adsabs/AstroCLIMB huggingface dataset:
Paper: Objective Separation between CP1 and CP2 Based on Feature Extraction with Machine Learning
Authors: Lun-Hua Shang, A-Li Luo, Liang Wang, Li Qin, Bing Du, Xu-Jiang He, Xiang-Qun Cui, Yong-Heng Zhao, Ri-Hong Zhu, Qi-Jun Zhi
Figure caption: Wavelength in the range from 4900 to 5600 Å. LAMOST J051711.10+365831.6, J193927.58+471826.3, J063114.46+184731.8, J030614.98+485615.0, and J041641.13+511253.2, are identified CP2 stars. LAMOST J060747.74+231159.4 is a CP1 star. The area filled in red is the flux depression region at center 5220 Å blend with a bandwidth of 230 Å. It is found that the flux depression at the 5220 Å blend is more obvious than that for a CP1 star.
Paper: Objective Separation between CP1 and CP2 Based on Feature Extraction with Machine Learning
Authors: Lun-Hua Shang, A-Li Luo, Liang Wang, Li Qin, Bing Du, Xu-Jiang He, Xiang-Qun Cui, Yong-Heng Zhao, Ri-Hong Zhu, Qi-Jun Zhi
Figure caption: The normalized averaged spectra of CP1 and CP2 stars. The red spectra in the upper panel are the averaged spectra of CP2 stars. The blue spectra in the bottom panel are the averaged spectra of CP1 stars. Black spectra in both panels are all normalized spectra of CP2 and CP1 stars spectra. For these spectra, their pseudocontinua are fitted with a seventh-order polynomial. All of the spectra are normalized by dividing the spectra by the pseudocontinua.
Paper: Objective Separation between CP1 and CP2 Based on Feature Extraction with Machine Learning
Authors: Lun-Hua Shang, A-Li Luo, Liang Wang, Li Qin, Bing Du, Xu-Jiang He, Xiang-Qun Cui, Yong-Heng Zhao, Ri-Hong Zhu, Qi-Jun Zhi
Figure caption: Showcase of manually identified characteristic lines of nine new CP2 star candidates. They were classified as spectral subclasses, B6, A1, A2, A3, A5, A6, A7, A9, and F0 from the top row to the bottom row. The spectral subclasses presented by the LAMOST catalogs are rough estimates. The reliable spectral subclass is presented by the MKCLASS code in column “SpT_mkclass” of Table 1.
Paper: Objective Separation between CP1 and CP2 Based on Feature Extraction with Machine Learning
Authors: Lun-Hua Shang, A-Li Luo, Liang Wang, Li Qin, Bing Du, Xu-Jiang He, Xiang-Qun Cui, Yong-Heng Zhao, Ri-Hong Zhu, Qi-Jun Zhi
Figure caption: The distributions of S/N vs. the Gaia DR2 G magnitude of the second year survey. The panels from left to right are the distributions of selected B-type, A-type and F0-type spectra, respectively.
Paper: Objective Separation between CP1 and CP2 Based on Feature Extraction with Machine Learning
Authors: Lun-Hua Shang, A-Li Luo, Liang Wang, Li Qin, Bing Du, Xu-Jiang He, Xiang-Qun Cui, Yong-Heng Zhao, Ri-Hong Zhu, Qi-Jun Zhi
Figure caption: Computing the feature importances of separation between CP stars and normal stars from the XGBoost algorithm with SHAP package. Each row represents a feature, a dot represents a sample. The values of the feature increase with the color change from blue to red. The horizontal axis is the snap value.
Paper: Objective Separation between CP1 and CP2 Based on Feature Extraction with Machine Learning
Authors: Lun-Hua Shang, A-Li Luo, Liang Wang, Li Qin, Bing Du, Xu-Jiang He, Xiang-Qun Cui, Yong-Heng Zhao, Ri-Hong Zhu, Qi-Jun Zhi
Figure caption: Example of a rectified flux. The top panel shows the truncated original spectrum, and the red line shows the fitted continuum obtained with seven-order polynomial function. The bottom panel shows the normalized flux, the blue line is the normalized flux is 1.
Paper: Objective Separation between CP1 and CP2 Based on Feature Extraction with Machine Learning
Authors: Lun-Hua Shang, A-Li Luo, Liang Wang, Li Qin, Bing Du, Xu-Jiang He, Xiang-Qun Cui, Yong-Heng Zhao, Ri-Hong Zhu, Qi-Jun Zhi
Figure caption: Spectral comparison diagram. The black curves are the row spectra, the red curves are the corresponding spectra in the rest frame.
Paper: Objective Separation between CP1 and CP2 Based on Feature Extraction with Machine Learning
Authors: Lun-Hua Shang, A-Li Luo, Liang Wang, Li Qin, Bing Du, Xu-Jiang He, Xiang-Qun Cui, Yong-Heng Zhao, Ri-Hong Zhu, Qi-Jun Zhi
Figure caption: The S/N distributions of the g band of CP2 and CP1 stars in the positive training data set. The upper panel is the distribution of CP2 stars, bottom panel is the distribution of CP1 stars.
Paper: The Impact of Observing Strategy on Cosmological Constraints with LSST
Authors: Michelle Lochner, Dan Scolnic, Husni Almoubayyed, Timo Anguita, Humna Awan, Eric Gawiser, Satya Gontcho A Gontcho, Melissa L. Graham, Philippe Gris, Simon Huber, Saurabh W. Jha, R. Lynne Jones, Alex G. Kim, Rachel Mandelbaum, Phil Marshall, Tanja Petrushevska, Nicolas Regnault, Christian N. Setzer, Sherry H. Suyu, Peter Yoachim, Rahul Biswas, Tristan Blaineau, Isobel Hook, Marc Moniez, Eric Neilsen, Hiranya Peiris, Daniel Rothchild, Christopher Stubbs
Figure caption: A simple attempt to combine multiple metrics to produce a combined cosmology metric. We show the combined metric (using the usual (metric-baseline)/baseline)) as a function of effective area (i.e., the area that meets the cuts defined in Section 3.1) and median number of visits (which correlates with depth for most simulations). Although the produced map is complex, the trade-off between depth and area can be seen. Several specific strategies are highlighted, the simulation names of which can be found in Table 2. Unlike for the rest of the figures, which focus on simulations that more or less vary one aspect of observing strategy at a time, we have highlighted here strategies from FBS v1.6, which are proposed observing strategies that aim to satisfy multiple science goals. “Solar system focus” and “Galactic plane focus” both take observations away from WFD to prioritize other science cases. “Combo dust” is a proposed large area footprint that has more area but less depth than “Baseline,” thus producing a similar performance. “Larger extragalactic footprint” is a larger area footprint that is defined by dust extinction and gives improved cosmological constraints due to the area gained by avoiding extinction. We caution the reader against assuming the performance in this metric would correspond to the exact percentage improvement/degradation in cosmological constraints. Only a full DETF FoM including systematics can indicate the exact numerical impact of observing strategy choice.
Paper: The Impact of Observing Strategy on Cosmological Constraints with LSST
Authors: Michelle Lochner, Dan Scolnic, Husni Almoubayyed, Timo Anguita, Humna Awan, Eric Gawiser, Satya Gontcho A Gontcho, Melissa L. Graham, Philippe Gris, Simon Huber, Saurabh W. Jha, R. Lynne Jones, Alex G. Kim, Rachel Mandelbaum, Phil Marshall, Tanja Petrushevska, Nicolas Regnault, Christian N. Setzer, Sherry H. Suyu, Peter Yoachim, Rahul Biswas, Tristan Blaineau, Isobel Hook, Marc Moniez, Eric Neilsen, Hiranya Peiris, Daniel Rothchild, Christopher Stubbs
Figure caption: Selected metrics, relative to their values at baseline, as a function of effective area (i.e., the area that meets the cuts described in Section 3.1) of different observing strategies. To improve readability, points that are nearby in the x-axis are binned with only the mean and error bar plotted for that bin. It can be seen that the 3 × 2 pt FoM metric simply prefers more area while the situation is more complex for time-dependent metrics. The photo-z metric tends to decrease with any reduction of depth or changes in filter distribution. We have highlighted specific simulations with numbered annotations. The simulation at 1 reduces the overall area available for cosmology by reducing visits in the crucial redder bands. Simulations 2 and 3 have larger area footprints, but degrade transient metrics and photometric redshifts due to a reduced number of visits in the extragalactic part of the WFD. Simulation 4 reverses this by completely prioritizing visits in areas of low dust extinction, resulting in both large area and a large number of visits and the best performance for all cosmology metrics. List of annotations: (1) footprint_bluer_footprintv1.5_10yrs, (2) footprint_newBv1.5_10yrs, (3) bulges_bs_v1.5_10yrs, and (4) footprint_big_sky_dustv1.5_10yrs.
Paper: The Impact of Observing Strategy on Cosmological Constraints with LSST
Authors: Michelle Lochner, Dan Scolnic, Husni Almoubayyed, Timo Anguita, Humna Awan, Eric Gawiser, Satya Gontcho A Gontcho, Melissa L. Graham, Philippe Gris, Simon Huber, Saurabh W. Jha, R. Lynne Jones, Alex G. Kim, Rachel Mandelbaum, Phil Marshall, Tanja Petrushevska, Nicolas Regnault, Christian N. Setzer, Sherry H. Suyu, Peter Yoachim, Rahul Biswas, Tristan Blaineau, Isobel Hook, Marc Moniez, Eric Neilsen, Hiranya Peiris, Daniel Rothchild, Christopher Stubbs
Figure caption: Selected metrics, relative to their values at baseline, as a function of the median internight gap of different observing strategies. To improve readability, points that are nearby in the x-axis are binned with only the mean and error bar plotted for that bin. The SN metric prefers shorter gaps between observations, but the situation is complicated by many other factors. Generally it is important that the cadence is at least no worse than the baseline. We have highlighted specific simulations with numbered annotations. Simulation 1 includes visits at high airmass, which is generally avoided, indicating that high airmass observations can improve cadence without too much degradation of metrics. Simulation 2 is when 2 × 15 s exposures are used instead of 1 × 30 s. Simulation 3 is a large area footprint but removes visits from the extragalactic area reducing cadence. List of annotations: 1-dcr_nham2_ugr_v1.5_10yrs, 2-baseline_2snaps_v1.5_10yrs, 3-footprint_newAv1.5_10yrs.
Paper: The Impact of Observing Strategy on Cosmological Constraints with LSST
Authors: Michelle Lochner, Dan Scolnic, Husni Almoubayyed, Timo Anguita, Humna Awan, Eric Gawiser, Satya Gontcho A Gontcho, Melissa L. Graham, Philippe Gris, Simon Huber, Saurabh W. Jha, R. Lynne Jones, Alex G. Kim, Rachel Mandelbaum, Phil Marshall, Tanja Petrushevska, Nicolas Regnault, Christian N. Setzer, Sherry H. Suyu, Peter Yoachim, Rahul Biswas, Tristan Blaineau, Isobel Hook, Marc Moniez, Eric Neilsen, Hiranya Peiris, Daniel Rothchild, Christopher Stubbs
Figure caption: Selected metrics, relative to their values at baseline, as a function of i-band median coadded depth of different observing strategies. To improve readability, points that are nearby in the x-axis are binned with only the mean and error bar plotted for that bin. Here we note that the 3 × 2 pt FoM metric is generally indifferent to depth, implying that larger area is more important (assuming the changes in depth remain small). Photometric redshifts improve with more depth and the transient science probes, particularly SNe and kNe, are strongly affected since generally greater depth corresponds to increased cadence. We have highlighted specific simulations with numbered annotations. Simulation 1 has the poorest performance because it removes visits from the extragalactic part of the footprint. Simulation 2 is a large area footprint that improves the static science metrics, but still reduces overall cadence and depth. Simulations 3 and 4 artificially perform well for the transient metrics because they unrealistically ignore all mini-surveys and put all visits into WFD. List of annotations: (1) footprint_newAv1.5_10yrs, (2) bulges_cadence_i_heavy_v1.5_10yrs, (3) wfd_depth_scale0.99_v1.5_10yrs, and (4) wfd_depth_scale0.99_noddf_v1.5_10yrs.
Paper: The Impact of Observing Strategy on Cosmological Constraints with LSST
Authors: Michelle Lochner, Dan Scolnic, Husni Almoubayyed, Timo Anguita, Humna Awan, Eric Gawiser, Satya Gontcho A Gontcho, Melissa L. Graham, Philippe Gris, Simon Huber, Saurabh W. Jha, R. Lynne Jones, Alex G. Kim, Rachel Mandelbaum, Phil Marshall, Tanja Petrushevska, Nicolas Regnault, Christian N. Setzer, Sherry H. Suyu, Peter Yoachim, Rahul Biswas, Tristan Blaineau, Isobel Hook, Marc Moniez, Eric Neilsen, Hiranya Peiris, Daniel Rothchild, Christopher Stubbs
Figure caption: The relative improvement over baseline for the number of SNe as a function median internight gap in each filter. The baseline cadence is represented by star markers. In general, the number of SNe improve as the cadence improves, but note the relatively small dependence on the g and u bands, indicating the higher importance of good cadence in the redder bands. A Gaussian process has been fitted to the set of points in each filter to help guide the eye; the mean is plotted with a solid line, and the standard deviation indicated with a colored envelope. Also note that we have restricted this plot to simulations with similar numbers of visits in the WFD.
Paper: The Impact of Observing Strategy on Cosmological Constraints with LSST
Authors: Michelle Lochner, Dan Scolnic, Husni Almoubayyed, Timo Anguita, Humna Awan, Eric Gawiser, Satya Gontcho A Gontcho, Melissa L. Graham, Philippe Gris, Simon Huber, Saurabh W. Jha, R. Lynne Jones, Alex G. Kim, Rachel Mandelbaum, Phil Marshall, Tanja Petrushevska, Nicolas Regnault, Christian N. Setzer, Sherry H. Suyu, Peter Yoachim, Rahul Biswas, Tristan Blaineau, Isobel Hook, Marc Moniez, Eric Neilsen, Hiranya Peiris, Daniel Rothchild, Christopher Stubbs
Figure caption: Selected metrics, relative to their values at baseline, as a function of varying choices of filter distributions. Note that these simulations use a different footprint, so all metrics are measured against the baseline filter_dist simulation, and not the standard baseline. We do not show the 3 × 2 pt FoM or LSS metrics because they do not vary significantly here. We find no compelling reason to vary the baseline filter distribution: different distributions improve different probes but at the cost of others.
Paper: The Impact of Observing Strategy on Cosmological Constraints with LSST
Authors: Michelle Lochner, Dan Scolnic, Husni Almoubayyed, Timo Anguita, Humna Awan, Eric Gawiser, Satya Gontcho A Gontcho, Melissa L. Graham, Philippe Gris, Simon Huber, Saurabh W. Jha, R. Lynne Jones, Alex G. Kim, Rachel Mandelbaum, Phil Marshall, Tanja Petrushevska, Nicolas Regnault, Christian N. Setzer, Sherry H. Suyu, Peter Yoachim, Rahul Biswas, Tristan Blaineau, Isobel Hook, Marc Moniez, Eric Neilsen, Hiranya Peiris, Daniel Rothchild, Christopher Stubbs
Figure caption: Selected metrics, relative to their values at baseline, for a simulation that uses a single 30 s exposure (baseline_nexp1_v1.7_10yrs.db), as compared to a simulation that uses two 15 s exposures (baseline_nexp2_v1.7_10yrs.db). Note that the “baseline” used here to normalize the metrics, baseline_nexp2_v1.7_10yrs.db, differs from that of other plots. It is clear that the efficiency gained through a single exposure improves all metrics, some dramatically.
Paper: The Impact of Observing Strategy on Cosmological Constraints with LSST
Authors: Michelle Lochner, Dan Scolnic, Husni Almoubayyed, Timo Anguita, Humna Awan, Eric Gawiser, Satya Gontcho A Gontcho, Melissa L. Graham, Philippe Gris, Simon Huber, Saurabh W. Jha, R. Lynne Jones, Alex G. Kim, Rachel Mandelbaum, Phil Marshall, Tanja Petrushevska, Nicolas Regnault, Christian N. Setzer, Sherry H. Suyu, Peter Yoachim, Rahul Biswas, Tristan Blaineau, Isobel Hook, Marc Moniez, Eric Neilsen, Hiranya Peiris, Daniel Rothchild, Christopher Stubbs
Figure caption: KN metrics as a function of selected observing strategies. Table 2 contains the exact simulation names corresponding to the short names used here. Metrics are transformed using the equations in Table 5 and are taken relative to their values at baseline in order to be directly comparable, with larger values always being better. Metric values at baseline are indicated in parentheses. Select annotations are added to highlight factors driving metric behavior. The larger area footprints tend to lower metric performance, indicating that good cadence is more important than area for detection of well-sampled KNe.
Paper: The Impact of Observing Strategy on Cosmological Constraints with LSST
Authors: Michelle Lochner, Dan Scolnic, Husni Almoubayyed, Timo Anguita, Humna Awan, Eric Gawiser, Satya Gontcho A Gontcho, Melissa L. Graham, Philippe Gris, Simon Huber, Saurabh W. Jha, R. Lynne Jones, Alex G. Kim, Rachel Mandelbaum, Phil Marshall, Tanja Petrushevska, Nicolas Regnault, Christian N. Setzer, Sherry H. Suyu, Peter Yoachim, Rahul Biswas, Tristan Blaineau, Isobel Hook, Marc Moniez, Eric Neilsen, Hiranya Peiris, Daniel Rothchild, Christopher Stubbs
Figure caption: Selected metrics, relative to their values at baseline, for a simulation that takes visit pairs in the same filter instead of changing filters between visits (baseline_samefilt_v1.5_10yrs). This clearly shows that while taking visit pairs in the same filter does not impact static science much, it dramatically degrades the transient metrics.
Paper: SEGUE-2: Old Milky Way Stars Near and Far
Authors: Constance M. Rockosi, Young Sun Lee, Heather L. Morrison, Brian Yanny, Jennifer A. Johnson, Sara Lucatello, Jennifer Sobeck, Timothy C. Beers, Carlos Allende Prieto, Deokkeun An, Dmitry Bizyaev, Michael R. Blanton, Luca Casagrande, Daniel J. Eisenstein, Andrew Gould, James E. Gunn, Paul Harding, Inese I. Ivans, H. R. Jacobson, William Janesh, Gillian R. Knapp, Juna A. Kollmeier, Sébastien Lépine, Martín López-Corredoira, Zhibo Ma, Heidi J. Newberg, Kaike Pan, Jakub Prchlik, Conor Sayers, Katharine J. Schlesinger, Jennifer Simmerer, David H. Weinberg
Figure caption: Residual distribution of T eff, ﹩\mathrm{log}\,g﹩, and [Fe/H] for repeat observations as a function of S/N. The sample of repeat observations is split into four different regions of (g − r)0. The (g − r)0 color range in the panels runs from blue at the top to red at the bottom. The dashed line denotes one Gaussian sigma as computed for bins of 200 stars ordered by S/N. See also Table 7.
Paper: SEGUE-2: Old Milky Way Stars Near and Far
Authors: Constance M. Rockosi, Young Sun Lee, Heather L. Morrison, Brian Yanny, Jennifer A. Johnson, Sara Lucatello, Jennifer Sobeck, Timothy C. Beers, Carlos Allende Prieto, Deokkeun An, Dmitry Bizyaev, Michael R. Blanton, Luca Casagrande, Daniel J. Eisenstein, Andrew Gould, James E. Gunn, Paul Harding, Inese I. Ivans, H. R. Jacobson, William Janesh, Gillian R. Knapp, Juna A. Kollmeier, Sébastien Lépine, Martín López-Corredoira, Zhibo Ma, Heidi J. Newberg, Kaike Pan, Jakub Prchlik, Conor Sayers, Katharine J. Schlesinger, Jennifer Simmerer, David H. Weinberg
Figure caption: Comparisons of adopted SSPP measurement of [Fe/H] with values from the high-resolution analysis, as a function of each parameter.
Paper: SEGUE-2: Old Milky Way Stars Near and Far
Authors: Constance M. Rockosi, Young Sun Lee, Heather L. Morrison, Brian Yanny, Jennifer A. Johnson, Sara Lucatello, Jennifer Sobeck, Timothy C. Beers, Carlos Allende Prieto, Deokkeun An, Dmitry Bizyaev, Michael R. Blanton, Luca Casagrande, Daniel J. Eisenstein, Andrew Gould, James E. Gunn, Paul Harding, Inese I. Ivans, H. R. Jacobson, William Janesh, Gillian R. Knapp, Juna A. Kollmeier, Sébastien Lépine, Martín López-Corredoira, Zhibo Ma, Heidi J. Newberg, Kaike Pan, Jakub Prchlik, Conor Sayers, Katharine J. Schlesinger, Jennifer Simmerer, David H. Weinberg
Figure caption: Comparison of spectroscopic (top two panels) and IRFM T eff (bottom two panels) from the SSPP with the fundamental IRFM T eff calculated using both SDSS photometry and 2MASS data. Comparisons are shown for both dwarfs and giants, separated at ﹩\mathrm{log}\,g﹩ = 3.5. The SSPP spectroscopic T eff works well for dwarfs but diverges from the 1:1 line by more than 200 K for giants. The SSPP IRFM temperature shows no such systematic trend and so is recommended for giant stars.
Paper: SEGUE-2: Old Milky Way Stars Near and Far
Authors: Constance M. Rockosi, Young Sun Lee, Heather L. Morrison, Brian Yanny, Jennifer A. Johnson, Sara Lucatello, Jennifer Sobeck, Timothy C. Beers, Carlos Allende Prieto, Deokkeun An, Dmitry Bizyaev, Michael R. Blanton, Luca Casagrande, Daniel J. Eisenstein, Andrew Gould, James E. Gunn, Paul Harding, Inese I. Ivans, H. R. Jacobson, William Janesh, Gillian R. Knapp, Juna A. Kollmeier, Sébastien Lépine, Martín López-Corredoira, Zhibo Ma, Heidi J. Newberg, Kaike Pan, Jakub Prchlik, Conor Sayers, Katharine J. Schlesinger, Jennifer Simmerer, David H. Weinberg
Figure caption:  u − g vs. g − r diagram illustrating the K- and M-giant target types for SEGUE-2. Gray points are stars from the Stripe 82 catalog of Ivezić et al. (2007), which have particularly accurate photometry. The red K giants occupy the region where K giants are found above the locus (of predominantly foreground dwarfs), the proper-motion K giants sample this locus at the red end of the K-giant color range, and the l-color K giants occupy the region where metal-poor stars are found below the locus. The M giants form the continuation to the red of the red K-giant sequence.
Paper: SEGUE-2: Old Milky Way Stars Near and Far
Authors: Constance M. Rockosi, Young Sun Lee, Heather L. Morrison, Brian Yanny, Jennifer A. Johnson, Sara Lucatello, Jennifer Sobeck, Timothy C. Beers, Carlos Allende Prieto, Deokkeun An, Dmitry Bizyaev, Michael R. Blanton, Luca Casagrande, Daniel J. Eisenstein, Andrew Gould, James E. Gunn, Paul Harding, Inese I. Ivans, H. R. Jacobson, William Janesh, Gillian R. Knapp, Juna A. Kollmeier, Sébastien Lépine, Martín López-Corredoira, Zhibo Ma, Heidi J. Newberg, Kaike Pan, Jakub Prchlik, Conor Sayers, Katharine J. Schlesinger, Jennifer Simmerer, David H. Weinberg
Figure caption: SSPP ﹩\mathrm{log}\,g﹩ values for spectroscopically verified BHBs that are confirmed members in clusters M13 and M92. It can be seen that most BHB stars have SSPP ﹩\mathrm{log}\,g﹩ values between 3.1 and 3.5, but that the hottest BHB stars in M92 have lower values, ranging up to 2.3. All of these stars have spectra with S/N > 35.
Paper: SEGUE-2: Old Milky Way Stars Near and Far
Authors: Constance M. Rockosi, Young Sun Lee, Heather L. Morrison, Brian Yanny, Jennifer A. Johnson, Sara Lucatello, Jennifer Sobeck, Timothy C. Beers, Carlos Allende Prieto, Deokkeun An, Dmitry Bizyaev, Michael R. Blanton, Luca Casagrande, Daniel J. Eisenstein, Andrew Gould, James E. Gunn, Paul Harding, Inese I. Ivans, H. R. Jacobson, William Janesh, Gillian R. Knapp, Juna A. Kollmeier, Sébastien Lépine, Martín López-Corredoira, Zhibo Ma, Heidi J. Newberg, Kaike Pan, Jakub Prchlik, Conor Sayers, Katharine J. Schlesinger, Jennifer Simmerer, David H. Weinberg
Figure caption: Color–magnitude diagram for the SEGUE-2 sample with major target classes indicated. BHB stands for blue horizontal-branch stars, ESM extremely cool subdwarfs, TO main-sequence turnoff stars, RKG red K giants, LKG l-color K giants, PKG proper-motion K giants, MP low-metallicity targets, HHV high-velocity halo stars, and MG M giants.
Paper: SEGUE-2: Old Milky Way Stars Near and Far
Authors: Constance M. Rockosi, Young Sun Lee, Heather L. Morrison, Brian Yanny, Jennifer A. Johnson, Sara Lucatello, Jennifer Sobeck, Timothy C. Beers, Carlos Allende Prieto, Deokkeun An, Dmitry Bizyaev, Michael R. Blanton, Luca Casagrande, Daniel J. Eisenstein, Andrew Gould, James E. Gunn, Paul Harding, Inese I. Ivans, H. R. Jacobson, William Janesh, Gillian R. Knapp, Juna A. Kollmeier, Sébastien Lépine, Martín López-Corredoira, Zhibo Ma, Heidi J. Newberg, Kaike Pan, Jakub Prchlik, Conor Sayers, Katharine J. Schlesinger, Jennifer Simmerer, David H. Weinberg
Figure caption: Color–color diagram for the SEGUE-2 sample with major target classes indicated, as described in Figure 2.