dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This research was supported by the  Exascale Computing Project  ( 17-SC-20-SC ), a joint project of the  U. S. Department of Energy's Office of Science and National Nuclear Security Administration , responsible for delivering a capable exascale ecosystem, including software, applications, and hardware technology, to support the nation's exascale computing imperative, and the  U. S. Department of Energy, Office of Science, Office of Advanced Scientific Computer Research , under Contract  DE-AC02-06CH11357.
They include observations of calibration fields, such as open and globular clusters, and an ≈100 deg 2 high-completeness minisurvey representative of the northern Galactic cap. We give an overview of these data sets in Section 7 and use them to demonstrate that our survey design and analysis pipeline meet the requirements set by the ambitious science goals of MWS. We summarize in Section 8. Machine-readable tables of the data shown in the figures of this paper are available at 10. 5281/zenodo. 7013864. The complete sets of targets selected for the main MWS and the DESI SV programs are available at https://data. desi. lbl. gov/ public/ets/target/as described by  Myers et al. (2023).
e. , dwarf galaxies and globular clusters). Our view of the stellar halo has changed dramatically since the first data releases of the Gaia mission. It is now understood that the inner halo (within 20-30 kpc) is dominated by one massive dwarf progenitor, namely the GSE  (Belokurov et al. 2018; Helmi et al. 2018). This major event in the Milky Way's assembly history was predicted from the observed halo star counts by  Deason et al. (2013) , who argued that the "break" in the stellar halo density profile (at 20-30 kpc) signifies the apocenter of a massive accretion event. Our view of the (inner) halo is thus intricately linked with the properties of the GSE event. The large increase in stellar halo tracers provided by DESI in the inner region of the halo dominated by the GSE will be essential to quantifying the details of this accretion event and the properties of the progenitor dwarf galaxy.
Survey Requirements The scientific goals above impose common requirements on the sample of stars to be observed, and on the performance of DESI and its pipelines, particularly on the accuracy of key measurements (stellar parameters, radial velocities, and abundances) on the final data set. The rest of this paper presents the design of the survey and our first evaluation of our pipeline against those requirements. In the next section, we describe our target selection procedure and forecast the size and content of the full sample. We determine the completeness of each target class by applying the DESI fiber assignment algorithm, accounting for the prioritization of targets from BGS. For the bulk of our targets the completeness is ≈30% averaged over the footprint. Using a simple mock catalog based on an empirical model of the Galactic structure, we demonstrate that our target selection recovers tracers with sufficient density and distance coverage to address the core dark matter and stellar halo structure science goals.
In future work, we will apply our target selection to mock catalogs derived from ab initio cosmological simulations, to better understand the effects of halo-to-halo variation and substructures. We then introduce our analysis pipelines and use data from early DESI observations to demonstrate that we can recover radial velocities and [Fe/H] to the necessary precision of 1 km s -1 and ≈0. 2 dex, respectively. We identify systematic offsets with respect to other surveys and literature [Fe/H] measurements for globular clusters and dwarf galaxies. We also demonstrate that metal pollution signatures of planetary systems can be identified in DESI spectra of white dwarfs. We identify priorities for development of the current pipelines, which we expect to include [α/Fe] measurements, more robust fitting of very cool stars, and potential improvements to radial velocity accuracy below 1 km s -1.
As we describe in detail below, MAIN-BLUE (defined by the simple color criterion gr < 0. 7) will provide a highly complete sample of metal-poor main-sequence, turnoff, and bluer subgiant stars, with distances in the range 5-30 kpc and no kinematic selection bias. This will be a dense, high-fidelity data set with which to study the thick disk and inner halo over the likely extent of the GSE debris, as well as Sagittarius and many other known streams. We assign redder stars (gr > 0. 7) in the same 16 < r < 19 magnitude range to the MAIN-RED category if Gaia astrometry indicates they are more likely to be distant halo giants, and to the MAIN-BROAD category otherwise. We give MAIN-RED 56 These names refer to the mws_mask (and in some cases scnd_mask and desi_mask) bitmask fields described in  Myers et al.
(2023). They are used in the DESI data products to identify categories of targets selected according to specific criteria. targets the same fiber assignment priority as MAIN-BLUE targets. We give MAIN-BROAD targets lower priority in order to select more MAIN-RED giant candidates to probe the kinematics and substructure of the outer stellar halo, particularly to provide dynamical constraints on the dark matter halo mass over a large fraction of the virial radius. Of course, not all stars in MAIN-RED will be distant giants. The astrometric selection criteria are "mild": as described in more detail below, they accept relatively high contamination from nearby red main-sequence stars as a trade-off to minimize kinematic bias. Based on the current DESI bright-time observing plan, focal plane state, and fiber assignment strategy, we expect MAIN-RED spectra to account for ≈12% of the main sample and to have a completeness of ≈32% (similar to that of  MAIN-BLUE).
The fraction of true distant halo stars in MAIN-RED will be sensitive to the form of the stellar halo density profile at large distances; below, we present a fiducial model of the halo density, which predicts ∼5000-10,000 MAIN-RED giants beyond 50 kpc and ∼1000 beyond 100 kpc. Although MAIN-BROAD targets will have lower spectroscopic completeness than MAIN-RED (∼19%), those spectra will still account for ≈32% of the main MWS sample. They will serve both as a constraint on forward models of the MWS data set and as an important scientific sample in their own right, providing radial velocities for thin disk stars without Gaia RVS spectra, as well as more detailed abundance measurements. In addition to the main sample, MWS includes several highpriority samples of targets with high scientific value and low surface density (10 per square degree), comprising white dwarfs (MWS-WD, Section 3.
Their targeting and prioritization is independent of MWS; hence they may also be observed as part of the MWS main sample or as one of the high-priority MWS samples. Main MWS targets will be observed only once by design; they will only be reobserved if no unobserved MWS targets or targets from any other program are available to a fiber. However, stars may be observed as flux standards multiple times in both bright and dark conditions. We refer to the MWS main, high-priority, and faint samples collectively as the primary MWS target categories. All these targets are selected within the bright-time program footprint, shown in Figure  1. The following subsections give the specific target selection criteria for each category. Most criteria are based on optical photometry from Data Release 9 of the DESI Legacy Imaging Survey (LS DR9;  Zou et al.
2017; Dey et al. 2019; D. J. Schlegel et al. 2022, in preparation) , which also includes infrared photometry derived from WISE data  (Meisner et al. 2017) , and is matched to photometry from Gaia DR2 and astrometric measurements from Gaia EDR3 (Gaia Collaboration et al. 2021a). Table  2  lists the number of targets per category in the brighttime footprint (categories are not mutually exclusive). Approximately 30. 5 million unique LS sources meet the primary sample selection criteria. Section 5 describes the relative prioritization among the categories and the resulting fiber assignment efficiencies over four passes of the footprint, which are also listed in Table  2. The complete survey will contain ;7. 2 million spectra of unique stars from the primary MWS categories, of which ;6. 5 million are from the three main sample categories.
Main Sample Selection The MWS main sample is selected from the LS DR9 source catalog combined with astrometric measurements from Gaia EDR3. It is divided into three subcategories: MAIN = + mws_transmission_r. We do not put any requirements on the availability or quality of data in the LS z band. The MAIN-BLUE subsample is further defined by the color criterion gr < 0. 7 with g defined in the same way as r above. MAIN-BLUE is therefore an approximately magnitude-limited selection, comprised mainly of main-sequence turnoff stars and bluer subgiants. Stars redward of the gr = 0. 7 cut include redder main-sequence stars, subgiants, and giants. To improve the sampling of the more distant galaxy, higher priority is given to MAIN-RED targets, which have astrometric cuts designed to reduce nearby main-sequence contaminants.
However, Gaia astrometry becomes less precise near the faint limit of MWS, so the lower-priority MAIN-BROAD target category loosens these astrometric cuts. More specifically, the MAIN-RED subsample is defined by the following additional criteria: 1. gr 0. 7 2. astrometric_params_solved 31 3. Gaia parallax π < 3σ π + 0. 3 mas 4. Gaia proper motion f f 5 m a sy r r 19 1 | | m < - f 10 x x 22. 5 2. 5 ( ) ( ) = - The MAIN-BROAD sample comprises stars with color gr 0. 7 that satisfy the same magnitude and data quality requirements as the other two categories, but do not satisfy one or more of the astrometric criteria that define MAIN-RED (stars that do not have well-measured astrometric parameters in the Gaia catalog are therefore included in MAIN-BROAD). The parallax cut applied to MAIN-RED with respect to MAIN-BROAD aims to remove nearby red contaminants in order to recover a larger sample of distant halo giants.
g. ,  Bell et al. 2008; Deason et al. 2013 Deason et al. , 2014)). To make more accurate predictions for MWS, we modify the Galaxia model such that the logarithmic slope of the stellar halo density profile 63 changes from -2. 5 to -4 at a Galactocentric distance of 25 kpc. To turn the output of Galaxia into a mock MWS survey, we convert magnitudes from Galaxia to the LS photometric system via the PS1 color terms given on the LS website, and assign proper-motion and parallax errors using PyGaia. 64 We then apply exactly the same footprint and target selection functions (except for the LS data quality flags) to obtain mock MAIN-BLUE, MAIN-RED, and MAIN-BROAD samples. The upper panels of Figure  3  show two broad peaks in both the MWS target catalog (top right) and our Galaxia mock catalog (top left).
We thank the reviewer,  Matthias Steinmetz , for his thorough reading of our manuscript and constructive suggestions. A. P. C. and N. K. are supported by a  Taiwan Ministry of Education (MoE)   Yushan Fellowship  awarded to A. P. C. , and by  Taiwan National Science and Technology Council (NSTC)  grant  109-2112-M-007-011-MY3. This work used high-performance computing facilities operated by the  Center for Informatics and Computation in Astronomy (CICA) at National Tsing Hua University. This equipment was funded by  MoE ,  NSTC , and  National Tsing Hua University. C. A. P. acknowledges financial support from the  Spanish Ministry of Science and Innovation (MICINN)  projects  AYA2017-86389-P  and  PID2020-117493GB-I00. C. J. M. acknowledges financial support from  Imperial College London  through an  Imperial College Research Fellowship grant.
M. V. and L. B. e. S. acknowledge support from  NASA-ATP  award  80NSSC20K0509. The work of A. D. and J. N. is supported by  NOIRLab , which is managed by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation. T. S. L. acknowledges financial support from the  Natural Sciences and Engineering Research Council of Canada (NSERC)  through grant  RGPIN-2022-04794. B. T. G. was supported by grant  ST/T000406/1  from the  Science and Technology Facilities Council (STFC). This project has received funding from the  European Research Council  under the European Unionʼs  Horizon 2020 research and innovation program  (grant agreement No. 101020057 ). This research made use of computing time available on the high-performance computing system at the  Instituto de Astrofisica de Canarias.
Modern mass spectrometry-based metabolomics generates vast amounts of mass spectral data as part of the chemical inventory of biospecimens. Annotation of the resulting MS/MS spectra remains a challenging task that mostly relies on database interrogations, in silico prediction and interpretation of diagnostic fragmentation schemes and/or expert knowledge-based manual interpretations. A key limitation is additionally that these approaches typically leave a vast proportion of the (bio)chemical space unannotated. Here we report a deep neural network method to predict chemical structures solely from high-resolution MS/MS spectra. This novel approach initially relies on the encoding of SMILES strings from chemical structures using a continuous chemical descriptor space that had been previously implemented for molecule design.
The deep neural network was trained on 83,358 natural product-derived MS/MS spectra of the GNPS library and of the NIST HRMS database with addition of the calculated neutral losses for those spectra. After this training and parameter optimization phase, the deep neural network approach was then used to predict structures from MS/MS spectra not included in the training data-set. Our current version, implemented in the Python programming language, accurately predicted 7 structures from 744 validation structures and the following 14 structures had a Tanimoto similarity score above 0. 9 when compared to the true structure. It was also able to correctly identify two structures from the CASMI 2022 international contest. On average the Tanimoto similarity is of 0. 40 for data of the CASMI 2022 international contest and of 0.
, 2016) , CFM-ID  (Wang et al. , 2021) , MassFormer  (Young et al. , 2021)  or QCxMS  (Koopman and Grimme, 2021) , in order to mine chemical structure libraries. Substructural information on unknown molecules can further be retrieved up to a limited extent by programs such as MESSAR  (Liu et al. , 2020)  or MS2LDA  (Wandy et al. , 2018). Other approaches such as CSI:FingerID  (Dührkop et al. , 2015) , MIST  (Goldman et al. , 2022)  or DeepEI  (Ji et al. , 2020)  use the generation of fingerprints from spectra to retrieve annotations from chemical structural databases. In terms of MS/MS data classification, fingerprints and similarity metrics can be used to create molecular networks as pioneered by Global Natural Products Social molecular networking (GNPS)  (Wang et al. , 2016) , which may further give insights into main metabolic classes present within a dataset.
Metrics used for molecular networking are also central to retrieve annotations by database searches. Finally, it is now possible to retrieve hierarchicallyorganized class-based annotations which are based on the ClassyFire  (Djoumbou Feunang et al. , 2016)  chemical ontology with the use of the deep neural network classifier CANOPUS  (Dührkop et al. , 2021). The computational prediction of molecular structures solely from mass spectra has long been envisioned as a Holy Grail in mass spectrometry, with first attempts to use artificial intelligence dating back to the launch of the DENDRAL project in 1965  (Buchanan and Feigenbaum, 1978). A seemingly logical approach to structure prediction would be to calculate the molecular formula of a molecule using SIRIUS  (Dührkop et al. , 2019)  or BUDDY  (Xing et al.
(2019). MSNovelist uses the fingerprint representation and molecular formula obtained by SIRIUS to feed a recurrent neural network that will then predict a set of SMILES which are then further ranked by scoring their probability. Another recently published tool is MS2prop, which applies transformer like architectures to predict 10 chemical properties of unknowns with high accuracy  (Voronov et al. , 2022a). Despite having been reported as part of peer-reviewed articles, codes to Spec2mol and MS2prop are not available for the general public and publicly released MSNovelist relies on SIRIUS which makes it computationally demanding when processing large datasets because as it still relies on hand crafted heuristics and kernel functions. Finally, none of the available tools can predict a set of functional groups predicted to be present in a given molecule, even though it is known that direct structure prediction alone is prone to errors.
Methods Spectral libraries were downloaded (16. 12. 2022) from GNPS  (Wang et al. , 2016)    matchms (version 0. 11. 0) package  (Huber et al. , 2020)  and rdkit  (Landrum, 2010). Spectra from low-resolution mass spectrometers were excluded. Noise signals were discarded by reducing the number of peaks to 250 and corresponding neutral losses were calculated, resulting in a maximum of 500 peaks (see also Data and code availability). Only positive ion mode spectra with single charges were considered for training, also less frequent adducts were discarded first in pandas (https://pandas. pydata. org/) and then manually inspected and harmonized in OpenRefine (3. 5. 0). For validation, 744 unique spectra were randomly selected based on Inchikey and all Inchikey corresponding spectra were then discarded from the training dataset, resulting in a final training dataset of 83,358 spectra with 18 different adducts (Table  1 ).
SMILES were encoded with the cddd package  (Winter et al. , 2019) , which is based on a pretrained continuous chemical descriptor space. The number of 60 different functional groups was extracted using prebuilt rdkit functions and the number of sugars was identified by sugar removal utility  (Schaub et al. , 2020)  with the command -t "3" -remTerm "false". Atom counts from molecular formulas were extracted with the molmass package. These numbers were then scaled to floating numbers to encode the information for the neural network. The neural network architecture (Figure  1 ) has a total of 33 million parameters and is based on 5 standard transformer encoder layers (16 heads and 2048 units for feed forward) as described in  Vaswani et al. , (2017)  which are feeding into a temporal convolutional neural network (TCN)  (Bai et al.
, 2018)  with a receptive field of 883, a kernel size of 8 and 256 filters. This is followed by 2x2 dense layers that produce two outputs of shape 512 and 71. The architecture is implemented in tensorflow (version 2. 11. 0,  Abadi et al. , 2015)  and the TCN is implemented by the keras-tcn package  (Bai et al. , 2018). The training was performed on one Nvidia Tesla V100S GPU with 32 Gb RAM on the IBMP computing cluster. Training progress was logged with the package wandb (version 0. 13. 5). A hyperparameter search was performed with keras-tuner package  (Chollet and others, 2015)  in the random search mode for 99 trials, with one execution per trial and 4 epochs (see also Data and code availability). In addition, manual inspections were performed to find optimal parameters. The final training was stopped after 50 epochs, as the model performance did not significantly improve with longer training (Figure  S1 ).
96 Tanimoto similarity to the true structure and an average Tanimoto similarity of 0. 38). The average Tanimoto similarity achieved from the final model (with 2 outputs) on this dataset was of 0. 4, with two structures having a similarity higher than 0. 9 and 10 more than 0. 7. On average there were 51 true numbers of functional groups out of 60. The molecular formula estimation resulted into 8 true hits, whereas when the predicted SMILES were converted into molecular formulas, 7 true hits were retrieved. For 59 out of 236 molecules, the network was able to predict the true number of heteroatoms, which on average resulted into 7 out of 8 possible heteroatom numbers. From the 744 validation spectra, Mass2SMILES was able to predict 7 true structures (Table  4 ), followed by 14 predictions with Tanimoto scores above 0.
9, 62 with Tanimoto scores above 0. 7. The average Tanimoto similarity between predicted and true structures was of 0. 39. Moreover, the model was able to correctly predict the exact presence of functional groups for 17 spectra (Table  5 ), whereas on average the model predicted 54 true numbers of across the dataset (out of a maximum of 60) (Data and code availability: Supplemental Data S2). Interestingly, the model was able to correctly predict 436 adducts, for 11 molecules it found the true adduct and the molecular formula. For 22 molecules, it found the true molecular formula alone and for 187 the true number of heteroatoms. When converting the predicted SMILES into molecular formulas it retrieved 63 true hits out of 744. We then also examined Mass2SMILES on a metabolomics dataset acquired for 20 Nicotiana species  (Elser et al.
, 2022)  to better judge of its performance on a real use case study. For this, we first predicted all the structures across the whole dataset. On a Intel Xeon E5-2630 v2 @ 2. 6 GHz CPU, this took 9 hours and resulted in 16616 smiles out of 17902 spectra, most likely the spectra without predictions contained less than 6 peaks and were therefore automatically discarded by Mass2SMILES. In general, the model produces valid SMILES in most of the cases e. g. for the 744 validation spectra only 10 created errors with parsing by rdkit. For 457 features on the Nicotiana dataset, the molecular formula was identical with the one predicted by SIRIUS which is frequently is described as a gold standard method to perform this task. These features were then selected to predict the ClassyFire classes  (Djoumbou Feunang et al.
, 2016)  which were then compared to the ones predicted in parallel by CANOPUS  (Dührkop et al. , 2021)  directly from the MS/MS spectra. For 235 of these, the superclass was identical and for 176 the class prediction matched. When inspected with in further details, classes that had a mismatch were frequently very close and in a lot of cases the Mass2SMILES prediction was even closer to the actual true structure (Table  6 ). Interestingly, Mass2SMILES was able to annotate structures that did not retrieve database hits, even for very common molecules such as nicotine (Table  6 ). This shows that neural networks such as implemented as part of Mass2SMILES could possibly provide alternative solutions to computationally intensive database searches. We observed for several spectra very accurate annotations that did not yield any database hits but were predicted to belong to these classes e.
g. terpenoids, coumarins, flavonoids, O-acyl-glucoses or O-acyl-glycerols (Data and code availability: Supplemental Data S3 and Table  6 ). Some molecules did not yield a CANOPUS annotation but could nonetheless be accurately predicted with Mass2SMILES (Table  6 ). As an additional case study, we processed metabolomics data and predicted structures with Mass2 SMILES for MS/MS spectra collected from a dataset of 9 bryophyte analyzed and reported earlier by  Peters et al. (2018). This dataset had further been previously used to test the performance of the MSNovelist de novo structure elucidation tool  (Stravs et al. , 2022). Figure  3  shows a molecular network that comprises the MS/MS feature 377 for which a flavonoid-like structure had been initially predicted as part of the study reporting the performance of MSNovelist  (Stravs et al.