dataesr/scientific-paragraphs-categorization huggingface dataset:
While we ran zCluster on SDSS and KiDS photometry using the same set of spectral templates as used in  Hilton et al. (2018) , i. e. , the default templates from the EAZY photometric redshift code  (Brammer et al. 2008 ), supplemented by the  Coleman et al. (1980, CWW hereafter)  templates, we found it necessary to switch the spectral template set in order to optimize the performance when running on DECaLS photometry. We used a subset of the spectral templates used in the COSMOS survey  (Ilbert et al. 2009; Salvato et al. 2011) , representing a range of normal galaxies and AGNs, removing all elliptical templates based on  Bruzual & Charlot (2003)  stellar population synthesis models (as these were found to give biased results for individual galaxies at moderate redshifts; we speculate that this is probably related to the extrapolation of the stellar population synthesis models into the WISE bands), and adding in the CWW template set.
Other Redshift Sources We adopted a large number of redshifts used in the ACT DR5 cluster catalog from various sources in the literature. In particular, we used redshifts from previous published SZ surveys by ACT  (Menanteau et al. 2013a; Sifón et al. 2016; Hilton et al. 2018)   Gonzalez et al. 2019) , which contains more than 2000 high redshift (0. 7 < z < 1. 5) clusters selected from a survey area that covers most of the extragalactic sky. We collected a large number of redshifts using the NASA Extragalactic Database (NED 7 ). We took care to classify such redshifts as spectroscopic or photometric with appropriate uncertainties. References for these miscellaneous sources can be found in the notes for Table 2.
Therefore, we caution users of the ACT DR5 cluster catalog that the ỹ0 measurements reported here (and in turn the M UPP 500c masses) may be systematically underestimated by ≈ 5 -10%, if the ACT DR3 catalog is taken as "truth". This should be kept in mind when comparing these values against external catalogs. However, mass calibration against external datasets can still be used to absorb any systematic calibration error (as should be the case for the M Cal 500c mass estimates). 5. DISCUSSION.
The SZ Cluster Mass Scale Mass calibration of cluster samples is the key systematic that limits their ability to constrain cosmological parameters and is a topic of much debate in the literature (e. g. , von der  Linden et al. 2014; Hoekstra et al. 2015; Planck Collaboration et al. 2016a; Battaglia et al. 2016; Smith et al. 2016; Medezinski et al. 2018; Miyatake et al. 2019). Cluster mass estimation based on any kind of data is dependent upon a number of assumptions. Here we present a simple comparison of the mass estimates available in the ACT DR5 catalog with previous SZ surveys, and a compendium of weak-lensing mass estimates (CoMaLit; Sereno 2015), as an illustration of how they may be used. Future works based on the ACT DR5 catalog will investigate this topic in much more detail. In  Hilton et al.
Fig. 21  presents a similar comparison between ACT DR5 and the PSZ2 catalog  (Planck Collaboration et al. 2016b ). Since we adopted the same fiducial X-ray calibrated mass scaling relation from A10 as used in the PSZ2 catalog, we would expect the ACT DR5 clusters to follow the same mass scale. Here the comparison is made against ACT DR5 mass estimates that neglect the bias correction that accounts for the steepness of the cluster mass function, as such a correction is not applied to the masses reported in the PSZ2 catalog (see Section 2. 3 and the discussion in  Battaglia et al. 2016). We use a ; Section 4) with weak-lensing masses from the CoMaLit database  (Sereno 2015). Here we use the LC 2single catalog from CoMaLit, which consists of objects modeled using a single halo, and we restrict the selection to include only clusters with weak-lensing mass estimates with < 25% uncertainties.
The lines have the same meaning as in Fig. 20. 5 radius to cross-match the two catalogs, resulting in a sample of 327 clusters, if we include all objects down to the detection threshold of each survey. As shown in Fig. 21 , the scatter between the PSZ2 and ACT DR5 masses is large, but the mass scale is indeed similar: we find M 500c [PSZ2] = (0. 966 ± 0. 022) M Unc 500c from the 148 objects detected with signal-to-noise > 6 in both catalogs. As an independent check of the richness-based weaklensing mass calibration used in this work, Fig. 22  presents a comparison with a heterogeneous database of weak-lensing masses assembled from the literature  (Sereno 2015). Even though the comparison is made only with clusters that have < 25% uncertainty in the weak-lensing masses, the scatter is large.
Nevertheless, the overall mass scale is consistent with the richnessbased weak-lensing calibration derived from DES observations; M 500c [CoMaLit] = (1. 09 ± 0. 08) M Cal 500c. Future work will explore the mass calibration of the ACT DR5 sample using optical weak-lensing data from DES, HSC-SSP, and KiDS, as well as from gravitational lensing of the CMB. In this Section we briefly discuss a few notable categories of systems that may be of interest for future studies. This list is not meant to be comprehensive, and results for the most part from visual inspection of the cluster catalog using the available optical/IR data (see Section 3. 2). Further possible examples besides those mentioned here may be found by inspecting the notes and warnings columns of the cluster catalog (see Table  1 ).
ACKNOWLEDGMENTSACT was supported by the U. S. National Science Foundation through awards AST-0408698, AST-0965625, and AST-1440226 for the ACT project, as well as awards PHY-0355328, PHY-0855887 and PHY-1214379. Funding was also provided by Princeton University, the University of Pennsylvania, and a Canada Foundation for Innovation (CFI) award to UBC. ACT operates in the Parque Astronómico Atacama in northern Chile under the auspices of the Comisión Nacional de Investigación (CONICYT). The development of multichroic detectors and lenses was supported by NASA grants NNX13AE56G and NNX14AB58G. Detector research at NIST was supported by the NIST Innovations in Measurement Science program. CS acknowledges support from the Agencia Nacional de Investigación y Desarrollo through FONDECYT Iniciación grant no.
NEOWISE is funded by the National Aeronautics and Space Administration. The Legacy Surveys imaging of the DESI footprint is supported by the Director, Office of Science, Office of High Energy Physics of the U. S. Department of Energy under Contract No. DE-AC02-05CH1123, by the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility under the same contract; and by the U. S. National Science Foundation, Division of Astronomical Sciences under Contract No. AST-0950945 to NOAO. Software: AstroPy (Astropy Collaboration et al. 2013), Core Cosmology Library (Chisari et al. 2019) Pixell (https://github. com/simonsobs/pixell/).
The  Pan-STARRS1 Surveys  ( PS1 ) and the PS1 public science archive have been made possible through contributions by the  Institute for Astronomy , the  University of Hawaii , the Pan- STARRS  Project Office, the  Max Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg , and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins University, Durham University , the  University of Edinburgh , the  Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under grant No. NNX08AR22G  issued through the  Planetary Science Division of the NASA Science Mission Directorate , the  National Science Foundation  grant No.
The InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) mission landed on the surface of Mars on November 26, 2018. One of the scientific instruments in the payload that is essential to the mission is the SEIS package (Seismic Experiment for Interior Structure) which includes a very broadband and a short period seismometer. More than one year since the landing, SEIS continues to be fully operational and has been collecting an exceptional data set which contains not only the signals of seismic origins, but also noise and artifacts induced by the martian environment, the hardware on the ground that includes the seismic sensors, and the programmed operational activities of the lander. Many of these non-seismic signals will be unfamiliar to the scientific community.
In addition, many of these signals have signatures that may resemble seismic events either or both in time and frequency domains. Here, we report our observations of common non-seismic signals as seen during the first 478 sols of the SEIS data, i. e. from landing until the end of March 2020. This manuscript is intended to provide a guide to scientists who use the data recorded on SEIS, detailing the general attributes of the most commonly observed non-seismic features. It will help to clarify the characteristics of the seismic dataset for future research, and to avoid misinterpretations when searching for marsquakes.
Introduction InSight launched on May 5, 2018 and successfully landed on Mars on the 26th of November of the same year  (Banerdt et al. , 2020). SEIS is one of the crucial instrument packages in the payload, consisting of two three-component seismometers (one very broadband or VBB, and one short period or SP), a wind and thermal shield (WTS) to minimize the environmental effects on the seismic sensors, and an electronics box (E-Box) located on the lander for managing sensors and data acquisition (see Appendix A for a list of acronyms). The SEIS and E-box are connected through a tether, the cable used for powering SEIS and transmitting analog signals between the SEIS E-Box and sensor assembly. Detailed information about the seismic instruments and SEIS package is available in  Lognonné et al.
(2019). The deployment phase for SEIS started in early December 2018, shortly after the landing, and was completed in February 2019 after the sensors were placed on the planet's surface, the release of the tether and opening of the associated load shunt assembly (LSA), and the placement of the WTS. Fig. 1  shows a sketch of the InSight workspace with various mission components and SEIS sensor orientations. The SEIS package is located approximately to the south of the lander at a distance of ∼1. 8m from the closest lander foot, and ∼3. 6m from the furthest. The HP 3 (Heat flow and Physical Properties Package;  Spohn et al. (2018) ) probe is ∼1. 2m from SEIS. The primary purpose of the SEIS experiment is to record seismic signals including marsquakes that will provide constraints on the interior structure of Mars.
In fact, the content of the SEIS data is much richer and includes, similar to the Viking seismometer recordings before it  (Anderson et al. , 1977; Lorenz et al. , 2017) , other features such as the noise induced by atmospheric conditions, signatures of operational activities performed by the lander's robotic arm, or artifacts caused by the response of the sensors in the severe martian environment. Part of these non-seismic events (i. e. signals not associated with seismic sources such as marsquakes or impacts) have time and spectral domain signatures that resemble seismicity which may lead to misinterpretations, e. g. wind induced signals (Fig. 2 ). Many of the other observations would be unfamiliar to the scientific community with unusual sources that require clarification, Fig. 1. A sketch of the InSight workspace showing the relative positions of the SEIS and HP 3 with respect to the lander and geographic north, modified following  Stutzmann et al.
(2020). Details on how true North is determined on Mars are in  Savoie et al. (2020). The solar panels are colored blue on either side of the main lander body, and the lander feet positions are indicated. The inset zooms into the SEIS package, showing the position and orientations of each VBB and SP components. The dip angle for each of the components is shown in square brackets, followed by the azimuth angle from north. More accurate values for azimuth and dip angles of the sensors are available in the station metadata (InSight Mars SEIS Data Service, 2019a). Note that the position of the HP 3 is its initial location, and that the housing has subsequently been moved. The figure is not to scale. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.
) e. g. sensors' response to wide range of changes in atmospheric temperature. Here, we provide a guide for these type of non-seismic events, in order to aid more accurate analysis of the InSight data in future research. We primarily focus on the seismic dataset collected between landing and the end of March 2020 (Sol 478), but we also present data collected from other InSight instruments that may be used for recognizing signals from non-seismic origins. We start with an introduction on the InSight operations, data repository and how non-seismic events are processed. Then, we describe each feature in detail. The information we give here is complimentary to other studies on the martian background noise and environment. For additional details, we refer readers to  Stutzmann et al. (2020)  for the polarization analysis of long period (T > 1s) seismic noise of the VBB,  Charalambous et al.
Operations The SEIS data (InSight Mars SEIS Data Service, 2019a) from the InSight lander is first transmitted to SISMOC (SEIS on Mars Operations Center). The data is then transmitted to the mission support groups such as the Marsquake Service (MQS), after basic technical processing (e. g. time stamp corrections and file format conversions) in near real time. MQS is a ground segment support entity within InSight that is responsible for routine data monitoring for detecting seismic signals, locating quakes, and managing the seismicity catalog  (Clinton et al. , 2018 (Clinton et al. , , 2020)). MQS has developed a suite of single-station approaches to determine a distance and back-azimuth  (Panning et al. , 2015; Khan et al. , 2016; Böse et al. , 2017) , and magnitudes when it is possible to obtain a distance  (Böse et al.
, 2018). The majority of the methods currently employed by the MQS in operations have been vigorously tested prior to landing using synthetic waveforms and event catalogs  (Ceylan et al. , 2017; Clinton et al. , 2017; van Driel et al. , 2019). When a suspected marsquake signal is identified, MQS performs further checks to verify that the signal does not originate from environmental factors. To do this, MQS uses data from the APSS (Auxiliary Payload Sensor Suite;  Banfield et al. (2018 Banfield et al. ( , 2020)) ) located on the lander, an additional science package on InSight that includes one pressure sensor, two sensors for measuring wind direction and speed, and a magnetometer (MAG;  Johnson et al. (2020) ). Along with the APSS data, the activity logs of the lander are also checked to make sure the signal is not caused by known lander activity, for example robotic arm movements or lander communications with the Mars orbiters.
While searching for signals of seismic origin, the MQS team has become familiar with a multitude of non-seismic features and oddities in the waveforms, and at various stages has made efforts to systematically identify these signals. The team manually annotates these observations in order to ensure the completeness of the signal analysis and prevent false event identification. These efforts were most significant at the start of the project, when MQS was unfamiliar with the patterns in the martian dataset.
Data inventory The data available from InSight (InSight Mars SEIS Data Service, 2019a, 2019b) has varied significantly over the course of the project because of the complexity of InSight operations (in particular during the first 70 sols until commissioning was completed), the fluctuating available bandwidth, and demand for data retrieval from all active martian missions. There are major differences in the availability of the various instrumental packages (e. g. SEIS vs. APSS) and instrument types (e. g. VBB vs. SP). Additionally, there are changes in the operational modes of the seismometers (e. g. low vs high gain, and engineering vs science), as well as the sampling rates. InSight adopts an extended version of the SEED (Standard for the Exchange of Earthquake Data) naming conventions (IRIS, 2012), with a unique channel/location code combination for every possible data stream.
Overview of non-seismic events and an ordinary sol The list of non-seismic events we explore in this paper is provided in Table  2. In an effort to understand and characterise the dataset, the MQS team has been annotating many of these since Sol 70, once the WTS was placed over SEIS and the sensors began collecting high quality data. The procedures to identify all these features summarized in Table  2  varied over the time. For the initial months into the mission, a systematic attempt was made to document all glitches and pressure drops above a certain limit. The procedures were relaxed following conjunction, in part as a response to the increased rate of marsquake observations. From this time, only very large pressure drops were identified, and only glitches associated with seismic signals or suspected seismic signals were annotated.
e. seismic events do not excite the lander modes, while wind does not excite the 2. 4 Hz mode  (Giardini et al. , 2020). For future reference, we emphasize that when a suspicious signal is detected on SEIS recordings, it should also be checked against the common patterns we describe here, as well as the APSS channels and lander activity. Although we generally use the lander modes (e. g. Figs. 4  and 8 ) to discriminate between weather induced phenomena and seismic events, there are often cases for which the 2. 4 Hz excitation coincides with the lander modes' excitation  (Clinton et al. , 2020) , which is not surprising considering the prevalence of wind-associated signals in the dataset. The current MQS seismicity catalog does include many examples of seismic events with high frequency energy that occur during the quiet period of the sol and do not excite the lander modes.
8a ) repeated over many sols. As crosstalk injects on to each SP and VBB sensor, if the sensor outputs are mixed to produce the horizontal SP and all three VBB components, the crosstalk appears as a superposition, with a more complex form (Fig. 8b ). At lower frequencies, the crosstalk injection moves quickly across the bandwidth, and is generally less apparent though if the counts are changing more slowly it can be identified. Another type of transient signal observed on SEIS data is called as the whistling. The main distinction between whistling and crosstalk is that whistling is seen only on one sensor without any crosstalk between instruments or sensors. An example for VBB is shown in Fig. 8c , labeled as VBB1 whistling in Table  3. Fig. 10b  demonstrates another example or migrating spectral signals seen on the SP2 sensor that we term sandman, which is explained together with the sunset chirps in a separate section below due to their similarities in spectral shape and time of occurrence.
Other common signals Other signals that regularly appear in the SEIS data are those generated by the lander activities or sensor maintenance operations. It is generally clear that such events are artificial; however, without easy access to the lander information, it can easily to be confused by these signals when analysing an unfamiliar dataset from another planet. The InSight lander has a robotic arm with a camera mounted on it. The arm has been actively used for deployment, other activities that require interaction with the instruments on the ground, and imaging the surroundings. It has been used significantly beyond the original plan in the efforts to help the HP 3 mole as it struggles to bury itself under the Martian surface. An arm motion is typically characterised on SEIS by a series of relatively high frequency (> 4 Hz) and dispersive signals on both VBB and SP (Fig.
Conclusions We presented the general characteristics of commonly observed signals of non-seismic origins in the seismic data of InSight between the landing and Sol 478 (end of March 2020). The SEIS data is dominated by the artifacts from the atmospheric conditions that are more evident during the windy periods, but also may occur in the quiet periods. These artifacts have time and spectral domain properties that may resemble seismic signals. Any automated approach to detect signals of seismic origins may be affected by the signatures of these anomalies, and care must be taken in any analysis. Glitches and donks are particularly ubiquitous features and hence need to be handled with care when interpreting InSight seismic data.
Data availability The InSight seismic event catalog version 3 (InSight Marsquake Service, 2020) and waveform data (InSight Mars SEIS Data Service, 2019a) are available from the IPGP Datacenter and IRIS-DMC. Seismic waveforms are also available from NASA PDS (National Aeronautics and Space Administration Planetary Data System; https://pds. nasa. gov/). A sample data set of daily mini-seed files as well as example source codes for plotting, waveform processing, and LMST conversions.
Acknowledgements We acknowledge  NASA ,  CNES , their partner agencies and institutions (UKSA,  SSO ,  DLR ,  JPL ,  IPGP-CNRS, ETHZ, IC, MPS-MPG ) and the flight operations team at  JPL, SISMOC, MSDS, IRIS-DMC  and  PDS  for providing SEED SEIS data. We also acknowledge the funding by (1)  Swiss National Science Foundation  and  French Agence Nationale de la Recherche  ( SNF-ANR  project  "Seismology on Mars ",  ANR-14-CE36-0012-02  and  MAGIS ,  ANR-19-CE31-0008-08 ), (2) Swiss  State Secretariat for Education, Research and Innovation  ( SEFRI  project " MarsQuake Service-Preparatory Phase "), (3) ETH Research grant  ETH-06 17-02  and, for French co-authors, (4) the  French Space agency CNES. Additional support came from the  Swiss National Supercomputing Centre (CSCS)  under project ID s922.
However, a SBML file containing all reactions involved in detailed non-metabolic processes (such as the transcription of all genes nucleotides per nucleotide, the translation of all mRNAs codon per codon as in  4  ) would contain several gigabytes of information with at least 4 × 10 6 reactions for Escherichia coli (more than 4000 genes with average length 1 kb)  24. Generating, storing and loading such a file in any software is unrealistic. Such combinatorial complexity typically arises in non-metabolic processes because reactants can adopt multiple states, such as the phosphorylation of protein residues. If each state is modeled as a separate chemical species, biochemical reactions must be duplicated, as each reaction only applies to one state. Rule-based languages  25, 26  such as BNGL  27  , Kappa  28  , or rxncon  29  , circumvent this problem by defining rules that apply to multiple species (including species involved in a complex) or states that match a pre-defined pattern.
These languages allow to define complex regulatory events or transduction processes with a minimal set of rules. Another alternative to reduce the number of reactions are meta-languages such as Antimony  30  and PySB  31  , which define reaction modules that can later be imported or expanded into an SBML or a rule-based model. However, for simulation, rule-based models must be converted into standardized reactions. In network-based approaches, rules are used to exhaustively enumerate possible reactions and reactants  32. For example, in the Fcǫ RI signaling model  33  , 24 rules become 58,276 reactions  34. These reactions can then be stored in a standardized format, such as SBML, or a solver-specific format, such as CPS for COPASI. In network-free approaches  28, [35] [36] [37]  , reactants are represented as individual particles; the matching of rules with particles allows to determine which reactions can be performed dynamically.