux of Earth, Venus, and the photobombed Earth. Photobombing depends on the flux of both Earth’s and Venus’s flux. The photobombed Earth’s flux is the best point source that generates a signal similar to that of Earth and Venus together.