dataesr/scientific-paragraphs-categorization huggingface dataset:
Primary Targets Figure  1  depicts the bitmask that defines the DESI TARGETID. 54 In the rest of this subsection, we will detail the bits that comprise a "primary" TARGETID, by which we mean the unique identifier for a source that is part of one of the primary DESI subprograms, i. e. , LRGs, ELGs, QSOs, the BGS or the MWS, rather than being part of a secondary program (see, e. g. , Table  2 ). Imaging from the Legacy Surveys  (Dey et al. 2019; D. Schlegel et al. 2023, in preparation) , on which DESI targeting draws, is processed through the legacypipe pipeline 55 to produce catalogs of sources. The legacypipe code extracts these sources in individual regions of the sky denoted as bricks. The bricks cover an area of roughly 0°. 25 × 0°. 25 on the sky, and each brick is assigned a unique integer from 1 to 662,174.
The RELEASE Bits OBJID and BRICKID encode a unique source but not the imaging reductions from which that source was extracted. To track this, starting with Data Release 4 (DR4), the Legacy Surveys included a column RELEASE that records imageprocessing information. 57 RELEASE is an integer in the thousands, with the first digit denoting the Data Release and subsequent digits representing the photometric system. The Legacy Imaging Surveys commenced with Data Release 1, which means RELEASE numbers <1000 have no predefined meaning. As we will discuss more in subsequent sections, we take advantage of this extra bit-space by using integers with RELEASE <1000 to indicate a target that (potentially) was selected using information from beyond the Legacy Surveys. In preparation for going on-sky, the DESI collaboration produced multiple catalogs of ersatz targets to stress test the end-to-end software pipelines and to model survey outcomes.
The MOCK bit is a single bit (again, see Figure  1 ) that encodes whether or not a source is derived from such "mock" data. In addition, the desitarget pipeline includes a randoms module that can be used to produce catalogs of points that mimic the selection function of sources in the Legacy Imaging Surveys (see Section 4. 5. 1). A value of "1" for the MOCK bit indicates that a target was generated as part of either a "mock" data run or a random catalog. Information at locations created in desitarget random catalogs is derived at the pixel level using images from the Legacy Surveys (again, see Section 4. 5. 1). As such, RELEASE and BRICKID are inherited directly from the Legacy Surveys for points in random catalogs. Within a brick, desitarget assigns an OBJID integer for random points sequentially in order of R.
The SKY Bit and TARGETIDs for Blank Skies The desitarget pipeline also generates blank-sky positions to facilitate spectral calibration (see Section 4. 4. 1). The SKY bit is a single bit (again, see Figure  1 ), which, if set, indicates that a target corresponds to a sky position. The majority of blank-sky locations in the DESI footprint are assigned at the pixel level using blob maps from the Legacy Surveys imaging (again, see Section 4. 4. 1). For these sky locations, RELEASE and BRICKID are therefore inherited directly from the Legacy Surveys. Within each brick, the desitarget code assigns sequential integers for OBJID. This schema is guaranteed to produce unique values of TARGETID as setting the SKY bit distinguishes combinations of [RELEASE, BRICKID, OBJID] from any target derived from the Legacy Surveys source catalogs.
Guiding, Focusing, and Alignment targets The DESI instrument requires samples of stars to use for guiding, focusing, and alignment. Collectively, we will refer to these as guiding, focusing and alignment targets, or just "GFAs. " The desitarget pipeline includes a module called gfa specifically for assembling these targets. GFAs are publicly available online in directories accompanying those described in Section 4. 1. 1, where the directory names terminate with gfas instead of targets. 72 Within a gfas directory, files have names of the form gfas-hp-HPX. fits. Here, HPX refers to the nside = 8 integer of the HEALPixel that contains the GFAs. 73  The selection of GFAs did not significantly change between iterations of SV and the DESI Main Survey. GFA targets essentially comprise all sources in Gaia limited to G < 21 in the Gaia G band.
We include extra imaging information for Gaia sources that are also in the Legacy Surveys sweep catalogs 74 (i. e. , that have REF_ID > 0) and that are not part of the 2020 Siena Galaxy Atlas (J. Moustakas et al. 2023, in preparation). This ensures that high-precision fluxes and morphologies are retained for GFAs in the primary DESI targeting footprint to make quality cuts downstream of desitarget, if needed. Between the SV and Main Survey phases of DESI the desitarget. gfa code was updated to include the option of substituting Gaia-based values from Gaia EDR3 in place of values from Gaia DR2 (see also Section 4. 1. 4). GFA targets for the DESI Main Survey included this substitution. In general, files of GFAs that were run with Gaia EDR3 have a GAIADR value in the FITS header that is set to "edr3.
Blank-sky Locations The DESI fiber allocation software (A. Raichoor et al. 2023 , in preparation) assigns fibers to empty or "blank" locations that the DESI spectroscopic pipeline  (Guy et al. 2022 ) uses to perform sky subtraction. To assign these fibers, DESI operations utilizes precalculated lists of sky locations that are expected to include minimal flux from astronomical sources when integrated over a DESI fiber. The desitarget pipeline 76 assembles such lists of blank-sky locations using two different techniques, a pixel-level approach and a Gaiaavoidance approach, which we describe in this section. In addition, as of the Main Survey, DESI operations software determines, on-the-fly, whether stuck fibers in the DESI focal plane would collect significant flux during survey observations.
Pixel-based Sky Locations The legacypipe software used by the Legacy Surveys to extract sources from astronomical images operates on areas, called "blobs," in which pixels have signal that exceeds a certain detection threshold. The legacypipe code builds maps where pixels that lie in a particular blob are set to a positive integer that encodes that blob and pixels that are not in any blob are set to -1. These "blob maps" are created for each brick of the Legacy Surveys and are available in the metrics subdirectory of the DR9 Legacy Surveys release  (D. Schlegel et al. 2023, in preparation) , with names resembling metrics/ 000/blobs-0003m015. fits. gz. 77 The desitarget code adapts these blob maps by setting all pixels that do (do not) have a value of -1 to True (False), essentially building a pixel map that stores values of True where no sources are detected.
The resulting source-detection maps share the Legacy Surveys native resolution for a brick of 3600 × 3600 pixels, at 0 262 pixel -1. So, each map contains 12. 96M pixels spread across the approximately 0. 0623 deg 2 unique area of a brick. The desitarget pipeline produces catalogs of pixel-level blank locations at a user-specified density of sky positions. The density used for the Main Survey, for instance, was 18,000 sky locations per square degree, which is easily sufficient to fill the DESI focal plane. 78 The desitarget code then performs a binary erosion on the source-detection maps (in each unique brick) to achieve the required density. For example, if 18,000 deg -2 sky locations are desired in a 0. 0623 deg 2 brick, then a total of about 1120 locations are needed-meaning a brick needs to be eroded until it has dimensions of approximately 34 × 34.
Supplemental Sky Locations To account for the fact that certain areas of the sky covered by DESI might be outside of-or missing in-the Legacy Surveys, the desitarget pipeline generates an additional catalog of sky positions using Gaia. These "supplemental" sky locations are produced at random positions across the sky within HEALPixels at a user-specified density. 82 For SV and Main Survey files, this density was chosen to be 18,000 deg -2 to match the density at which pixel-based sky locations were generated. Supplemental sky locations are then removed if they are within a radius of 2″ of any source in Gaia DR2. Finally, to limit the total density of sky locations that could be used by the DESI fiber-assignment software, supplemental skies are removed if they share a HEALPixel with a pixel-based sky location at nside = 4096, which corresponds to a resolution of approximately 18,000 deg -2.
Random Catalogs The main desitarget module used to create random catalogs is called desitarget. randoms and the main worker function is randoms. quantities_at_posi-tions_in_a_brick(). Initially, a desired density of random points (deg -2 ) is specified. 87 Then, for each brick in the Legacy Surveys, the desired density is multiplied by the (∼0. 0623 deg 2 ) brick area, with the resulting number being modified by a (random) draw from the Poisson distribution to produce a final number of points to be sampled in a given brick. Locations (in RA and decl. ) are then generated in the brick according to that final number of points. The desitarget pipeline then queries the Legacy Surveys imaging at the pixel level, by directly sampling information from the CCDs used to produce the Legacy Surveys. In general, the files used as inputs to desitarget in this context are from the Legacy Surveys coadded stack files.
Quality Assurance Web Pages The "pixweight" files described in Section 4. 5. 2 can be used to derive useful plots of sky coverage for targets. In particular, the FRACAREA quantity allows target densities to be correctly weighted by the actual areal coverage of the Legacy Surveys imaging. In addition, as the "pixweight" files collate information about observational properties of the imaging surveys, such as depth and local stellar density, they can be used to determine how target densities correlate with systematic effects (see, e. g. ,  Chaussidon et al. 2022b ). The desitarget. QA module can be used to combine the "pixweight" files from Section 4. 5. 2 with the target files detailed in Section 4. 1. 1 to create web pages of diagnostic plots for DESI targeting. The gateway to these web pages also includes a version of the target density matrices provided in Colors distinguish different numbers of g-band observations in DR9 of the Legacy Surveys.
An Overview of Secondary Targets In Section 2 we discussed how secondary targets can be retrieved from the DESI target files (Section 4. 1. 1) by applying the scnd_mask bit-names to bit-values in columns that resemble SCND_TARGET. In Section 3. 2 we detailed the specific, unique TARGETID that desitarget assigns to secondary targets. However, a full description of all of the scientific goals that underlie the DESI secondary target programs is beyond the scope of this paper and will be reserved for forthcoming DESI Data Release papers (e. g. , DESI Target density in the HEALPixel for each target class bit-name (see Table  3 ).
Secondary targets added after the Main Survey may also be present in additional directories named main2, main3, etc. Each of these subdirectories includes a file of notes about each secondary target class; a docs directory of text files or Jupyter notebooks documenting a particular secondary program; an indata directory of the targets supplied by a secondary program to be processed by desitarget; and an outdata directory containing files for bright-time (bright) and darktime (dark) secondary targets that include the TARGETID, DESI_TARGET, and SCND_TARGET assigned by desitarget (again, see Sections 2 and 3. 2). The outdata directory contains one or more subdirectories, which correspond to the different versions of the desitarget pipeline that were run to produce secondary targets. Each secondary targeting file is named $bit-name.
fits file. Secondary targets that have OVERRIDE set to False are not necessarily as carefully vetted as primary targets. So, the desitarget code takes several precautions to ensure that DESI fibers are not placed on very bright OVERRIDE targets that could contaminate the spectra of primary targets. First, any OVERRIDE targets that lie inside of the bright-star mask discussed in Section 4. 4. 4 are removed completely from the output files of secondary targets. Second, when secondary targets are matched to primary targets (see above), they are also matched to the Legacy Surveys sweep catalogs. 99 During this match, information from Gaia and the Legacy Surveys is added to the secondary targeting file, including flux and magnitude quantities (FLUX_G, FLUX_R, FLUX_Z, GAIA_PHOT_G_ MEAN_MAG, GAIA_PHOT_BP_MEAN_MAG, and GAIA_ PHOT_RP_MEAN_MAG).
How to Run the desitarget Pipeline Researchers may wish to fully recreate a suite of desitarget output files in order to conduct large-scale structure studies in the context of DESI or to inform target selection for future sky surveys. To help facilitate such endeavors, we have provided a tutorial on GitHub, in the form of a Jupyter notebook, that includes a detailed overview of how each of the data products outlined in this section were produced for the DESI Main Survey using the desitarget pipeline. 100.
SUBPRIORITY Reproducibility Assigning fibers to targets during early DESI Main Survey observations revealed an additional issue that could affect the overall reproducibility of the DESI survey. As noted in Table  4 , desitarget adds a SUBPRIORITY column to target files that is used to break ties in the event that the DESI fiberassignment code has to choose between two targets that are otherwise equivalent in observational priority. A similar SUBPRIORITY column is added by desitarget to the pixel-based and supplemental sky locations discussed in Section 4. 4. As of version 1. 0. 0 of the desitarget pipeline, SUBPRIORITY values were added as part of the function that writes files to disk. This meant that if an external routine read a target (or sky) file produced by desitarget and then wrote it back out using the desitarget writing utilities then each SUBPRIORITY would be overwritten with a new value.
Rewriting SUBPRIORITY using the approach of version 1. 0. 0 of the desitarget pipeline was not a bug, per se, but it proved problematic downstream of desitarget. In particular, the software that assigns fibers to DESI targets (A. Raichoor et al. 2023, in preparation)  uses the desitarget writing routines to produce its own output data files. To guard against accidentally overwriting SUBPRIORITY values throughout the Main Survey, the desitarget pipeline was updated to only overwrite zero-valued SUBPRIORITY entries -meaning that no values are accidentally overwritten when the DESI fiber-assignment pipeline (or any other software) uses the desitarget writing functions. Although this issue with SUBPRIORITY was fixed before the Main Survey proceeded with version 1. 1. 1 targets, differences would persist in how fibers were assigned on the 157 Main Survey tiles that used targets compiled with version 1.
Summary In this paper, we have provided a detailed overview of the DESI target-selection pipeline. The exact algorithms used to classify specific subsets of DESI bright-time and dark-time targets are detailed in other papers  (Chaussidon et al. 2022a; Hahn et al. 2022; Raichoor et al. 2022; Zhou et al. 2022; Cooper et al. 2022 ) but aspects of how these targeting algorithms are implemented are crucial to understanding how to work with DESI data. This paper also details the approaches used to classify targets to help calibrate DESI spectroscopy, such as GFA targets and blank-sky locations. The DESI target-selection pipeline, which is known as desitarget, is publicly available on GitHub. As with any software product, some documentation of the desitarget code and the resulting products will be described online and in other papers related to the DESI end-to-end pipeline (E.
Schlafly et al. 2023, in preparation; A. Raichoor et al. 2023, in preparation; Guy et al. 2022; S. Bailey et al. 2023, in preparation). As such, here we have tried to focus on an overview of the most important concepts related to the technical aspects of DESI target selection. The desitarget pipeline sets bit-values in output data files in a number of different columns, which indicate whether a particular target meets specific selection criteria. The names of these columns resemble DESI_TARGET (for dark-time DESI targets), BGS_TARGET (for bright-time galaxies), MWS_TARGET (for Milky Way targets), and SCND_TARGET (for targets beyond the primary DESI campaigns). Values in these columns can be interpreted using bitmasks with names like desi_mask, bgs_mask, mws_mask, and scnd_mask. Another important output of the desitarget pipeline is a unique TARGETID that allows each target to be tracked throughout the DESI survey.
This TARGETID encodes additional, useful information about each DESI target, such as whether a target is selected from Legacy Surveys imaging or from Gaia, whether a target is a sky location or part of a random catalog, and whether a target is part of a secondary program or is a ToO. It is important to note that different versions of the desitarget pipeline were used for different phases of the DESI survey, such as the Survey Validation phase-including the DESI One-Percent Survey-and the Main Survey phase. The evolving nature of desitarget spawned multiple complexities in target selection that we have attempted to document in this paper. Examples of such technical details include (but are not limited to): 1. That sometimes Gaia EDR3 data, instead of Gaia DR2 data, is incorporated by desitarget when processing certain target-selection classes.
2. That the URAT survey is used to help select GFA targets for sources that are missing astrometric information in Gaia DR2. 3. That a bright-star mask based on Tycho and Gaia is applied to all sky locations and secondary targets, and that secondary targets are further removed from any DESI program if they are too bright in Gaia or Legacy Surveys imaging. 4. That targets processed using version 1. 0. 0 of the desitarget pipeline were observed for the earliest part of the DESI Main Survey but that, for reproducibility reasons, DESI rapidly updated to observing targets processed with version 1. 1. 1 of the desitarget pipeline. The desitarget code base is now mostly stable as regards producing the static files used for DESI target selection. Further modifications to desitarget might be expected in modules related to producing random catalogs for large-scale structure analyses, and to converting static files to the dynamic files used to monitor the state of a DESI target as it is observed (see also E.
Legacy Surveys also uses data products from the  Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE ), a project of the  Jet Propulsion Laboratory/California Institute of Technology , funded by the  National Aeronautics and Space Administration. Legacy Surveys  was supported by the Director,  Office of Science, Office of High Energy Physics of the U. S. Department of Energy ; the  National Energy Research Scientific Computing Center , a  DOE Office of Science User Facility ; the  U. S. National Science Foundation ,  Division of Astronomical Sciences ; the  National Astronomical Observatories of China , the  Chinese Academy of Sciences , and the  Chinese National Natural Science Foundation. LBNL is managed by the  Regents of the University of California  under contract to the  U.
2019). Some of the results in this paper have been derived using the healpy and HEALPix packages. Facilities:  Blanco (DECam) ,  Bok  ( 90Prime ), Gaia, Mayall (DESI, Mosaic-3), NEOWISE, WISE Software: astropy (Astropy Collaboration et al. 2013Collaboration et al. , 2018)), fitsio (https://github. com/esheldon/fitsio),healpy (Zonca et al. 2019), matplotlib (Caswell et al. 2021), numpy (Harris et al. 2020), photutils (Bradley et al. 2019), pyyaml (https://pyyaml. org/),scipy (Gommers et al. 2021).
Gene Ontology and Pathway analysis The DEG from each comparison were submitted to ShinyGO  (11)  for Gene Ontology (GO) analysis. Pathway analysis based on the KEGG database revealed 39 enriched pathways at the end of P2 for CR and 18 at the end of P3 for LW (Top 10 shown in Fig. 4 ). However, the smaller number of DEG identi ed at the end of P2 for LW and at the end of P3 for CR did not allow to reach any signi cant KEGG pathway enrichment. When analysed within the NF treatment (P3 vs. P2), ShinyGO could not nd any KEGG enrichment for CR and only 2 pathways were found for LW. Within the RF treatment (P3 vs. P2), 3 KEGG pathways were enriched in CR and 14 for LW. For LW, the number one enriched pathway within the RF treatment was thermogenesis followed by metabolic pathways. Enriched pathways within treatments are shown in Fig.
A timerestricted feed restriction was chosen to induce a mild restriction but to allow reduced competition that could lead to excessive feeding by some pigs. During the experiment, 2 CR pigs (one NF, one RF) died due to illness or injury and their data were removed from the database, thus the nal dataset consisted of data from 44 pigs [17 NF (8CR, 9LW) and 27 RF (14 LW, 13 CR)].
Measurements Ambient temperature and relative humidity were continuously recorded (1 measurement every 15 min) in the building using a stand-alone USB data logger (EL-USB-2+; DATAQ Instruments, Inc. , Akron, OH) located near the growing pens. Body weight was measured at the beginning of the experiment (d0) and then at the end of each period (week 15, week 18, week 21). In order to evaluate the effect of feed restriction and breed on thermoregulation, rectal temperature (RT) and skin temperature (ST) were measured in all pigs at the end of each period (week Library preparation and sequencing preparation was performed according to Aboshady et al. (54). High-quality RNA (RIN > 7. 5) was used for the preparation of cDNA libraries according to Illumina's protocols (Illumina TruSeq RNA sample prep kit for mRNA analysis).
Quality control and read mapping to the reference genome The quality control check on raw reads in FASTQ format were processed using FASTQC and the Q30, GC content and length distribution of the clean data were calculated. The sequences obtained by RNA-Seq were splice-aligned for each library, using STAR (version 2. 3. 0e with standard parameters)  (54). The reads were mapped to the Sus Scrofa genome (assembly 11. 1). HTSeq (http://pypi. python. org/pypi/HTSeq) (55) was used to calculate the number of sequence reads aligned to all protein-coding genes from the ENSEMBL v74 annotation of the Sus scrofa genome. The Bioconductor package DeSeq2 (56) was then used to identify differentially expressed genes (DEGs). Four treatment comparisons were tested for DEGs for each breed: (i) RF v. NF at the end of Period 2; (ii) RF v.
NF at the end of Period 3; (iii) NF Period 2 v. RF Period 3; and (iv) NF Period 2 v. RF Period 3. Statistically signi cant (P < 0. 05) DEGs with a Benjamini-Hochberg false discovery rate of < 0. 05 were deemed to be signi cant. Analysis of canonical pathways and regulatory effects as well as network analysis were performed using Ingenuity pathway analysis (IPA) software (Ingenuity Systems, Redwood City, CA) for DEGs in each comparison. IPA identi es known regulators, including genes and other molecules that may affect the expression of DE genes, then it calculates a z-score, which is a statistical measure of the match between the expected relationship direction between the regulator and its targets, and the observed gene expression  (57). Moreover, KEGG pathway and Gene Ontology enrichment analyses were performed using ShinyGO  (11).