dataesr/scientific-paragraphs-categorization huggingface dataset:
We explore the advantages of DECaLS, and exhibit the measurements of shear ratio and cosmic magnification as two promising tools in using the great constraining power of DECaLS ×DESI. Additionally, to achieve the expected precision, we propose requirements on the DECaLS data, in terms of the shear calibration and the redshift distribution calibration. This work is organized as follows. In Section 2 , we briefly introduce the observables and their theoretical predictions. In Section 3 , we describe the DESI, DECaLS, KiDS, and HSC data we use. In Section 4 , we show the g-g lensing measurements for different DESI density tracers and different lensing surv e ys, and the measurements of shear ratio and cosmic magnification. We summarize our findings from DESI ×DECaLS for the 1 per cent surv e y in Section 5.
KiDS The Kilo-Degree Survey (KiDS) is run by the European Southern Observatory and is designed for weak lensing studies in ugri optical bands. The KiDS data are processed by THELI  (Erben et al. 2013  ) and ASTRO-WISE  (Begeman et al. 2013 ; de Jong et al. 2015 ). The galaxy shear measurements are obtained by LENSFIT  (Miller et al. 2013 ; Fenech Conti et al. 2017 ) , and the photo-zs are measured by BPZ  (Benitez 2000 ; Ben ítez et al. 2004  ) using the KiDS ugri optical bands and the ZYJHK s infrared bands from VIKING  (Wright et al. 2019 ). The KiDS shears are calibrated following the same equation as equation (  10 ) with image simulation  (Kannawadi et al. 2019 ). We use the KiDS-1000 shear catalogue  (Asgari et al. 2021 ; Giblin et al. 2021 ).
The (small scale, large scale) detection significance are ( -0. 3, 1. 4) for ELG ×DECaLS, ( -1. 1, 1. 4) for ELG ×KiDS, and (2. 5, 2. 6) for ELG ×HSC. The ne gativ e values at small scale represent ne gativ e measurements, which might be due to the non-linear galaxy bias, satellite fraction, or shot noise. to the ∼11 σ of KiDS-DR4 and ∼17 σ of HSC-Y3. Therefore, we emphasize the importance of calibrating DECaLS data in a more precise way in the future for reliable cosmological measurements. We note the current measurements with DESI 1 per cent surv e y have S/N 20 σ , therefore the impacts from such biases are still within the error budget. The assumed systematics can enlarge the large(small)-scale uncertainties from ∼17 per cent ( ∼10 per cent ) to ∼18 per cent ( ∼12 per cent ). We further estimate the requirements on the DECaLS calibrations for precision cosmology.
8. We note that using tomography and combining g-g lensing measurements from different density tracers (BGS, LRGs, ELGs, and possibly QSOs in the future) can bring stronger S/N, so the requirements on the calibration terms will be stricter. Ho we ver, these studies will require a much larger covariance, thus more jackknife subregions and much larger overlapped regions, which are beyond Table  1. Forecast the future statistical power (upper table) and calibration targets for the systematics (lower table) for DESI g-g lensing. We summarize the S/N of the DESI 1 per cent surv e y (SV3) g-g lensing results in Figs 2 , 5 , and 7 , and forecast the ideal final S/N with full DESI, by rescaling the covariance based on the o v erlapped area, assuming DECaLS data can be well calibrated. We note that the ELG measurements become ne gativ e sometimes, and therefore decide not to predict its final S/N.
From this table, we see that the advantage of DECaLS is at lowz (with BGS) and large scale. In the lower part of the table, we focus on the forecast for the large-scale results (for cosmology) with DESI BGS (as the y hav e the highest S/N), and additionally present the possible bias in the forecasted S/N, namely S/N. It includes the contribution from the statistical error of the current measurement, and residual systematical bias from the data calibration. The statistical contribution of S/N results from rescaling the 1 σ error from Figs 2 , 5 , and 7 , and is scale independent and redshift independent. The contribution from multiplicative bias m is also scale independent, while the contribution from redshift bias z is weakly scale dependent and redshift dependent. We use multiplicative bias | m | ∼ 0.
Additionally, we find that by including shear galaxies from 0 < z p < 0. 4, the significance of magnification detection drops, due to the lowz data having much weaker lensing efficiency as in equation (  2 ), and is mainly contributing noise. The fitting goodness of the reduced χ 2 (defined by the χ 2 between the best fit and the data, divided by the degrees of freedom) is generally close to ∼1 for each case. This shows no significant deviation between the model and the data. The detected ∼3 σ positive signal can be either due to the cosmic magnification, or very similar stochastic photoz outliers between the three lensing surv e ys. As DECaLS, KiDS, and HSC have totally different photometric bands, photoz algorithms, and training samples, we think the detected signals are less likely due to the similar photoz outliers, and more likely to be the cosmic magnification signal.
C O N C L U S I O N S In this work, we study the cross-correlations between DESI 1 per cent surv e y galaxies and shear measured from DECaLS, one of the imaging surv e ys for DESI target selection. For the 1 per cent DESI data, DECaLS can have comparable performances compared with the main Stage III lensing surv e ys KiDS and HSC. More specifically, we measure the cross-correlations of DESI BGS/LRGs/ELGs ×different shear catalogue, shown in Figs  2 , 5 ,  and 7. We forecast the level of significance with full DESI data in Table  1. Assuming systematic errors can be cleaned with high precision in the future, we find the large-scale S/N could reach > 50 σ for DECaLS ×BGS, > 15 σ for DECaLS ×LRG, and > 10 σ for DECaLS ×ELG, which are very promising before the Stage IV surv e ys come out. We point out that the main difficulty in obtaining DECaLS cosmology is the calibrations for the systematics.
In order to safely use the large-scale ∼50 σ information of BGS ×DECaLS, we need to achieve the minimum requirements on (1) the multiplicative bias of | m | < 0. 006 and (2) the mean of redshift distribution | z| < 0. 008, when requiring the introduced bias follows < 0. 31 σ. To safely use the full-scale ∼100 σ data, the requirement is a factor of 2 stronger. The requirement could be even higher when combining different observables, but it will require a larger footprint than the 1 per cent surv e y for the study. These requirements are essential guides for future calibrations and studies on cosmology. To fully use the advantage of DECaLS, we further explored two promising observables, the shear ratio and the cosmic magnification. We show the current 1 per cent BGS data can constrain shear ratio with σ R ∼ 0.
AC K N OW L E D G E M E N T S We thank  Xiangkun Liu ,  Weiwei Xu , and  Jun Zhang  for their helpful discussions. We thank  Chris Blake ,  Daniel Gruen , and  Benjamin Joachimi  for their contribution during the DESI Collaboration-wide re vie w. This work is supported by  National Key R&D Program of China  no. 2022YFF0503403. HS acknowledges the support from  NSFC of China  under grant no. 11973070 , the  Shanghai Committee of Science and Technology  grant no. 19ZR1466600 , and  Key Research Program of Frontier Science, Chinese Academy of Sciences , grant no. ZDBS-LY-7013. PZ acknowledges the support of  NSFC  grant no. 11621303  and the  National Key R&D Program of China  no. 2020YFC22016. JY acknowledges the support from  NSFC  grant no. 12203084 , the  China Postdoctoral Science Foundation  grant no.
2021T140451 , and the  Shanghai Post-doctoral Excellence Program  grant no. 2021419. We acknowledge the support from the science research grants from the  China Manned Space  Project with nos  CMS-CSST-2021-A01 ,  CMS-CSST-2021-A02 , and  CMS-CSST-2021-B01. We acknowledge the usage of the following packages: PY-CCL , 5 TREECORR , 6 HEALPY , 7 MATPLOTLIB , 8 EMCEE , 9 CORNER , 10 AS-TR OPY , 11 PAND AS , 12 SCIPY , 13 and DSIGMA 14 for their accurate and fast performance and all their contributed authors. This research is supported by the  Director, Office of Science, Office of High Energy Physics of the U. S. Department of Energy  under contract no. DE-AC02-05CH11231 , and by the  National Energy Research Scientific Computing Center , a  DOE Office of Science User Facility  under the same contract; additional support for DESI is provided by the  U.
DECaLS, BASS, and MzLS together include data obtained, respectively, at the  Blanco Telescope ,  Cerro Tololo Inter-American Observatory ,  NSF's NOIRLab ; the  Bok Telescope, Steward Observatory ,  University of Arizona , and the  Mayall Telescope ,  Kitt Peak National Observatory, NOIRLab. NOIRLab is operated by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation. Pipeline processing and analyses of the data were supported by  NOIRLab  and the  Lawrence Berkeley National Laboratory. Le gac y Surv e ys also use 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.
Coseismic Surface Displacement and Strain We quantify coseismic surface displacement for the Maduo earthquake by sub-pixel correlation of pre-event and post-event SPOT-6/7 satellite images (image information shown in Table  S1  in Supporting Information S1). These images are orthorectified in the COSI-Corr software using camera metadata for viewing geometry correlation and a 5-m ALOS (Advanced Land Observing Satellite) digital elevation model for removal of the topographic distortion  (Ayoub et al. , 2009; Leprince et al. , 2007) , and then are co-registered by tying common features between the pre-and post-event images. Sub-pixel correlation of these corrected images is then performed using the MicMac correlator, using a sliding window of 3 × 3 pixels (4. 5 m × 4. 5 m), a step of 1 pixel (1.
Acknowledgments This study is supported by  Institute of Geology, China Earthquake Administration  ( IGCEA2138 ), by the  National Natural Science Foundation of China  ( U2139202 ), and by the  Second Tibetan Plateau Scientific Expedition and Research Program (STEP)  ( 2019QZKK0901 ). We thank  S. Liang ,  J. Chen ,  Y. Zhou , and  S. Wei  for fruitful discussions. Critical and thorough reviews of the manuscript by  C. Scott , one anonymous reviewer, the Associate Editor  A. Fagereng , and the Editor  G. Prieto  significantly improved the current version.
They are obtained in azimuthal moments in combinations of the azimuthal angles of the hadron transverse momentum and transverse spin of the nucleon relative to the lepton scattering plane. In order to extract asymmetries, the initially unpolarized MonteCarlo was re-weighted in the true kinematic variables, hadron types and parton flavors based on global fits of fixed target SIDIS experiments and e + e -annihilation data. The expected statistical precision of such measurements is extrapolated to 10 fb -1 and potential systematic uncertainties are approximated given the deviations between true and reconstructed yields. Similar neutron information is obtained by comparing the ECCE e+p pseudo-data with the same from the EIC Yellow Report and scaling the corresponding Yellow Report e+ 3 He pseudo-data uncertainties accordingly.
These two effects are related to the corresponding transverse momentum depdendent distribution and fragmentation functions (TMDs) which give access to the three-dimensional momentum structure of the nucleon and provide some of the main information to spin-orbit effects of the nucleon. The Sivers function describes the correlation between the transverse spin of the nucleon and the transverse momentum of a parton within it. With the expected statistics and energy range of the EIC, the precise transverse and longitudinal momentum dependent distributions of not only valence but also sea quarks and gluons can be extracted. At present only up and down quark Sivers functions are known in the valence region but with rather large uncertainties, particularly in the transverse momentum. Closely related to it is the scale dependence of the corresponding single spin asymmetries that is at present poorly known due to the fact that only fixed-target experimental data is available with very similar hard scales.
Data selection The simulated data were obtained using the pythiaeRHIC  [21]  implementation of pythia6  [22]  with the same settings and events that were also used in the SIDIS studies of the EIC Yellow report  [23]. It should be noted that for these studies no dedicated radiative effects were generated other than what is already included in pythia. These effects are likely very relevant, especially at large y but are common to all EIC detector proposals and were therefore not studied here. The generated data, in its eic-smear  [24]  format, was then run through a geant4 simulation of ECCE that contains all the relevant tracking detectors and calorimeters, as well as some of the support material, magnet yoke, the PID detectors, etc. , c. f. [25]. The PID information in these simulations came from a parametrization based on the rapidity and momentum dependent PID resolutions that can be expected for the various PID subsystems.
The data was obtained at the energy combinations that are summarized in Table  1  where the simulations were separated into low Q 2 data and higher Q 2 data in order to still obtain reasonable statistics at the lower cross sections at higher Q 2. Unlike in the Yellow Report, no dedicated e+ 3 He simulations were run and instead for the impact studies the Yellow Report uncertainties were rescaled based on the ECCE e+p simulations. As can be seen from these luminosities, especially at low Q 2 the accumulated data is still far below the level of statistics to be expected from the EIC. Nevertheless the statistics are large enough to evaluate the statistical uncertainties that can be expected. At the higher Q 2 > 100 GeV 2 range, the luminosities are generally larger which in turn compensates for the lower cross sections and event rates expected there.
However, such an unfolding would require substantially more, and preferably independent, MC simulations as well as a much more detailed description of all detector components, etc. Even in an unfolding procedure, if the discrepancies between true and reconstructed asymmetries get large (i. e. larger off-diagonal elements in the smearing matrix), also the uncertainties in the unfolding will increase and therefore taking at this point these differences as uncertainties is a prudent, albeit very conservative approach. Not shown in the figures or data tables that are provided to the theorists for impact evaluations is an additional global 3% relative systematic uncertainty related to the precision of extracting the beam polarization in the EIC. An example of the expected uncertainties in three select x and Q 2 bins are shown in Figs.
It shows that apart from slight differences due to the ranges assumed for the particle identification and the amount of actual detector smearing, the uncertainties are quite comparable. This again highlights that the ECCE detector concept fulfills the requirements set for the reference detector in the Yellow Report using realistic simulations of detectors, materials and support structure. It should be noted that any impact figure relies on the baseline parameterization of that particular group of global fitters and the assumptions that group has used within their global fits. The expected uncertainties from other groups will look different, particularly in regions of low-x where so far no single spin asymmetry data exists and most of the uncertainty bands originate in the functional form, positivity bounds and other constraints.
The question is not so much about which of these fits is right as much as the fact that a similar increase in the precision with EIC or ECCE pseudo-data is expected. As such, the impact studies presented here are only one representative of the large number of groups working on global extractions for the Sivers functions  [32, 33, 34, 35, 27, 36, 37, 38, 39, 40, 41, 42, 43].
Collins-function-based transversity measurements: For the quark transversity distribution and the related tensor charges also the impact studies of the Yellow Report  [23, 20]  were revisited using the ECCE pseudo-data at the same ener-gies as in the Yellow Report, following the methodology of the global fit from  [18]. The resulting transversity distributions and tensor charges can be seen in Fig. 22. Again, one can see that the impact of the EIC data significantly reduced the uncertainties on the charges. Particularly the inclusion of the 3 He data improves the so far poorly known down quark transversity distribution and tensor charge. The tensor charges, as well as their iso-vector combination g T can be precisely evaluated thanks to the large range in x to reduce possible uncertainties due to the extrapolation to lower x that currently is one of the main uncertainties in the extractions.
One can also see that the impact of the ECCE pseudodata is comparable to the impact studies that were performed using the parametrized reference detector of the Yellow Report. As such, it is again shown that ECCE fulfills the detector requirements to successfully obtain these physics goals. Again for this impact study, the expected impact is based on the assumptions, functional form, etc of this particular global fit, but the impact should be comparable for other transversity extractions as well. The small differences between Yellow Report and ECCE impact values arise from the different pseudo-data sets resulting is slightly different central values. Most importantly, the expected uncertainties on these tensor charges are comparable with the lattice QCD simulations  [44, 45] , and therefore will allow a quantitative comparison of experimental and lattice results, where one can explore the potential impacts in the case of discrepancies  [19, 46].
Acknowledgements We thank the  EIC Silicon Consortium  for cost estimate methodologies concerning silicon tracking systems, technical discussions, and comments. We acknowledge the important prior work of projects eRD16, eRD18, and eRD25 concerning research and development of MAPS silicon tracking technologies. We thank the  EIC LGAD Consortium  for technical discussions and acknowledge the prior work of project  eRD112. We thank (list of individuals who are not coauthors) for their useful discussions, advice, and comments. We acknowledge support from the  Office of Nuclear Physics in the Office of Science in the Department of Energy , the  National Science Foundation , and the  Los Alamos National Laboratory Laboratory Directed Research and Development (LDRD)   20200022DR. This work was also partially supported by the  National Science Foundation  under grant No.
Overall followed approach The general approach followed to integrate IAQ impacts to building LCA is presented in Fig. 1. The different parts of the method (for a given ventilation rate) are shown with main in uential factors at each step and the method is described with fuller details in the next sections. As an example, the methodology is applied using existing software: Pleiades modeller allowing to create a building model, Pleiades STD to perform dynamic thermal simulation (Peuportier and Blanc Sommereux 1990), Pleiades LCA for life cycle assessment  (Polster et al. 1996) , and INCA Indoor for IAQ calculations  (Mendez et al. , 2015). We model the building under study using Pleiades Modeller (IZUBA ÉNERGIES 2001a) which allows to de ne building characteristics: design, layout, materials, mechanical ventilation rates, openings (windows and doors), location and occupancy.
Heating needs are calculated with Pleiades STD (Peuportier and Blanc-Sommereux 1990) based on outdoor climate, thermal properties of building materials and use characteristics (e. g. occupancy, internal heat gains, temperature set point). For the building's LCA, we use Pleiades LCA  (Polster et al. 1996; Thiers and Peuportier 2012) , considering all stages of its life cycle: construction, operation (heat and electricity consumption based on calculated heating needs and hot/cold water consumption based user inputs), renovation and deconstruction. The LCA database ecoinvent v3. 4 cutoff (Frischknecht et al. , 2004) is used. Material VOC emissions are calculated considering a material age of one year, after which emissions are stabilised (see Supporting information 2 section S. 3. ). Activity VOC and PM 2.
x M intake,path,x T M intake,path,x = V room ∫ XF path × C in,x dt t=0 V room x C in,x XF 2. 3. 2. VOC effect factors Effect factors EF (µDALY/µg intake ) are calculated based on ED 10 or ED 50 (the lifetime doses per person that causes a disease probability of 10% and 50% after intake) obtained from USEtox (Fantke et al. 2017) and the ToxVal database (US EPA and Richard 2018), for cancer diseases, reproductive/developmental non-cancer diseases and general non-cancer diseases. 2. 3. 3. PM 2. 5 effect factors - - M i,r (RR i ( C in,PM 2. 5 + ΔC in,PM 2. 5 ) -RR i ( C in,PM 2. 5 )) × × SF i,r - RR i ( EF ( C in,PM 2. 5 ) == The effect factor (EF) is based on the Global Burden of Disease (GBD) integrated exposure-response (IER) model, adapted from Fantke et al. (2019) by Bhoonah et al. (2023b) for indoor contexts, as given in Eq.
Mass spectrometry-based proteomics analysis of pull-down assays The pull-down samples were treated on-beads by trypsin/LysC (Promega). The resulting peptides were loaded and separated on a C18 columns for online liquid chromatography performed with an RSLCnano system (Ultimate 3000, Thermo Scientific) coupled to an Orbitrap Fusion Tribrid mass spectrometer (Thermo Scientific). Maximum allowed mass deviation was set to 10 ppm for monoisotopic precursor ions and 0. 6 Da for MS/MS peaks. The resulting files were further processed using myProMS v3. 9. 3 (https://github. com/bioinfopf-curie/myproms;  Poullet et al. , 2007 [2] ). False-discovery rate (FDR) was calculated using Percolator  [3]  and was set to 1% at the peptide level for the whole study. Label-free quantification was performed using peptide extracted ion chromatograms (XICs), computed with MassChroQ [4] v.
Acknowledgements We thank  Thaleia Papadopoulou ,  Amandine Molliex  and  Navarro Pablo  for providing mouse Embryonic Stem Cells (mESCs) extracts, and for fruitful discussions. We thank  Nadia Izadi-Pruneyre  for providing the bench space necessary to carry out the pull-down experiments with fresh mESCs extracts. We thank the  IR-RMN  (now Infranalytics), notably  Nelly Morellet ,  François Giraud  and  Ewen Lescop , for their reactivity, their support, and their long-standing, efficient care of the 950 MHz spectrometer. We thank  Marie Sorin ,  Baptiste Nguyen  and  Benjamin Bacri , who contributed to this study during their Master1-Master2 internships.
Fundings This work was supported by the  CNRS  and the  CEA-Saclay , by the  French Infrastructure for Integrated Structural Biology  (https://frisbi. eu/, grant number  ANR-10-INSB-05-01 ,  Acronym FRISBI ) and by the  French National Research Agency (ANR ; research grants  ANR-14-ACHN-0015  and  ANR-20-CE92-0013 ). Financial support from the  IR INFRANALYTICS   FR2054  for conducting the research is also gratefully acknowledged. This work was also supported by grants from the  "Région Ile-de-France"  and  Fondation pour la Recherche Médicale  (D. L. and LSMP).
Acknowledgements We thank  ESA  for the opportunities to contribute to the SciSpace White Papers, and to write this review which has evolved out of our contribution to the White Papers 164. This work was supported in part by grants  MecaTiss  ( ANR-17-CE30-0007 ) and  EpiMorph  ( ANR-2018-CE13-0008 ) from the  Agence Nationale de la Recherche  (M. B. A. ),  MIUR-PRIN  grant  2020F3NCPX  "Mathematics for industry  4. 0  (Math4I4)" (P. C. ) and the  Max Planck Society  (P. A. H. ).