adsabs/AstroCLIMB huggingface dataset:
Paper: Photometric Completeness Modelled with Neural Networks
Authors: William E. Harris, Joshua S. Speagle, 佳士 沈
Figure caption: Example of our multistep training scheme for our NN model (see Section 6.3) over artificial-star data from NGC 1275. The black curve shows the average (log-)loss values over the training set over each epoch, while the red curve shows the learning rate associated with each epoch. The behavior for the burn-in phase, our three tuning phases, and the final settling phase are highlighted in shades of red, yellow, and blue, respectively, along with the associated amount of training data used in each phase.
Paper: Photometric Completeness Modelled with Neural Networks
Authors: William E. Harris, Joshua S. Speagle, 佳士 沈
Figure caption: Accuracy (black), completeness (red), precision (blue), and F-score (purple) for the NN models trained over NGC 1275 assuming a flat LF (top) and NGC 3377 (bottom), plotted as a function of the probability threshold used to decide whether an object is successfully recovered or not. The peak accuracy values are highlighted as an orange star. Both models are able to achieve >94% accuracy with stable performance over a wide range of values.
Paper: Photometric Completeness Modelled with Neural Networks
Authors: William E. Harris, Joshua S. Speagle, 佳士 沈
Figure caption: As Figure 16, but now for NGC 3377. We see the same trends but even stronger signs that the NN model is able to derive accurate estimates of the recovery fraction even in the presence of shot noise.
Paper: Photometric Completeness Modelled with Neural Networks
Authors: William E. Harris, Joshua S. Speagle, 佳士 沈
Figure caption: Measured CMDs for NGC 1275 (left) and NGC 3377 (right) with individual objects colored by the predicted recovery probability from our best-fit NN models. As in the previous graphs, note the nonlinear color scale.
Paper: Forced Measurement of Astronomical Sources at Low Signal-to-noise
Authors: A. Dutta, J. R. Peterson, G. Sembroski
Figure caption: In these plots, we show the performance of the traditional moment matching algorithm for 12,773 cases. We vary the size from 2 to 5 pixels and flux from 1 to 105 counts. Background varies from 50 to 2000 counts. Ellipticity values vary from 0 to 0.7 distributed between both e 1 and e 2. The upper left plot shows the failure percentage as a function of S/N. The upper right plot shows the percent deviation of the flux from the true value. The lower left and lower right plots show the percentage error in ﹩{\sigma }_{{xx}}^{2}﹩ and ﹩{\sigma }_{{xy}}^{2}﹩ as a function of S/N, respectively. It is clear that at S/N ≲ 10, catastrophic failures become more common, and the errors also become very large, as expected, sometimes even exceeding 100%. In the range 0.1 < S/N < 1, the failure rate is 89.5% ± 3.6%, and the percentage error in flux is 396% ± 474%. The error in determining ﹩{\sigma }_{{xx}}^{2}﹩ is −1.9% ± 48%, while the percentage error for ﹩{\sigma }_{{xy}}^{2}﹩ is −40% ± 211%.
Paper: Forced Measurement of Astronomical Sources at Low Signal-to-noise
Authors: A. Dutta, J. R. Peterson, G. Sembroski
Figure caption: The red points show the coadd measurements using the traditional moment matching algorithm, whereas blue points show the median of the measurements obtained from the individual images using forced measurement. The x-axis is the S/N of individual images. We also inject an error of 10% in initial guess flux, ﹩{\sigma }_{{xx}}^{2}﹩, ﹩{\sigma }_{{yy}}^{2}﹩, and ﹩{\sigma }_{{xy}}^{2}﹩. When simulating the individual sources, an error of 10% is injected in flux and σ, and an error of 1 pixel is introduced in the centroid. All guess parameters except the centroid have 10% overbias. This level is shown by the black line. In virtually all cases, forced measurement produces a better flux estimate. The accuracy at ﹩{\mathrm{log}}_{10}(﹩S/N) < 0.1 using the forced measurement method is 5% ± 4.3% for flux; ﹩{\sigma }_{{xx}}^{2}﹩ can be determined with an accuracy of 10.4% ± 1.3%, and ﹩{\sigma }_{{xy}}^{2}﹩ can be determined with an accuracy of 10.5% ± 4.8%. In the same S/N range, using traditional moment matching on the coadd to determine the same parameter gives an accuracy of 1.7% ± 9.9% for flux, while the error for ﹩{\sigma }_{{xx}}^{2}﹩ is 16.1% ± 26%. The percentage error for ﹩{\sigma }_{{xy}}^{2}﹩ is 18.6% ± 177%.
Paper: Forced Measurement of Astronomical Sources at Low Signal-to-noise
Authors: A. Dutta, J. R. Peterson, G. Sembroski
Figure caption: The red points show the coadd measurements, whereas blue points show the measurements obtained from the individual images. The x-axis is the S/N of individual images. We also inject an error of 15% in guess flux and σ 2 parameters. When simulating the individual sources, an error of 15% is injected in flux and σ, and an error of 1 pixel is introduced in the centroid. We see a slight bias of −2.9% ± 4.1% in flux measurement when using forced measurement when ﹩{\mathrm{log}}_{10}(\mathrm{S/N})\lt 0.1﹩. This can be attributed to the injected centroid errors. No bias is present in the forced measurement of ﹩{\sigma }_{{xx}}^{2}﹩ and ﹩{\sigma }_{{xy}}^{2}﹩. Once again, the tighter error properties of forced measurement are clear.
Paper: Forced Measurement of Astronomical Sources at Low Signal-to-noise
Authors: A. Dutta, J. R. Peterson, G. Sembroski
Figure caption: In these plots, we show the performance of the forced measurement algorithm for the same range of cases as Figure 7. Even at an extremely low S/N, unlike the traditional moment matching algorithm, we manage to avoid catastrophic failures as shown in the first graph. This is crucial for measuring ultrafaint sources. The error does increase significantly at low S/N. In the range 0.1 < S/N < 1, the failure rate is 0%, and the percentage error in flux is −1.9% ± 21%. In the same range, the error in ﹩{\sigma }_{{xx}}^{2}﹩ is 0% ± 0.8%, and the error in ﹩{\sigma }_{{xy}}^{2}﹩ is −0.2% ± 4.7%.
Paper: Forced Measurement of Astronomical Sources at Low Signal-to-noise
Authors: A. Dutta, J. R. Peterson, G. Sembroski
Figure caption: This plot shows the magnitude vs. size (in pixels) of all sources measured in the i-band coadd. The vertical strip corresponds to stars. Shown in the black box are all the sources for which individual frame measurements are performed, i.e., the test sample. The test sample includes sources with magnitude brightness in the range from 17th to 22.5th magnitude, while the size is limited from 2.8 to 3.4 pixels. These limits were determined visually, but as is clearly seen, almost all of these sources are likely to be stars.
Paper: Forced Measurement of Astronomical Sources at Low Signal-to-noise
Authors: A. Dutta, J. R. Peterson, G. Sembroski
Figure caption: In these plots, we show the performance of our modified moment matching algorithm run until convergence for the same range of cases as Figure 7. We find that convergence is consistent only when S/N ≳ 100. This shows that forced measurement does not perform well when applied iteratively, unless the source is at extremely high S/N ≳ 100.
Paper: Forced Measurement of Astronomical Sources at Low Signal-to-noise
Authors: A. Dutta, J. R. Peterson, G. Sembroski
Figure caption: The red points show the x-position of the centroid from the coadd using the traditional moment matching algorithm, whereas the blue points show the median of the individual measurements obtained using forced measurement. The simulations are done under the same conditions as Figure 11.
Paper: Forced Measurement of Astronomical Sources at Low Signal-to-noise
Authors: A. Dutta, J. R. Peterson, G. Sembroski
Figure caption: (a) The normalized S/N histogram for all 12,773 sources is shown. These sources are used to gauge the performance of the traditional moment matching algorithm, the forced measurement algorithm run until convergence, and the forced measurement algorithm. This graph has been truncated at S/N = 25. Similarly, (b) and (c) show the normalized histograms of the area and ellipticity of the sources, respectively.
Paper: Forced Measurement of Astronomical Sources at Low Signal-to-noise
Authors: A. Dutta, J. R. Peterson, G. Sembroski
Figure caption: The red points show the coadd measurements, whereas blue points show the measurements obtained from the individual images. The x-axis is the S/N of individual images. When using forced measurement, we do not find any bias. The graphs clearly show the better error properties obtained when using this method.
Paper: The Solar System Notification Alert Processing System (SNAPS): Asteroid Population Outlier Detection
Authors: Michael Gowanlock, David E. Trilling, Daniel Kramer, Maria Chernyavskaya, Andrew McNeill
Figure caption: Comparison of the four outlier detection methods on an example d = 2 feature space, showing the rotation period as a function of g − r color. (a)–(b) show the two metrics for DSSJ and (c)–(d) show the two metrics for k NNSJ. The points that fall within the top 1% of the ranked list of outliers are shown as solid red points in each plot. The inliers (99%) are shown as the translucent blue points.
Paper: The Solar System Notification Alert Processing System (SNAPS): Asteroid Population Outlier Detection
Authors: Michael Gowanlock, David E. Trilling, Daniel Kramer, Maria Chernyavskaya, Andrew McNeill
Figure caption: Histograms of the distribution of metric values for the corresponding plots in Figure 11. The red shaded regions are illustrative examples of where outliers are found in the respective distributions. The mean distance metric for DSSJ (b) yields two regions where outliers are found. Points with no neighbors within their search radius will yield a mean distance of zero and so all points that do not have any neighbors are assigned a rank of 0. And those points that have large mean distances to their neighbors will also be outliers.
Paper: The Solar System Notification Alert Processing System (SNAPS): Asteroid Population Outlier Detection
Authors: Michael Gowanlock, David E. Trilling, Daniel Kramer, Maria Chernyavskaya, Andrew McNeill
Figure caption: Outliers detected using the k NNSJ mean distance to k neighbors metric rankings in the light-curve amplitude, rotation period, and HAVG feature space. The inliers (99% of the objects) are plotted as translucent circles and the outliers with “x” markers (1% of the objects). Red and blue markers denote C-type (g − r color ≤ 0.563) and S-type (g − r color > 0.563) asteroids, respectively. Two-dimensional slices through this space are shown in Figure 15.
Paper: The Solar System Notification Alert Processing System (SNAPS): Asteroid Population Outlier Detection
Authors: Michael Gowanlock, David E. Trilling, Daniel Kramer, Maria Chernyavskaya, Andrew McNeill
Figure caption: The same as Figure 7, but for the mean distance to k neighbors metric. The plot shows 98.5% of the k select values across all feature spaces.
Paper: The Solar System Notification Alert Processing System (SNAPS): Asteroid Population Outlier Detection
Authors: Michael Gowanlock, David E. Trilling, Daniel Kramer, Maria Chernyavskaya, Andrew McNeill
Figure caption: The same as Figure 11, but for a d = 3 feature space (light-curve amplitude, rotation period, and g − r color).
Paper: The Solar System Notification Alert Processing System (SNAPS): Asteroid Population Outlier Detection
Authors: Michael Gowanlock, David E. Trilling, Daniel Kramer, Maria Chernyavskaya, Andrew McNeill
Figure caption: Performance results for k NNSJ showing the total time to find k select for both outlier detection metrics, and compute the rankings for all ∣D∣ = 32,752 feature spaces. (a) The response time (hr) to compute k select and the corresponding rankings for both k NNSJ metrics as a function of the oversubscription (o) factor with four GPUs. (b) The response time as a function of the number of GPUs, demonstrating the scalability when o = 5.
Paper: The Solar System Notification Alert Processing System (SNAPS): Asteroid Population Outlier Detection
Authors: Michael Gowanlock, David E. Trilling, Daniel Kramer, Maria Chernyavskaya, Andrew McNeill
Figure caption: The distribution of k select values for the in-degree metric across all 32,752 feature spaces (Section 3.3). To improve readability, the vertical axis is truncated and does not show all k select values, which are mostly zeros. The plot shows 99.5% of the k select values across all feature spaces.
Paper: The Solar System Notification Alert Processing System (SNAPS): Asteroid Population Outlier Detection
Authors: Michael Gowanlock, David E. Trilling, Daniel Kramer, Maria Chernyavskaya, Andrew McNeill
Figure caption: The response time (hr) to compute ϵ select and the corresponding rankings for both DSSJ metrics as a function of the oversubscription (o) factor using one GPU. This includes the total time to find ϵ select for both outlier detection metrics, and compute the rankings for all ∣D∣ = 32,752 feature spaces.
Paper: The Solar System Notification Alert Processing System (SNAPS): Asteroid Population Outlier Detection
Authors: Michael Gowanlock, David E. Trilling, Daniel Kramer, Maria Chernyavskaya, Andrew McNeill
Figure caption: The same as Figure 5, but for the mean distance metric.
Paper: The Solar System Notification Alert Processing System (SNAPS): Asteroid Population Outlier Detection
Authors: Michael Gowanlock, David E. Trilling, Daniel Kramer, Maria Chernyavskaya, Andrew McNeill
Figure caption: Scatterplots showing a comparison of rankings for the same d = 3 feature space shown in Figure 13. (a) Ranking comparison for the two DSSJ metrics. (b) Ranking comparison for the two k NNSJ metrics. (c) Comparison of the two distance-oblivious metrics. (d) Comparison of the two distance-aware metrics. Note that across all figures, the distance-oblivious metrics (DSSJ: number of neighbors and k NNSJ: in-degree) have numerous points that share the same rank, and so they have a smaller range of possible rank values compared to the distance-aware metrics (DSSJ: mean distance and k NNSJ: mean distance to k neighbors).
Paper: The Solar System Notification Alert Processing System (SNAPS): Asteroid Population Outlier Detection
Authors: Michael Gowanlock, David E. Trilling, Daniel Kramer, Maria Chernyavskaya, Andrew McNeill
Figure caption: Outliers detected using the DSSJ number of neighbors metric in the light-curve amplitude, rotation period, and HAVG feature space. The inliers (99% of the objects) are plotted as translucent circles and the outliers are plotted with “x” markers (1% of the objects). We highlight with cyan diamond markers objects with a light-curve amplitude ≥ 1 mag and rotation period of ≥500 hr. There are only 14 objects with these properties and our method is able to detect all of these as outliers denoted by the 14 diamond markers imposed behind the “x” markers.
Paper: The Solar System Notification Alert Processing System (SNAPS): Asteroid Population Outlier Detection
Authors: Michael Gowanlock, David E. Trilling, Daniel Kramer, Maria Chernyavskaya, Andrew McNeill
Figure caption: The same as Figure 14, except that only the outliers are shown. The panels ordered from top to bottom are as follows: rotation period as a function of amplitude, HAVG as a function of amplitude, and HAVG as a function of rotation period. Outliers in this space are equally divided between C-type and S-type asteroids, indicating that composition is not a significant driver of unusual behaviors in these three dimensions.
Paper: Estimating Microlensing Parameters from Observables and Stellar Isochrones with pyLIMASS
Authors: E. Bachelet, M. Hundertmark, S. Calchi Novati
Figure caption: (Left) Reconstruced angular source radius of the source of MOA-2013-220Lb from the (V, I) S  measurements of Vandorou et al. (2020) and the stellar isochrones of Bressan et al. (2012), assuming the source at D  S  = 8.19 ± 0.76 kpc. This is in good agreement with the estimation of V20 (yellow) as well as the measurement from Equation (13). (Right) The estimated magnitude of the source in the K  s  band from the isochrones is in tension with the measurement of the source (yellow) magnitude reported by V20. The lens magnitude is also reported in red.
Paper: Estimating Microlensing Parameters from Observables and Stellar Isochrones with pyLIMASS
Authors: E. Bachelet, M. Hundertmark, S. Calchi Novati
Figure caption: Distribution of the lens mass and the lens distance for the microlensing event MOA-2013-BLG-220Lb (Yee et al. 2014; Vandorou et al. 2020). (Left) The pyLIMASS distribution with the lens flux in K  s  bands and the measurements from V20 in red. (Right) The same distribution, but assuming the extra constraint on the lens flux I  L  = 21.10 ± 0.1 mag.
Paper: Estimating Microlensing Parameters from Observables and Stellar Isochrones with pyLIMASS
Authors: E. Bachelet, M. Hundertmark, S. Calchi Novati
Figure caption: Distribution of the lens mass and the lens–source distance for the microlensing event OGLE-2017-1254L (Zang et al. 2020). (Left) The pyLIMASS distribution with weak constraints on the source distance (i.e., 2 < D  S  < 15 kpc) and the measurements from Zang et al. (2020) in red. (Right) The same distribution but assuming the source distance D  S  = 7.8 ± 0.8 kpc as an observable.
Paper: Estimating Microlensing Parameters from Observables and Stellar Isochrones with pyLIMASS
Authors: E. Bachelet, M. Hundertmark, S. Calchi Novati
Figure caption: pyLIMASS posterior distribution of the angular source radius of the source (left), Einstein angular radii (middle), and source distance (right) for the event Gaia16aye (Wyrzykowski et al. 2020). The 1σ  measurements of W20 are represented in red.
Paper: Estimating Microlensing Parameters from Observables and Stellar Isochrones with pyLIMASS
Authors: E. Bachelet, M. Hundertmark, S. Calchi Novati
Figure caption: Distribution of the parallax components for the two models of OGLE-2017-1254L described in Zang et al. (2020; blue stars) and samples generated by the GM model (orange dots).
Paper: Estimating Microlensing Parameters from Observables and Stellar Isochrones with pyLIMASS
Authors: E. Bachelet, M. Hundertmark, S. Calchi Novati
Figure caption: Distribution of the lens mass and the lens distance for the microlensing event OGLE-2019-0960Lb (Yee et al. 2021).(Left) The pyLIMASS distribution with a strong constraint on the lens flux and the measurements from Yee et al. (2021) in red. (Right) The same distribution, but assuming a lower limit on the lens brightness.
Paper: Estimating Microlensing Parameters from Observables and Stellar Isochrones with pyLIMASS
Authors: E. Bachelet, M. Hundertmark, S. Calchi Novati
Figure caption: Distribution of the lens mass and the lens distance for the microlensing event Gaia16aye (Wyrzykowski et al. 2020). The measurements of W20 are represented in red.
Paper: Helium in Exoplanet Exospheres: Orbital and Stellar Influences
Authors: Vigneshwaran Krishnamurthy, Nicolas B. Cowan
Figure caption:  F XUV/F Ly α  vs. F XUV for all planets sourced from the NASA Exoplanet Archive. The blue triangles and red circles represent planets with detected and undetected helium, respectively, with their size scaled by their TSM. The purple histogram shows the helium-observed samples, and the blue filled histogram for detected planets illustrates that planets with detected helium tend to prefer lower F XUV/F Ly α , contrary to predictions. Here we used Lyα as a proxy for mid-UV flux.
Paper: Helium in Exoplanet Exospheres: Orbital and Stellar Influences
Authors: Vigneshwaran Krishnamurthy, Nicolas B. Cowan
Figure caption: Semimajor axis vs. stellar effective temperature. The red circles and blue triangles are helium-observed planets, scaled as in Figure 3. The “detection sweet spot,” bounded by black boxes, encompasses the helium-detected planets. The first (left) box contains K-star planets that have ideal spectra to excite helium, while the second box includes only hot Jupiters around G stars.
Paper: Helium in Exoplanet Exospheres: Orbital and Stellar Influences
Authors: Vigneshwaran Krishnamurthy, Nicolas B. Cowan
Figure caption: Planet density vs. F XUV. The red circles and blue triangles are helium-observed planets, scaled as in Figure 3. The accompanying histograms illustrate that planets with detected helium (blue filled) tend to have lower density. Well-known planets are highlighted.
Paper: Helium in Exoplanet Exospheres: Orbital and Stellar Influences
Authors: Vigneshwaran Krishnamurthy, Nicolas B. Cowan
Figure caption: Cumulative XUV (X-ray + EUV) fluxes for all planets from the NASA Exoplanet Archive plotted against the cumulative Lyα flux. We have used Lyα flux as a proxy for mid-UV flux. The blue-outlined triangles and red-outlined circles denote the helium-detected and nondetected planets, respectively. All points are scaled by their TSM and colored by their age. It is noticeable that the helium-detected planets are concentrated within a narrow range of cumulative XUV flux of 1.75 × 1020 –1.75 × 1021 erg cm−2.
Paper: Helium in Exoplanet Exospheres: Orbital and Stellar Influences
Authors: Vigneshwaran Krishnamurthy, Nicolas B. Cowan
Figure caption: Radius–mass plot for all planets from the NASA Exoplanet Archive (gray dots) superimposed with helium-observed planets. Red-outlined circles are planets probed for helium but on which helium was not detected, while blue-outlined triangles are planets with detected helium. The size of the helium (non)detections has been scaled by their TSM (Kempton et al. 2018) and colored by their orbital separation. Radius for different classes of planets are highlighted on the left. Error bars are removed for clarity.
Paper: Helium in Exoplanet Exospheres: Orbital and Stellar Influences
Authors: Vigneshwaran Krishnamurthy, Nicolas B. Cowan
Figure caption: Stellar metallicity dependence on helium detection. The red circles and blue triangles are helium-observed planets, scaled as in Figure 3. No preference in metallicity is observed.
Paper: Photo-dynamical Analysis of Circumbinary Multi-planet System TOI-1338: A Fully Coplanar Configuration with a Puffy Planet
Authors: Mu-Tian Wang, 牧天 王, Hui-Gen Liu, 慧根 刘
Figure caption: The evolution of orbital plane and the change of impact parameter of TOI-1338 b’s primary and secondary transits. Top: the evolution of impact parameters of primary (red) and secondary transit (blue) integrated from the best-fit model. It shows that TOI-1338 b will permanently transit the primary star but hardly transit the secondary star. Bottom: the sky-projection view of the orbits of the binary and TOI-1338 b. The orbit of TOI-1338 b is integrated forward by 7000 days to show the effect of nodal precession due to interactions with binary stars. The size of the point scales with the line-of-sight distance; thus, smaller points are behind the binary orbits. The red and blue regions show the crossing area of stellar disks of primary and secondary stars, respectively, accounting for the binary orbital motions. Transits can occur when the planet orbit and the stellar crossing area are intersected. The aspect ratio is 70:1. Due to the small mutual inclination of TOI-1338 b, the variation range of planetary inclination is very small; thus, its orbit will always intersect the primary star’s orbital crossing region. The secondary star has a smaller radius; thus, the stellar and planetary orbits are not mutually intersected, precluding potential transitability.
Paper: Photo-dynamical Analysis of Circumbinary Multi-planet System TOI-1338: A Fully Coplanar Configuration with a Puffy Planet
Authors: Mu-Tian Wang, 牧天 王, Hui-Gen Liu, 慧根 刘
Figure caption: The masses and radii of low-mass sub-Jovian planets (4 R ⊕ < R  p  < 9 R ⊕, 2 M ⊕ < M  p  < 40 M ⊕) on the NASA Exoplanet Archive as of 2024 April 7 with masses and radius measured better than 30%, color-coded by the insolation fluxes. TOI-1338 b is highlighted in magenta. We also plot the transiting CBPs in the sub-Saturn regime with larger white filled circles. Dashed lines show the expected mass and radius for sub-Saturn planets at 4 Gyr old and 10 Earth insolation S ⊕ from the tide-free, thermal evolution models of Millholland et al. (2020), for H/He envelope mass fractions of 30% (blue), 45% (green), and 48% (orange).