dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2013; Pani and Verma, 2014). Also, the daytime mean BC concentration exhibits low hourly variability and corresponds to the well-mixed layer of the atmosphere  (Verma et al. , 2013; Pani and Verma, 2014). Hence, the lower value of the daytime mean from the model than from observations is primarily attributable to a low emission strength. Evaluation of model estimates for both the daytime and all-day mean thus provides a systematic hypothetical approach to identify the model discrepancy if primarily due to emissions or due to model processes attributed to meteorology (which is an input to the various aerosol processes that govern the atmospheric residence time of aerosols). This approach is further strengthened by implementing BC emissions from five new BC emission inventory databases and simulating BC transport subjected to the same aerosol physical and chemical processes with CHIMERE.
e. for 100 % (100 %) of the total data points corresponding to the measured value at stations under study. Estimates from Pku exhibit the best performance for about 50 % (50 %) of the total stations. Those from the Smog and Edgar simulations are about 40 % (10 %) of the total stations under study. Estimates from the Smog and Edgar simulations are the most frequent, corresponding to moderate and poor efficiency, respectively. Notably, unlike the Constrained simulation, the best efficiency is infrequent (< 10 %), specifically for the daytime mean BC concentrations from the Smog, Edgar, and Cmip simulations, thereby indicating that the BC emission strengths of the respective emission database as input in the model are insufficiently low to simulate the BC distribution adequately over the IGP.
6e ). It is also seen that the patch with the most substantial value (>15 W m -2 ) of net radiative forcing due to BC is observed in and around megacities and is extended to the eastern coast  son of the radiative effect due to BC from Constrained estimates with that from the Smog simulation estimates shows that bottom-up BC emissions (e. g. Smog-India) lead to relatively lower wintertime radiative warming at ATM and TOA by 30 %-50 % than the Constrained emiss emissions over most of the IGP and by more than 80 % over the northern IGP (in and around Delhi). The comparison between the bottomup and Constrained estimates thus indicates the potential underestimation of wintertime direct radiative perturbation due to BC aerosols over the IGP attributable to the low BC emission strength in the bottom-up BC emission database.
Conclusion In the present study, wintertime direct radiative perturbation due to black carbon (BC) aerosols was examined over the Indo-Gangetic Plain (IGP) by evaluating the efficacy of the fine grid-resolved (0. 1 • × 0. 1 • ) BC aerosol transport in a chemical transport model (CHIMERE) offline coupled with the WRF regional meteorological model. The efficacy of CHIMERE to simulate the observed BC surface concentration was assessed by implementing the new BC emission inventories and through a detailed validation and statistical analysis of the simulated BC concentration with respect to ground-based measurements at stations over the IGP. The five BC transport simulations (Constrained and bottomup, with the latter including Smog, Cmip, Edgar, and Pku) were performed by implementing BC emission data from the India-based "Constrained emiss " and bottom-up Smog-India database as well as those from the three global bottom-up The WRF-simulated winter monthly mean of the meteorological parameters resembled (bias < ± 25 %) the measured counterparts.
However, the diurnal distribution of meteorological parameters exhibited a considerable discrepancy, specifically during midnight to early morning hours (00:00 to 05:00 LT), having implications for the diurnal distribution of the BC concentration. In this regard, a better temporally resolved meteorological boundary condition in the WRF, aided by data assimilation at a fine temporal resolution (e. g. 1hourly) using observations for India-based stations, needs to be assessed in a future study. A strong association of the winter monthly mean BC concentration between modelled and measured values for stations under study corresponding to each of the five simulations was noticed. The efficacy to simulate the magnitude of the observed wintertime BC distribution was found to be moderate to poor for the bottomup simulation.
The Bright Time Extension permitted changes to the main survey strategy, the inclusion of new programs in response to scientific discoveries or to exploit major new datasets not available at the outset of the survey design, and expansions of existing programs to enhance their scientific success and reach. After describing the motivations, implementation, and assessment of these programs, we also leave a summary of lessons learned from nearly a decade of APOGEE-1 and APOGEE-2 survey operations. A companion paper, F. Santana et al. (submitted), provides a complementary presentation of targeting modifications relevant to APOGEE-2 operations in the Southern Hemisphere.
INTRODUCTION The Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2; S. Majewski et al. in prep)  is one of the programs in the Sloan Digital Sky Survey IV (SDSS-IV;  Blanton et al. 2017 ) and the successor survey to APOGEE (referred to as APOGEE-1, hereafter, for clarity;  Majewski et al. 2017)  in SDSS-III  (Eisenstein et al. 2011). APOGEE-2 operates in both the Northern and Southern hemispheres, with the original APOGEE spectrograph on the Sloan Foundation Telescope at Apache Point Observatory (APO;  Gunn et al. 2006 ) and an almost clone spectrograph on the Irénée DuPont telescope at Las Campanas Observatory (LCO;  Bowen & Vaughan 1973) ; both instruments are described in  Wilson et al. (2019). When referring to the SDSS-III program, we will use "APOGEE-1" and, for the combination of APOGEE-1 and APOGEE-2, we will use "APOGEE" to encompass the joint dataset.
APOGEE Datasets Used in this Paper Throughout this paper, we will show results from APOGEE-1 and APOGEE-2 using the final sample and pipeline to be released as Data Release 17 (DR17) planned for December 2021 (J. Holtzman et al. , in prep. ). Each of the image reduction, radial velocity measurements, spectral combination, and the derivation of stellar parameters and chemical abundances have been modified from that of Data Release 16 described in  Jönsson et al. (2020). For the purposes of this paper, we typically show targets using targeting-related planes (typically Gaia color-magnitude diagrams), but for the evaluation of some targeting strategies, we will use the stellar parameters (log g and T eff ). From the perspective of what is used in this paper, however, the impacts are overall small and the reader may use intuition from  Jönsson et al.
To determine the numbers of stars observed in each of these stellar classifications, we will only compare the numbers of those stars with calibrated measurements (see  Jönsson et al. 2020, (J. Holtzman et al. , in prep. ) ), e. g. , those with the LOGG tag populated. We will use the following definitions: a dwarf is a star with log g> 4. 1, a giant has -1 <log g<3. 5, and subgiants have 3. 5 <log g<4. 1. APOGEE2 TARGET3 = 30 G. l = 0 • 270 • 180 • 90 • 180 • 0 • 30 • b = 60 • -30 • -60 • APOGEE-2N APOGEE-1 APOGEE-2S Bulge Disk Because spectro-photometric can be determined for stars that are well beyond the current reach of trigonometric parallaxes from Gaia, will also use spectro-photometric distances following the methods of  Rojas-Arriagada et al. (2017 , 2019 , 2020) , but applied to the internal, incremental data release mentioned above that uses the DR16 pipeline final DR17 dataset described above (e.
g. not the specific datasets described in those works, but using the same spectro-photometric distance method). As described in Rojas-Arriagada et al. (2020, their Section 2. 2), the APOGEE spectroscopic parameters (T eff , log g, and [M/H]) are used to match an observed star to potential absolute magnitudes on PARSEC stellar evolution tracks  (Bressan et al. 2012; Marigo et al. 2017). Using the observed 2MASS photometry (JHK s ), the distance and extinction can be determined. A detailed comparison of these distances to those derived by other studies including Gaia trigonometric parallaxes is given in Rojas-Arriagada et al. (2020, their appendix A).
Telluric Selection and MaStar Co-Targeting The SDSS-IV MaNGA Stellar program (MaStar;  Yan et al. 2019 ) uses the MaNGA fiber bundles  (Bundy et al. 2015)  to collect stellar spectra; this is distinct from APOGEE-2N co-targeting with MaNGA galaxy observations that is described in Z17. The aim of these observations is to build a fully empirical stellar library with a broad span in stellar type and abundance for use in the MaNGA project  (Yan et al. 2016 (Yan et al. , 2019)). Because the observations for MaStar occur in tandem with the APOGEE-2N observations using the same plates, the plate design process takes into account the locations of MaStar targets, and this can influence APOGEE-2N targeting. Prior to the BTX, the MaStar targets were included on the plate at the lowest priority, with the intent of having the smallest impact on the APOGEE-2 targeting.
BTX Halo Targeting Method 1: Known Halo Giants in SEGUE Coincident with the BTX planning was the submission of an Ancillary Science Program on the distant halo, described in Subsection B. 4, that intended to use spectro-photometric distances derived from SEGUE and SEGUE-II observations in SDSS-II and SDSS-III, respectively, to expand upon the APOGEE-2 halo targeting at large heliocentric distances  (Yanny et al. 2009; Eisenstein et al. 2011, C. Rockosi et al. in prep). We opted to fold the Ancillary Science Program into the BTX halo program, making the former much larger in scope and implemented at a high priority. Xue et al. (2014)  identified over 6000 giants in the SEGUE sample, many of which were both metal poor and distant; exploiting this dataset is an ideal way to ensure that APOGEE-2 adequately samples the chemical fingerprint of the distant halo.
The remaining panels of Figure  4  show metrics to quantify the relative success of our BTX halo targeting strategy against those of the other targeting scenarios used in APOGEE-2. Here we focus on three in particular: • MaNGA Co-Targeting: As described in Z17, APOGEE-2 observations were obtained in tandem with MaNGA observations of their galaxy sample. These fields are in the North Galactic Cap, which is in our halo ( ,b) range. The APOGEE-2N targeting strategy for these "free fibers" adopted the high-latitude halo color-cut (Table  2 ), but a faint limit of H ∼11. 5. 10 These pointings can be identified using the PROGRAMNAME of "manga" and the program is comprised of 3-visit fields. While not formally part of the APOGEE-2N Halo Program, the MaNGA Co-Targeting sample is a good comparison set as it is a true near-infrared-based magnitude-color selection without any other interventions or modifications aiming to isolate giants.
However, this was due to the sheer number of fibers allocated in this way, and the halo stars accumulated were collected very inefficiently; the BTX strategy is more efficient by factors of ∼2 to ∼18 over the MaNGA co-targeting program in terms of yield per fiber hour (Table  5 ). The counts of the MaNGA sample suggest that even relatively shallow but wide-area strategies can accumulate large numbers of stars at halo-relevant distances; however, the stars are accumulated highly inefficiently and adding in a proper-motion based criteria would improve the efficiency at reaching distant halo stars. We note that in many cases the highest-latitude fields are so sparse in stars of any type that cohorted targeting strategies like the Original Halo will "run-out" of suitable bright targets. This too motivates the sample building potential of a wide-and-shallow approach, like those taken in the MaNGA co-targeting or BTX halo.
Recently, however, Souto et al. (  2017 ) was able to use APOGEE spectra to extract detailed chemical abundances that paved the way for adaptations in the ASPCAP methodologies that address the atmospheres of cool dwarfs. As a result, the DR16 ASPCAP pipeline included a multi-year effort for extensive expansion of the input linelist to improve the underlying spectral synthesis to expand the range of T eff with reliable ASPCAP results (these efforts are described in  (Jönsson et al. 2020 ) and V. Smith et al (in prep. )). At the same time, the work of  Souto et al. (2020)  and  Birky et al. (2020)  establishes key calibrator datasets for late type dwarfs, although the number of appropriate calibration stars is still small in number compared to the target sample for which they are needed. Indeed, it is expected that, by the end of survey, a sample of 10's of thousands M dwarfs will exist in the APOGEE dataset.
EXPANSION OF EXISTING PROGRAMS This section discusses programs that were previously described in Z17 but that were expanded upon in the BTX. These program expansions largely applied to "Goal Programs" that were outside of the "main red star sample" that formed the core of the targeting strategy. The following programs or objectives were expanded: (1) Open Clusters    and C13 ). Each panel reflects the APOGEE dataset in the vicinity of these clusters (through APOGEE-1, the original APOGEE-2 program, and the BTX), with all targets in the fields shown as small grey points. Special targets are shown as larger, colored symbols with confirmed cluster members (APOGEE2 TARGET2 bit 10) in yellow and candidate cluster members (APOGEE2 TARGET1 bit 9) in blue. Combining these clusters, it is possible to compare pipeline results against external data for these clusters to understand better the ASPCAP pipeline systematics.
For the BTX, the KOI program was expanded by ∼40% via the inclusion of multi-epoch observations for two additional Kepler modules (FIELD of K18 070+14 btx and K19 076+07 btx). As with the base program, the NExSCI archive 21  was queried for the current state of KOIs (CONFIRMED versus CANDIDATE) during targeting (approximately May 2018). These fields were drilled and designed as early as possible in the BTX implementation to ensure that they obtain the maximal possible APOGEE-2N time baseline (∼2 years). The BTX fields have PROGRAMNAME 'koi btx' and the FIELD names have ' btx' appended. KOI objects are APOGEE2 TARGET3 bit 0 set and control targets have APOGEE2 TARGET3 bit 2 set. An Ancillary Science Program for tidally synchronized binaries have APOGEE2 TARGET2 bit 12 (Subsection B. 7).
• (Subsection A. 7) One of the Ancillary Science Programs is focused on targeting a library of well-known and previously well-measured stars for the purposes of improving ASPCAP calibration. Beyond the spectroscopic surveys, we have also now guaranteed broad spectroscopic coverage for photometric objects of interest coordinated with Kepler, K2, and TESS. In particular, our targeting in the BTX relied on the input catalogs or target lists from those photometric surveys such that we could draw from those parent samples using a selection algorithm that enables study of large stellar samples. Lastly, our datasets have large overlap with stars having Gaia astrometry (either from DR2, provided in DR16, or eDR3, to be provided with DR17) and we anticipate additional overlap in successive releases from that mission  (Jönsson et al.
To enable these projects, the APOGEE-2 targeting team performed detailed assessments of existing targeting strategies and devised new ones, as described in this paper. Lastly, many of our programs relied more heavily on complementary datasets from other surveys and, as a result, the cross-survey calibration samples were bolstered significantly. Targets from these two programs represents over ∼25% of the total number of stars observed in the APOGEE-2N and serve to open new scientific explorations and explore unconventional ideas while also reinforcing our core science goals. APOGEE's ability to achieve its scientific goals over its decade of operations stems not just from the established and successful organizational infrastructure of SDSS, but also the tight collaborative cooperation between its scientific and technical teams, and its adaptations to developments in the broader scientific field.
Together, this permits an unprecedentedly comprehensive study of the early evolution of cluster forming complexes. The massive star sample was selected from the combination of NIR-MIR color CMDs and color-color based selection criteria following  Roman-Lopes et al. (2016). The catalogue was then cross matched with the known OB stars, using the SIMBAD database  (Wenger et al. 2000)  together with the catalogues of spectral classification of stars in the direction of W345. The YSO sample was selected based on two different approaches. The first used Spitzer colors combined with an X-ray detection that identifies probable young star members. These are traditionally subdivided into three categories: Class I sources (deeply embedded protostars with large IR-excesses), Class II sources (those with IR excesses typified by the presence of disks), and Class III sources (those lacking IR excesses, having had their disks dispersed).
A. 12. M Dwarf Kepler Objects of Interest Stellar parameters such as mass (M * ) and chemical composition play an important role in influencing planetary formation and the nature of the resultant planetary-system architecture. The abundances of key metals, such as O, Mg, Si, or Fe, affect the nature and structure of the planets themselves. Unlike F, G, or K dwarfs, very little is known about the detailed chemical abundance distributions of M-dwarfs in general, and for planet-hosting M-dwarfs in particular, due to the complex nature of their optical spectra. This situation can be improved greatly by shifting the analysis into the near-infrared where the high-density of molecular lines drops dramatically. High-resolution NIR spectra are the preferred dataset for abundance analyses of the cool M-dwarfs, thus APOGEE is well-positioned to contribute to this field.
The new PanSTARRs (PS1) catalog of variable stars is currently still under construction. We selected a list of targets determined on the basis of the following steps: [1] identification of Cepheids-like variables in the entire PS1 database, by using the variability parameters by  Hernitschek et al. (2016)  and colors defined on the basis of magnitudes in the grizy (PS1) and near-infrared bands from 2MASS  (Skrutskie et al. 2006 ) and ALLWISE  (Cutri & et al. 2014 ); [2] multi-band template fitting of the sparsely sampled PS1 light curves for all the 200,000 sources selected and determination of pulsation period and other parameters (including distance modulus and extinction); [3] use of machine-learning techniques to extract the variables classified as Cepheids with highly significant probability (i.
B. 8. SubStellar Companions With the ever-growing sample of companions (both stellar and substellar) being discovered around stars from ever more diverse environments, population studies comparing host environments are now possible. For example, recent work suggests the hot Jupiter occurrence rate in M67 is higher than in the field  (Brucalassi et al. 2016 ), although it is not clear whether this is the norm for open clusters generally. We aim to expand upon the substellar companion science program in APOGEE-2 (Z17) to include stars in a variety of cluster environments with a wide range of ages, metallicities, and densities. These observations will help constrain the role environment has to play in the formation and evolution of stellar systems containing companions of various masses. Critically, this program will both extend the time baseline for targets observed in APOGEE-1 and increase the number of RV measurements to enable robust orbital fits in the final dataset.
CIS: The Carnegie Institution for Science or CIS is an SDSS-IV partner and operates the Las Campanas Observatory in Chile. Cohort: Set of targets in the same field that are observed together on all of their visits. A given plate may have multiple cohorts on it. Contributed Program: Term for programs allocated to Principal Investigators by the CIS or CNTAC but whose data are contributed to the APOGEE-2 survey. These data appear in SDSS data releases, but their targeting was performed by the PI. These programs are described in (F. Santana et al. in prep. ).
CNTAC: The Chilean National Telescope Allocation Committee, which allocates observing resources to the Chilean community. Design: Set of targets drilled together on a plate, consisting of up to one each of short, medium, and long cohorts. A design is identified by an integer Design ID. Changing a single target on a design results in a new design. Design ID: Unique integer assigned to each design. Drill Angle: Hour angle (distance from the meridian) at which a plate is drilled to be observed. This places the fiber holes in a way that accounts for differential refraction across the FOV. External Program: General term for programs and targets observed during the APOGEE-2S time allocated by the Carnegie Observatories (OCIS) or the Chilean Time Allocation Committee (CNTAC). These targets will not be included in the SDSS dataset.
GAP: The K2 Galactic Astrophysics Program, which is described in  Stello et al. (2017). LCO: Las Campanas Observatory, site of the Irénée du Pont 2. 5-m telescope  (Bowen & Vaughan 1973)  on which the APOGEE-S spectrograph operates. Location ID: Unique integer assigned to each field on the sky. Main Red Star Sample: The sample drawn from a simple selection function defined by magnitude and color that comprises the bulk of the APOGEE program. This program is explained in Z13 and Z17. MaNGA: Mapping Nearby Galaxies at Apache Point Observatory; A SDSS-IV program described in  Bundy et al. (2015). MaStar: The MaNGA Stellar Program; a program within the MaNGA Survey with the objective of constructing a high-fidelity stellar library. An overview of the project and its first data release is described in  Yan et al.
ACKNOWLEDGMENTS The  APOGEE  project thanks  Jeff Munn  ( NOFS ) for collecting Washington+DDO51 imaging for large areas of the sky. The authors also recognize contributions from prior members of the targeting team and the members of the science working groups in both APOGEE-1 and APOGEE-2. We graciously thank the staff at  Apache Point Observatory  and the  SDSS Observers  for both their dedication and their remarkable ability to exceed themselves without needing to. We also warmly recognize the APOGEE-2N Operations team for their diligence and the  SDSS Plate Shop  for their enduring patience. We thank the  SDSS-IV Data Team  for always remembering and then carefully considering all of the details and doing so without a complaint, albeit often with a stroopwafel. We thank the  SDSS-IV Management Committee  and all SDSS-IV leadership whose commitment to SDSS-IV is literally is the glue that keeps the project going.
J. D. and P. M. F. acknowledge support for this research from the  National Science Foundation AAG and REU programs  ( AST-1311835 ,  AST-1715662 ,  PHY-1358770 ,  & PHY-1659444 ). D. G. gratefully acknowledges support from the  Chilean Centro de Excelencia en Astrofísica y Tecnologías Afines (CATA) BASAL  grant  AFB-170002. D. G. also acknowledges financial support from the  Dirección de Investigación y Desarrollo de la Universidad de La Serena  through the  Programa de Incentivo a la Investigación de Académicos (PIA-DIDULS). S. H. is supported by an  NSF   Astronomy and Astrophysics Postdoctoral Fellowship  under award AST-1801940. AR-L acknowledges financial support provided in Chile by  Comisión Nacional de Investigación Científica y Tecnológica (CONICYT)  through the  FONDECYT  project  1170476  and by the  QUIMAL  project  130001.