adsabs/AstroCLIMB huggingface dataset:
Paper: Apache Point Observatory Follow-up of ACcelerating Candidate ExopLanet Host Stars (APO ACCELS): Ages for 166 Accelerating Stars in the Northern Hemisphere
Authors: Anne E. Peck, Eric L. Nielsen, Robert J. De Rosa, William Thompson, Bruce Macintosh, William Roberson, Adam J. R. W. Smith, Jessica Klusmeyer, Asif Abbas, Jason Jackiewicz, Jon Holtzman, Hannah Gallamore, Marah Brinjikji, Jennifer Patience, Jayke S. Nguyen, Alex Madurowicz, Dmitry Savransky
Figure caption: Left: ages derived from lithium alone yield an age distribution consistent with a constant star formation rate. Eight percent of the combined distribution falls below 1 Gyr, which is consistent with the expected 7.7%. Right: ages derived from gyrochronology alone yield a significantly younger age distribution than expected. For the 20 late-type stars with rotation periods ≲15 days, 92% of the combined distribution of ages falls below 1 Gyr, which corresponds to ∼17% of the full gyrochronology sample.
Paper: Apache Point Observatory Follow-up of ACcelerating Candidate ExopLanet Host Stars (APO ACCELS): Ages for 166 Accelerating Stars in the Northern Hemisphere
Authors: Anne E. Peck, Eric L. Nielsen, Robert J. De Rosa, William Thompson, Bruce Macintosh, William Roberson, Adam J. R. W. Smith, Jessica Klusmeyer, Asif Abbas, Jason Jackiewicz, Jon Holtzman, Hannah Gallamore, Marah Brinjikji, Jennifer Patience, Jayke S. Nguyen, Alex Madurowicz, Dmitry Savransky
Figure caption: We calculate age posteriors from rotation periods using gyro-interp (L. G. Bouma et al. 2023). The age posterior for HIP 41277 with an adopted Teff of 4551  ±  195 K and a measured rotation period of 3.70  ±  0.24 days has a median age of 96 Myr and a 68% confidence interval between 30 and 316 Myr.
Paper: Apache Point Observatory Follow-up of ACcelerating Candidate ExopLanet Host Stars (APO ACCELS): Ages for 166 Accelerating Stars in the Northern Hemisphere
Authors: Anne E. Peck, Eric L. Nielsen, Robert J. De Rosa, William Thompson, Bruce Macintosh, William Roberson, Adam J. R. W. Smith, Jessica Klusmeyer, Asif Abbas, Jason Jackiewicz, Jon Holtzman, Hannah Gallamore, Marah Brinjikji, Jennifer Patience, Jayke S. Nguyen, Alex Madurowicz, Dmitry Savransky
Figure caption: Left: the combined age posterior from lithium detections and/or gyrochronology for the late-type stars in the sample. The peak at young ages is consistent with stars that are <1 Gyr being most likely to have detectable lithium or clear rotation signals. Right: for stars that only have lithium upper limits, the age posterior is mostly flat, following the prior, with only the youngest ages excluded.
Paper: Apache Point Observatory Follow-up of ACcelerating Candidate ExopLanet Host Stars (APO ACCELS): Ages for 166 Accelerating Stars in the Northern Hemisphere
Authors: Anne E. Peck, Eric L. Nielsen, Robert J. De Rosa, William Thompson, Bruce Macintosh, William Roberson, Adam J. R. W. Smith, Jessica Klusmeyer, Asif Abbas, Jason Jackiewicz, Jon Holtzman, Hannah Gallamore, Marah Brinjikji, Jennifer Patience, Jayke S. Nguyen, Alex Madurowicz, Dmitry Savransky
Figure caption: The medians and 68% confidence intervals for our derived age posteriors for the full sample of 166 stars, as a function of B − V. The symbol and color of each star represent which age tracer (or tracers) was used to derive the age posterior. The error bars represent the 68% confidence intervals.
Paper: Apache Point Observatory Follow-up of ACcelerating Candidate ExopLanet Host Stars (APO ACCELS): Ages for 166 Accelerating Stars in the Northern Hemisphere
Authors: Anne E. Peck, Eric L. Nielsen, Robert J. De Rosa, William Thompson, Bruce Macintosh, William Roberson, Adam J. R. W. Smith, Jessica Klusmeyer, Asif Abbas, Jason Jackiewicz, Jon Holtzman, Hannah Gallamore, Marah Brinjikji, Jennifer Patience, Jayke S. Nguyen, Alex Madurowicz, Dmitry Savransky
Figure caption: We fit a two-component sinusoid model (red) to a light curve (black) for HIP 117410. We find the periods of the two components to be 10.65  ±  1.07 days and 0.69  ±  0.07 days.
Paper: Microlensing Constraints on the Stellar and Planetary Mass Functions
Authors: Jennifer C. Yee, Scott J. Kenyon
Figure caption: Schematic diagram of the planetary (blue, dotted line) and stellar (magenta dashed line) MFs. The vertical gray lines are drawn at 1 MSat and 0.08 MSun (the hydrogen-burning limit). Black horizontal lines indicate the approximate mass ranges of “Stars,” “Brown Dwarfs,” and “Planets.” The lines are straight for masses that clearly belong to that population; when they extend to the edge of the plot, arrows indicate that the range extends beyond that point. The lines are wavy and end with an arrow in regions where the extreme limit is unknown or ambiguous. The upper and lower limits of the planetary and stellar MFs, respectively, and the location of the minimum between them are particularly uncertain, leading to ambiguity in the nature of brown dwarfs, both individually and as a population.
Paper: Microlensing Constraints on the Stellar and Planetary Mass Functions
Authors: Jennifer C. Yee, Scott J. Kenyon
Figure caption: Frequencies for planets/objects with a ≥ 10 au in various bins (bin edges are shown as vertical black dotted lines). The microlensing frequencies from T. Sumi et al. (2023) are shown as brown lines: solid for μFFPs alone and dotted for the sum of μFFPs and stellar brown dwarfs. These are compared to the frequencies measured for direct imaging (circles) and radial velocities (gray squares; B. J. Fulton et al. 2021); measurements from E. L. Nielsen et al. (2019) are shown in orange, and those from A. Vigan et al. (2021) are shown in teal and light blue. The legend indicates the host mass range for each frequency measurement; see text for B. J. Fulton et al. (2021). There are more direct imaging and radial velocity companions than μFFPs, suggesting that some fraction of the imaging and radial velocity companions come from the tail of the stellar MF.
Paper: Microlensing Constraints on the Stellar and Planetary Mass Functions
Authors: Jennifer C. Yee, Scott J. Kenyon
Figure caption: MFs from A. Gould et al. (2022; blue dotted line) and G. Chabrier (2005; blue dashed line), T. Sumi et al. (2023; orange) and J. D. Kirkpatrick et al. (2024; orange). The dotted lines show the μFFP component while the dashed lines show the stellar component. The solid line shows their sum. The orange dashed–dotted line indicates that the J. D. Kirkpatrick et al. (2024) MF is extrapolated beyond the available data below 10−2 M⊙. The black, vertical, dashed line and dotted line are drawn at 3 MJup and 15 MJup, respectively. Although this regime is often considered to be “planetary” (as defined by mass), in these mass functions, the “stellar” component is clearly dominant.
Paper: Microlensing Constraints on the Stellar and Planetary Mass Functions
Authors: Jennifer C. Yee, Scott J. Kenyon
Figure caption: Frequencies for planets with a ≥ 10 au in various bins; dotted vertical and brown horizontal lines as in Figure 4. The distribution of planets inferred from debris disks, assuming a single planet sculpts the disk (T. D. Pearce et al. 2022), is shown as light blue squares. The distribution for planets assuming every ALMA gap contains a planet are shown as circles with different colors for results from S. Zhang et al. (2018) for α  =  10−3, G. Lodato et al. (2019) for the Combined sample, S. Wang et al. (2021) for α  =  10−3 and the dust assumption, S. Zhang et al. (2023) for ﹩{\alpha }_{{\rm{\max }}}\,=\,1\,{\rm{mm}}﹩ (see legend). For masses mp  >  1 MSat, there is significant tension between the population of planets inferred from disks under these assumptions and the limits from the μFFP population.
Paper: Microlensing Constraints on the Stellar and Planetary Mass Functions
Authors: Jennifer C. Yee, Scott J. Kenyon
Figure caption: Cumulative distributions of minimum planet masses from T. D. Pearce et al. (2022) assuming the inner disk edge is sculpted by a single planet. The distribution for the full sample is shown in black, while the subsamples for different spectral types are shown in color (red = M, blue = K, green = G, blue = F, purple = A/B). The top and middle panels show the normalized cumulative distributions. Power laws with various indices (﹩dN/d\,{\mathrm{log}}\,{M}_{p}\propto {M}_{p}^{-p}﹩) are shown in the top panel, normalized to fit the observed distribution at the points indicated by the dotted lines. The bottom panels show the inferred debris-disk planet distributions shifted by the median mass for each group and the difference of the shifted distributions relative to the full sample.
Paper: Microlensing Constraints on the Stellar and Planetary Mass Functions
Authors: Jennifer C. Yee, Scott J. Kenyon
Figure caption: Comparison of host mass (﹩{\mathrm{log}}\,M﹩) and the duration of observations Δt as a fraction of the period P at a  =  10 au for the radial velocity sample from L. J. Rosenthal et al. (2021) and B. J. Fulton et al. (2021). Blue points are for all stars. Magenta points indicate stars with planetary-mass companions at a ≥ 10 au. The horizontal dashed line at y  =  0.6 is roughly the minimum for detecting a ≥ 10 au companions. The vertical dashed lines are drawn at ﹩{\mathrm{log}}\,M\,=\,-0.2,0.2﹩.
Paper: Microlensing Constraints on the Stellar and Planetary Mass Functions
Authors: Jennifer C. Yee, Scott J. Kenyon
Figure caption: Example of the microlensing light curve of a μFFP based on the properties of OGLE-2016-BLG-1540 (P. Mróz et al. 2018). The Einstein timescale of the event is tE  =  0.320 days. The blue curve shows the light curve under a point-source approximation. The orange curve includes the effect of the physical extent of the source star (FSPL), which enables a measurement of the Einstein radius.
Paper: Microlensing Constraints on the Stellar and Planetary Mass Functions
Authors: Jennifer C. Yee, Scott J. Kenyon
Figure caption: Magenta points compare the minimum masses calculated by T. D. Pearce et al. (2022) for a single planet to shape the disk (mp, x-axis) vs. multiple, equal-mass planets (mp,n, y-axis). The gray solid line is one-to-one. Black lines show masses of solar system planets (see legend).
Paper: Down But Not Out: The Case of Long-period Comet C/2021 O3 (Panstarrs)
Authors: David Jewitt, Jing Li, Michael Jaeger, Yoonyoung Kim
Figure caption: Photometry within 10″ apertures extracted from images in the Cometas archive (filled red circles) and from CFHT (yellow square), Calar Alto (X), and Jaeger Observatory (green diamond). The solid curve shows Equation (4) with H = 13.00 ± 0.26 and n = 2.59 ± 0.21 (solid black line). The dashed curve shows the same model but displaced by +3.5 mag to better match the postperihelion data. The date of perihelion is marked P.
Paper: Down But Not Out: The Case of Long-period Comet C/2021 O3 (Panstarrs)
Authors: David Jewitt, Jing Li, Michael Jaeger, Yoonyoung Kim
Figure caption: Position angle expected from syndyne (solid red line) and synchrone (dashed blue line) models as a function of particle release date (measured in days prior to April 29 (DOY 484)) and β, respectively. The horizontal black line and yellow band mark the measured position angle and ±1σ uncertainty. The derived estimates of release time and β are marked with blue and red vertical lines, respectively.
Paper: Down But Not Out: The Case of Long-period Comet C/2021 O3 (Panstarrs)
Authors: David Jewitt, Jing Li, Michael Jaeger, Yoonyoung Kim
Figure caption: (Left:) Synchrones for ejection 10, 20, 40, 80, and 160 days prior to the April 29 (DOY 484) observation date and (right:) syndynes for particles with β = 1, 0.3, 0.1, 0.03, and 0.01 (i.e., nominal particle radii 1, 3, 10, 30, and 100 μm, respectively). Both panels show a region 4° × 4° in size.
Paper: An Advanced Deep Learning Model for Identifying Blue Horizontal-branch Stars from LAMOST DR10
Authors: Yuhang Zhang, Yude Bu, Jiangchuan Zhang, Ke Wang, Huili Wu, Mengmeng Zhang, Shanshan Li, Jingzhen Sun, Xiaoming Kong, Zhenping Yi, Meng Liu
Figure caption: A BHB candidate spectrum identified through a large-scale search and its two-stage predicted probability distributions. The top left panel displays the normalized spectrum. The top right panel provides the LAMOST-LRS DR10 unique spectrum identifier (ObsID), LAMOST DR10 unique source identifier (UID), LAMOST DR10 g-band S/N, R.A., decl., and the subclass label from the LAMOST pipeline. The contour plot in the bottom right panel presents the two-dimensional distribution of P(class = BHB), P(class = Hsd), P(class = A), P(class = B), P(class = Galaxy), and P(class = QSO) from the first-stage prediction. The contour plot in the bottom left panel illustrates the two-dimensional distribution of P(class = BHB) and P(class = Hsd/A/B) from the second-stage prediction. Notably, the two-dimensional distribution of P(class = BHB) vs. P(class = Hsd/A/B) exhibits a significantly negative correlation, indicating that these two classes are more easily confused with each other. The purple histograms represent the probability distribution based on 50 samples, the orange lines indicate the mean predicted probability for each class, and the gray shaded regions cover the 2nd to 98th percentiles (light gray) and the 16th to 84th percentiles (dark gray).
Paper: An Advanced Deep Learning Model for Identifying Blue Horizontal-branch Stars from LAMOST DR10
Authors: Yuhang Zhang, Yude Bu, Jiangchuan Zhang, Ke Wang, Huili Wu, Mengmeng Zhang, Shanshan Li, Jingzhen Sun, Xiaoming Kong, Zhenping Yi, Meng Liu
Figure caption: Distributions of apparent magnitude, distance, R, and Z for the BHB candidates identified in this study. The KDE curves in the top left panel compare the G-band apparent magnitude distribution of our BHB candidates with that of the BHB stars from X.-X. Xue et al. (2011) and J. J. Vickers et al. (2021). The three remaining panels present the comparative distributions of distance (bottom left), R (top right), and Z (bottom right).
Paper: An Advanced Deep Learning Model for Identifying Blue Horizontal-branch Stars from LAMOST DR10
Authors: Yuhang Zhang, Yude Bu, Jiangchuan Zhang, Ke Wang, Huili Wu, Mengmeng Zhang, Shanshan Li, Jingzhen Sun, Xiaoming Kong, Zhenping Yi, Meng Liu
Figure caption: Wavelength selection results for a normalized candidate spectrum based on mutual information. The top panel shows the wavelength selection for Teff. The middle panel shows the wavelength selection for ﹩{\mathrm{log}}\,g﹩. The bottom panel shows the wavelength selection for [Fe/H]. The purple solid curve represents the entire original normalized spectrum. The horizontal coordinates of the orange circles denote the selected effective wavelengths, while the vertical coordinates correspond to the normalized fluxes at these wavelengths. The black dashed lines represent four hydrogen Balmer lines, specifically Hδ, Hγ, Hβ, and Hα.
Paper: An Advanced Deep Learning Model for Identifying Blue Horizontal-branch Stars from LAMOST DR10
Authors: Yuhang Zhang, Yude Bu, Jiangchuan Zhang, Ke Wang, Huili Wu, Mengmeng Zhang, Shanshan Li, Jingzhen Sun, Xiaoming Kong, Zhenping Yi, Meng Liu
Figure caption: Normalized spectra near the Hδ line (left) and Hγ line (right) for a BHB candidate (﹩{\rm{obsid}}=459109162,{T}_{{\rm{eff}}}\approx 9640\,{\rm{K}},\,{\mathrm{log}}\,g\approx 2.90\,{\rm{dex}}﹩). The purple solid curve represents the normalized spectrum, while the orange dashed curve denotes the fitting profile. The values of D0.2 and fm are also shown.
Paper: An Advanced Deep Learning Model for Identifying Blue Horizontal-branch Stars from LAMOST DR10
Authors: Yuhang Zhang, Yude Bu, Jiangchuan Zhang, Ke Wang, Huili Wu, Mengmeng Zhang, Shanshan Li, Jingzhen Sun, Xiaoming Kong, Zhenping Yi, Meng Liu
Figure caption: Left panel: positions of the BHB candidates identified in this study in the Gaia HRD. Our BHB candidates, the BHB stars from X.-X. Xue et al. (2011), and the objects from Gaia DR3 are represented by orange plus signs, red open triangles, and filled squares, respectively. The green trapezium indicates the BHB candidate region in the Gaia HRD defined by R. Culpan et al. (2021). Right panel: zoom-in of the BHB candidate region.
Paper: An Advanced Deep Learning Model for Identifying Blue Horizontal-branch Stars from LAMOST DR10
Authors: Yuhang Zhang, Yude Bu, Jiangchuan Zhang, Ke Wang, Huili Wu, Mengmeng Zhang, Shanshan Li, Jingzhen Sun, Xiaoming Kong, Zhenping Yi, Meng Liu
Figure caption: Comparison results between the atmospheric parameters estimated by LightGBM and the labels provided by M. Xiang et al. (2022).
Paper: An Advanced Deep Learning Model for Identifying Blue Horizontal-branch Stars from LAMOST DR10
Authors: Yuhang Zhang, Yude Bu, Jiangchuan Zhang, Ke Wang, Huili Wu, Mengmeng Zhang, Shanshan Li, Jingzhen Sun, Xiaoming Kong, Zhenping Yi, Meng Liu
Figure caption: Distribution of the 6253 candidates colored by [Fe/H] in the Teff−﹩{\mathrm{log}}\,g﹩ plane.
Paper: An Advanced Deep Learning Model for Identifying Blue Horizontal-branch Stars from LAMOST DR10
Authors: Yuhang Zhang, Yude Bu, Jiangchuan Zhang, Ke Wang, Huili Wu, Mengmeng Zhang, Shanshan Li, Jingzhen Sun, Xiaoming Kong, Zhenping Yi, Meng Liu
Figure caption: Distributions of the BHB candidates in the D0.2,δ vs. fm,δ plane (left) and D0.2,γ vs. fm,γ plane (right). The orange box represents the criteria from J. Ju et al. (2023) to select BHB stars.
Paper: An Advanced Deep Learning Model for Identifying Blue Horizontal-branch Stars from LAMOST DR10
Authors: Yuhang Zhang, Yude Bu, Jiangchuan Zhang, Ke Wang, Huili Wu, Mengmeng Zhang, Shanshan Li, Jingzhen Sun, Xiaoming Kong, Zhenping Yi, Meng Liu
Figure caption: Sky distribution of the BHB candidates identified in this study. Our BHB candidates, the BHB stars from X.-X. Xue et al. (2011), and those from and J. J. Vickers et al. (2021) are represented by purple circles, orange circles, and green circles, respectively.
Paper: Heartbeat Stars Recognition Based on Recurrent Neural Networks: Method and Validation
Authors: Min-Yu Li, Sheng-Bang Qian, Li-Ying Zhu, Wen-Ping Liao, Lin-Feng Chang, Er-Gang Zhao, Xiang-Dong Shi, Fu-Xing Li, Qi-Bin Sun, Ping Li
Figure caption: The first and second rows show the test results of the GRU and LSTM networks, respectively, on real data sets. The column headings denote the names of the four test data sets. In each panel, the x-axis represents the eccentricity from the reference papers; the y-axis represents the eccentricity predicted by the RNN; the two gray dashed lines indicate deviations of ±0.15. The red circles represent the HBSs with TEOs.
Paper: Heartbeat Stars Recognition Based on Recurrent Neural Networks: Method and Validation
Authors: Min-Yu Li, Sheng-Bang Qian, Li-Ying Zhu, Wen-Ping Liao, Lin-Feng Chang, Er-Gang Zhao, Xiang-Dong Shi, Fu-Xing Li, Qi-Bin Sun, Ping Li
Figure caption: Four new HBSs. The orbital period (Pclc) is derived using the approach in Section 2.4. The eccentricity (enn) is predicted by our RNNs. For KIC 6794131, the solid red line shows the K95+ model fitted to the light curves, and the residuals are also shown. The eccentricity (efit) is obtained according to the K95+ model. Note: The plots show phases 0−2 to make the heartbeat signal clear, and phases 1−2 are an exact copy of phases 0−1.
Paper: Heartbeat Stars Recognition Based on Recurrent Neural Networks: Method and Validation
Authors: Min-Yu Li, Sheng-Bang Qian, Li-Ying Zhu, Wen-Ping Liao, Lin-Feng Chang, Er-Gang Zhao, Xiang-Dong Shi, Fu-Xing Li, Qi-Bin Sun, Ping Li
Figure caption: Two examples of the synthetic light curves and their Fourier spectra. Panels (a1) and (a2) show the noneclipsing and eclipsing HBSs, respectively. Panels (b1) and (b2) show the corresponding Fourier spectra. The red vertical dashed lines represent the orbital harmonics.
Paper: Heartbeat Stars Recognition Based on Recurrent Neural Networks: Method and Validation
Authors: Min-Yu Li, Sheng-Bang Qian, Li-Ying Zhu, Wen-Ping Liao, Lin-Feng Chang, Er-Gang Zhao, Xiang-Dong Shi, Fu-Xing Li, Qi-Bin Sun, Ping Li
Figure caption: The architecture of the two neural networks. Each network consists of two recurrent layers (either GRU or LSTM) of 50 units each, interleaved with a dropout layer (rate = 0.2). The input layer processes 100-dimensional features, and a one-unit dense layer generates the output.
Paper: Heartbeat Stars Recognition Based on Recurrent Neural Networks: Method and Validation
Authors: Min-Yu Li, Sheng-Bang Qian, Li-Ying Zhu, Wen-Ping Liao, Lin-Feng Chang, Er-Gang Zhao, Xiang-Dong Shi, Fu-Xing Li, Qi-Bin Sun, Ping Li
Figure caption: Training process for the GRU and LSTM networks. The left and right two panels show the loss and accuracy curves, respectively. Blue solid and orange dashed lines correspond to the training and validation sets, respectively.
Paper: Identification of Outer Galaxy Cluster Members Using Gaia DR3 and Multidimensional Simulation
Authors: Vishwas Patel, Joseph L. Hora, Matthew L. N. Ashby, Sarita Vig
Figure caption: Membership distribution for one of the 10 HDBSCAN-MC runs (corresponding to the horizontal axis in Figure 3) generated for Cluster 257 in the W4/W5 complex. The horizontal axis indicates the number of times YSOs are identified as cluster members, while the vertical axis shows the count of such YSOs within each histogram bin. The dotted green line represents the Monte Carlo threshold (﹩{ \mathcal M }{{ \mathcal C }}_{thr}=29,000﹩) set for this cluster.
Paper: Identification of Outer Galaxy Cluster Members Using Gaia DR3 and Multidimensional Simulation
Authors: Vishwas Patel, Joseph L. Hora, Matthew L. N. Ashby, Sarita Vig
Figure caption: High-level flowchart depicting HDBSCAN-MC execution process. To enhance readability, the arrows associated with the calculation of positional (X, Y, Z) and velocity dimensions (VelR.A., Veldecl.) are color coded in blue and red, while the rest of the data/information flow is depicted using black arrows. The algorithm produces the membership record array, indicating the number of times each YSO is identified as a cluster member across 105 Monte Carlo iterations of 3D sampling. This involves generating a mock YSO cluster catalog by sampling from the ICRS distance error distribution, proper motion in R.A. (pmra or ﹩{\mu }_{\alpha }\cos \delta ﹩), and proper motion in decl. (pmdec or μδ) for each YSO in the R.A.–decl. region under analysis.
Paper: Identification of Outer Galaxy Cluster Members Using Gaia DR3 and Multidimensional Simulation
Authors: Vishwas Patel, Joseph L. Hora, Matthew L. N. Ashby, Sarita Vig
Figure caption: HDBSCAN-MC stability test for Cluster 257 (Cluster in W4/W5 complex) (a) For 1000 iterations (b) For 100,000 iterations. The horizontal axis denotes the run for which the algorithm is repeated, and the vertical axis gives the number of cluster members identified by HDBSCAN-MC in that run. The Monte Carlo threshold derived from the cluster simulation is set at 29,000 for this cluster. The dotted red line marks the number of common members across all HDBSCAN-MC runs, representing the final cluster members. The plots demonstrate the convergence of common members to a stable value as the number of Monte Carlo iterations increases.
Paper: Identification of Outer Galaxy Cluster Members Using Gaia DR3 and Multidimensional Simulation
Authors: Vishwas Patel, Joseph L. Hora, Matthew L. N. Ashby, Sarita Vig
Figure caption: Comparison of feature distribution for the W4/W5 complex (Cluster 257) for a mock data set generated during the execution of HDBSCAN-MC: (a) before scaling and (b) after scaling. The X, Y, and Z coordinates (in pc) represent the YSO’s Galactocentric positions derived from sampled ICRS distances, while VelR.A. and Veldecl. (in km s−1) represent velocities obtained from sampled ICRS YSO distances and sampled proper motions. The application of feature scaling according to Equations (5) and (6) effectively brings the distribution of YSO astrometric features into comparable ranges, facilitating clustering with HDBSCAN*.