dataesr/scientific-paragraphs-categorization huggingface dataset:
ALMA ALMA Band 7 observations were carried out on S5-z17-1 on 2022 September 16 as a Cycle 8 DDT program (#2021. A. 00031. S, PI: S. Fujimoto). The requested continuum sensitivity was achieved via three frequency setups ranging nearly 24 GHz wide over ∼334-358 GHz to maximize a chance of the [C II] line detection at z = 4. 31-4. 69 (red shaded region in the right panel of Figure  1 ), which covers around the peak of the redshift probability distribution P(z) corresponding to the lower-redshift solution for S5-z17-1 due to a lower-z red galaxy with strong emission lines (Section 3. 1). Each tuning was observed for 16 minutes, resulting in a total of 48 minutes including calibrations and overheads. The ALMA data were reduced and calibrated with the Common Astronomy Software Applications package version 6.
4. 1. 12 (CASA; THE CASA TEAM et al. 2022) with the pipeline script in the standard manner. We imaged the calibrated visibilities with natural weighting, and a pixel scale times than the best-fit high-z SED, expecting a ∼10σ detection from the ALMA Band 7 observation (gray curve in Figure  3 ). The images on this panel present 2″ × 2″ NIRCam cutout images of S5-z17-1. Middle: P(z) from the SED fitting by EAZY (brown curve) and CIGALE (light blue curve) with a redshift range at 0 < z < 25. Right: same as the middle panel, but at 0 < z < 10. The gray dashed line denotes the atmospheric transmission for [C II]. The red shade indicates the [C II] redshift range of z = 4. 31-4. 69 covered by our ALMA Band 7 observations spanning 334-358 GHz with three frequency tunings, which is optimized to maximally cover the peak of the lower-redshift solution's P(z) and avoid the significantly low atmospheric transmission.
16. One note is that such a high stellar-tohalo-mass ratio implies a significantly high star formation efficiency. We further discuss the validity of the high-z solution in Section 4. 1. Based on these results, both interpretations are possible, and it is challenging to conclude which is more likely with the current data sets. Once the line feature is confirmed, the low-z solution at z = 4. 61 will be verified with the JWST/NIRSpec follow-up by targeting the strong rest-frame optical emission lines that cause the dropout feature between the F200W and F277W filters. In fact, this is the case of another extremely high-z galaxy candidate, initially estimated at z ∼ 17 (e. g. ,  Donnan et al. 2023) , which has been subsequently spectroscopically confirmed to be at z = 4. 91  (Arrabal Haro et al.
2005; Noll et al. 2009; Boquien et al. 2019). We adopt the same assumptions in the fitting described in Section 3. 1. We use the public grizli catalog for GHZ1/GLz11 and GHZ2/ GLz13, where the JWST data reduction, calibration, and photometry are processed in the same manner as S5-z17-1 (Section 2. 1). We also use the photometry of the HST/ACS-WFC3 images in the catalog, including the latest ACS data taken as part of a DDT program (#17231, PI: T. Treu), which is processed using the grizli pipeline in the same manner as  Kokorev et al. (2022). We list the JWST and HST photometry of GHZ1/GLz11 and GHZ2/GLz13 in Appendix A. The optical-NIR photometry of HD1 is taken from  Harikane et al. (2022). The ALMA photometry measurements of GHZ1/ GLz11, GHZ2/GLz13, and HD1 are taken from the previous studies  (Bakx et al.
Summary In this paper, we present the ALMA Band 7 observations of a remarkably bright and high-redshift galaxy candidate S5-z17-1 (M UV = -21. 6 at z phot ∼ 17) with a robust NIRCam/F200W dropout feature identified in JWST ERO data of Stephan's Quintet. The number of UV-bright high-z candidates at z > 9 exceeds most pre-JWST predictions, remarking on the importance of testing lower-z contaminants, especially from populations with a red continuum and strong emission lines, which can produce similar dropout features of high-z galaxies in the NIRCam filters. In conjunction with the other three UVbright z  11 candidates recently observed ALMA, we systematically conduct the SED analysis over the optical-to-millimeter wavelengths and discuss their physical properties in their highz solutions and remaining low-z possibilities for each candidate.
Note. a Difference of the χ 2 value from the best-fit high-z galaxy solution at z ∼ 11-17 summarized in Table  4 , suggesting that high-z solution is still favored in every candidate. 54 The difference in the PSF shape has been reported between the JWST software tool webbpsf and the empirical approach using stars observed in the NIRCam field of view (e. g. ,  Ding et al. 2023; Ono et al. 2023 ). 2. We do not detect dust continuum at 866 μm from S5-z17-1, placing the 2σ upper limit at 90. 0 μJy. We adopt a spectral dust index of 2. 0 and dust temperature of T d = 90 K by extrapolating the T d -z evolution model  (Sommovigo et al. 2022 ) to z = 18, which is consistent of the lower limit of T d > 80 K obtained from the radiative equilibrium model  (Inoue et al. 2020; Fudamoto et al. 2022)  based on a clumpy ISM assumption and a very compact effective radius of r e ∼ 140 pc measured in  Ono et al.
Acknowledgments We thank the anonymous referee for providing constructive comments and suggestions with careful review. We are grateful to  Yuichi Harikane  for sharing the latest photometry of S5-z17-1, helping with the comparison of the photometry, and providing useful comments on the paper. We thank  Rohan Naidu ,  Paola Santini ,  Marco Castellano ,  Masafusa Onoue ,  Andrea Ferrara ,  Laura Sommovigo ,  Vasily Kokorev ,  Fengwu Sun ,  Romain Meyer ,  Leindert Boogaard , and  Rui Marques-Chaves  for helpful comments and discussions. This paper makes use of the ALMA data: ADS/JAO. ALMA #2021. A. 00031. S. ALMA is a partnership of the  ESO  (representing its member states),  NSF (USA) , and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI ( Republic of Korea) , in cooperation with the  Republic of Chile.
(Face), we compared the brain-evoked potentials of familiar and scrambled faces presented to participants who performed a face symmetry judgment task designed to maintain their attention  11. In the third experiment (Oddball), participants had to respond to oddball sounds (70 ms of 1000 Hz) interspaced by frequent standard sounds (70 ms at 500 Hz)  12  (see "Methods" section). We high-pass filtered the data at 0. 5 Hz, extracted data epochs (from -1 to 2 s) with no baseline removal, and calculated at each latency the average number of significant channels using random resamples of 50 epochs (see "Methods" section). We then searched for the 100-ms window with the most significance. We found the latency of maximum effect size in each dataset differed (Fig. 1 ): Go/No-go (350-450 ms), Oddball (400-500 ms), and Face (250-350 ms).
This range, custom for each dataset, was used to assess the efficacy of different signal preprocessing methods. Preprocessing methods. High-pass filtering. Is there an ideal frequency cutoff to high-pass filter EEG data? Linear filter length increases as the cutoff frequency decreases, making it impractical to filter EEG at frequencies below 0. 5 Hz. As a result, we used a 4th-order Butterworth filter to assess the optimal high-pass filter cutoff frequency (ERPLAB package; see "Methods" section). We tested filters at 0. 01 Hz, 0. 1 Hz, 0. 25 Hz, 0. 5 Hz, 0. 75 Hz, and 1 Hz. Filtering had the most important effect on the percentage of significant channels compared to all other preprocessing steps. It improved performance by 13% (Face), 47% (Oddball), and 57% (Go/No-go). The best filtering was at 0.
1 Hz (Face), 0. 5 Hz (Oddball), and 0. 75 Hz (Go/No-go). Filters above 0. 1 Hz led to significant improvement compared to no filtering for the Visual Go/No-go and Auditory Oddball datasets (p < 0. 0001), and also led to significant improvement compared to the 0. 01-Hz filter (p < 0. 0001). Although there was a significant difference between a filter at 0. 01 Hz and one at 0. 5 Hz for two datasets (Go/No-go and Oddball; p < 0. 0001 in both cases), this was not the case for the Face dataset, likely because of a filter applied during EEG acquisition (see Discussion) (Fig. 2 ). Not all filters are created equal. Most software packages have various options and parameters to design filters, and it was impractical to test them all. We tested the default filter in the publicly available software packages Figure  1.
Percentage of significant electrodes across time for three publicly available experiments. Event-related potentials for two conditions (Visual Go/No-go: animal targets vs. non-animal distractors; Face: familiar vs. scrambled faces; Auditory Oddball: oddball vs. standard sounds) are extracted from the raw high-pass filtered data at 0. 5 Hz, and the average percentage of significant electrodes in 20,000 random resamples of 50 trials per condition is calculated at each latency in 50-ms increments. Black curves indicate the median, red regions indicate median absolute deviations, and gray curves show the percentage of significant channels for individual subjects in each dataset. The blue region indicates the 100-ms region of maximum effect size, which may correspond to response-related activity for the Go/No-go and Oddball datasets.
The scalp topography for each experiment is shown using a µV scale. EEGLAB, MNE, Brainstorm, and FieldTrip (see "Methods" section and Supplementary Figure  2 ) and compared them to the reference filter in Supplementary Figure  2. There ERPLAB reference filter performed better than all other filters for the Oddball dataset (p < 0. 0001) but not for the Face and Go/No-go dataset. For the Go/No-go dataset, the MNE filter performed significantly worse than the ERPLAB reference filter (p < 0. 0001), the Brainstorm filter (p < 0. 005), and the EEGLAB filter (trend at p = 0. 02). The MNE filter performed worse for the Face dataset than the EEGLAB filter (p < 0. 002). For Fieldtrip, we tried two methods: one-step preprocessing, which we realized filters raw data epochs, and multi-step preprocessing, which allows filtering raw data before extracting data epochs.
For the Oddball dataset, the FieldTrip filter applied to data epochs decreased the percentage of significant electrodes up to 25% compared to the reference filter (p < 0. 0001) and should be avoided. We use the default EEGLAB filter (see "Methods" sections) to high-pass the data from all datasets at 0. 5 Hz in all analyses in "Line noise removal", "Referencing", "Artifact rejection methods", "Baseline" sections-including those using the MNE, Brainstorm, and FieldTrip software packages. Line noise removal. There are several ways to remove or minimize line noise in EEG data (see "Methods" section; Supplementary Figure  3 ). The most common method is filtering out the line noise frequency using a bandstop filter, also known as a notch filter. We used an FIR notch filter and IIR notch filter (see "Methods" section), and observed no change in the percentage of significant channels for any of the datasets-note that the Visual Go/No-go dataset already had the line noise notched (Supplementary Table  1 ).
We also tested the cleanline EEGLAB plugin  13  , which estimates and removes sinusoidal line noise, and the Zapline-plus EEGLAB plugin  14  , which combines spectral and spatial filtering to remove line noise. These methods did not yield significant differences compared to no line-noise removal, except for the Face dataset, where they led to a small yet significant performance decrease (cleanline p < 0. 001) or a trend performance decrease (Zapline-plus p < 0. 02). We also rejected noisy channels based on their activity distribution at the line noise frequency (see "Methods" section). We interpolated channels that had 1, 1. 25, 1. 5, 2, 3, and 4 standard deviations more line noise than other channels (see "Methods" section). For the Go/No-go dataset, rejecting noisy channels led to significant performance improvements (p < 0.
002 for all standard deviations values tested), with a 1 standard deviation threshold leading to the best results for the Face dataset (average of 17% of channels rejected) and a 1. 25 standard deviation threshold for the Auditory Oddball dataset (average of 17% of channels rejected). For our final pipeline (see "Optimized pipelines" section), we chose the default threshold of 4 standard deviations, which led to significant improvement for the Face and Oddball datasets (p < 0. 005 in both cases) and a modest percentage of channels rejected (average of 1% to 6%, depending on the dataset). Referencing. Figure  3  shows the comparison of using different references for the Oddball dataset, for which it was possible to test more reference montages than for other datasets (see Supplementary Figure  4  for the Go/ No-go and Face data).
For this dataset, all re-referencing significantly decreased the percentage of significant channels (p < 0. 005 in all cases), including the PREP reference, which uses channel interpolation to remove artifacts (see "Methods" section). For the Go/No-go dataset, a trend decrease in performance was observed for all reference types (p < 0. 09 or lower), and a non-significant decrease was also noted for the Face dataset. For the Oddball dataset, the median, average, REST, and PREP references decreased performance the least, although they were not significantly better than other reference methods except the circumferential reference. The Nose reference introduced the highest variability (Fig. 3 ). The circumferential reference daisy chain montage also performed significantly below the median, average, REST, and PREP references for the Go/No-go, Face, and Oddball datasets (significance or trend of p < 0.
EEGLAB clean_rawdata channel rejection. The core plugin clean_rawdata was used for detecting bad channels with correlation thresholds ranging from 0. 15 to 0. 975 (see "Methods" section and Supplementary Figure  5A ). A higher correlation threshold is more aggressive at rejecting channels. We found that a correlation of threshold of 0. 95 led to a significant 2% improvement compared to no channel rejection for the Face dataset, with 7% of channels rejected. The best correlation was 0. 97 for the Oddball dataset, with 29% of channels rejected and a 15% increase in performance. To avoid rejecting too many channels, for the final EEGLAB pipeline ("Optimized pipelines" section), we chose to use 90%, which led to a significant performance increase for the Face and Oddball datasets (p = 0. 003 and p < 0.
0001, respectively) and rejected between 3 and 12% of the channels, depending on the dataset. EEGLAB clean_rawdata ASR rejection. The core plugin clean_rawdata was used for detecting bad data segments using the first step of the Artifact Subspace Reconstruction (ASR) method  5  (see "Methods" section and Supplementary Figure  5B ). The threshold used for ASR ranged from 5 to 200 (Table  1 ). No ASR value led to a significant increase in performance. A low threshold of 5 tended to reject 100% of the trials for all subjects of the Oddball dataset. Since ASR rejection did not significantly decrease the percentage of significant channels, for the final EEGLAB pipeline ("Optimized pipelines" section), we chose the default value, which is equal to 20. EEGLAB ICLabel eye movement and muscle rejection.
Independent Component Analysis (ICA) was first applied to EEG (Picard plugin; see "Methods" section and Supplementary Figure  6 ). ICLabel 15 was then applied to detect artifacts with thresholds ranging from a probability of 0. 5 to 0. 9 for belonging to the eye or muscle component category. For eye artifacts, we found a trend advantage of using ICA for the Go/No-go dataset (p < 0. 05 for all thresholds from 50 to 90%, with a minimum of p = 0. 01 at 50%). For muscle artifacts, we found a significant advantage to using ICA for the Oddball dataset (0. 6 threshold and p = 0. 008). For the final EEGLAB pipeline ("Optimized pipelines" section), we used the default value, which is 0. 9. We also tested ICLabel on other ICA algorithms commonly used on EEG data  16  (runica, AMICA, FastICA, and SOBI), but none increased performance significantly (Supplementary Figure  7 ).
FieldTrip ft_artifact_zvalue. The function ft_artifact_zvalue of FieldTrip was used to detect artifacts automatically (see "Methods" section). We varied the z-score threshold from 1 to 6 (Table  1  and Supplementary Figure  8 ). For low frequency, we observed no significant performance improvement. A threshold below 4 for the Oddball dataset decreased performance significantly (p < 0. 0005). For high frequency, a threshold of 5 or 6 increased performance for the Go/No-go dataset (p < 0. 0001) but decreased performance for the Oddball dataset (p < 0. 001). In the final FieldTrip pipeline ("Optimized pipelines" section), we set the threshold to 4 for both low and high frequency, as recommended in the FieldTrip tutorial (see "Methods" section). Brainstorm bad segment detection. In Brainstorm, we used the function to detect bad data portions (see "Methods" section and Supplementary Figure  9 ).
This function allows finding low-frequency or high-frequency Table  1. Evaluation of different methods for automated artifact rejection in the most popular open-source software packages for EEG data analysis (EEGLAB, FieldTrip, Brainstorm, and MNE). For all data rejection methods, the data is first high-pass filtered at 0. 5 Hz (see "Methods" section). The percentage of significant channels is compared to raw data high-pass filtered at 0. 5 Hz. For each method, we indicate the range of parameters tested. Then for each dataset, the first value indicates the optimal parameter, the second value (in parentheses) is the percentage of rejected trials (or interpolated channels for the EEGLAB clean_rawdata channel correlation method) followed by the p-value (see "Methods" section). artifacts with a sensitivity level varying from 1 to 5.
For low frequency, a sensitivity of 5 led to a significant increase in performance for the Go/No-go dataset (p < 0. 002). Sensitivity values of 1 and 2 also led to a significant performance increase (p < 0. 003) but rejected one or more subjects. There were no significant differences for other datasets, except for the sensitivity value of 1 in the Oddball dataset, which rejected 11/14 subjects. Given the aggressiveness of this method, we used the lowest sensitivity of 5 in the final Brainstorm pipeline. For high-frequency artifacts, at levels 1 and 2, no trials were left for any subjects in any datasets. Level 5, the least aggressive sensitivity value, provided trend improvements for the Oddball dataset (p < 0. 005), with 19% of trials rejected. We used this threshold in the final Brainstorm pipeline ("Optimized pipelines" section).
Brainstorm bad trial detection. In Brainstorm, we also used the function to detect bad data trials (process_ detectbad), varying the threshold from 200 to 5000 (see "Methods" section and Supplementary Figure  9 ). No value led to a significant increase or decrease in performance. We used a threshold of 200 in the final Brainstorm pipeline ("Optimized pipelines" section). MNE Autoreject. We used MNE and the Autoreject plugin library (see "Methods" section and Supplementary Figure  10 ). Autoreject scans the parameter space for optimal values and the main free parameter is the number of epochs to fit the data. We used 20 epochs and all epochs (from about 300 to 600 epochs, depending on the dataset) (Supplementary Figure  10 ) and observed no significant difference between the two. In the optimal MNE pipeline ("Optimized pipelines" section), we fitted the data using the first 20 epochs to speed up computation, as advised in the Autoreject tutorial (see "Methods" section).
Baseline. We tested if pre-stimulus baseline periods ranging from 100 to 1000 ms affected the percentage of significant channels (Fig. 4 ). Applying a pre-stimulus baseline did not significantly improve performance for any of the three datasets. In fact, for all baselines performance significantly decreased or trended-p < 0. 025 in all cases except for baseline ranging from 500 to 1000 ms in the Oddball dataset. Performance decreased the most for shorter baselines, with a significant performance drop of 3% to 6% for the 400-ms baseline in all datasets (Fig. 4 ) and a significant performance decrease between the 400-ms and the 1000-ms baseline for the Go/No-go and Oddball datasets (p = 0. 001 in both cases). These results contradict standard practice in event-related potential (ERP) research, although the results might have been different if the data had not been high-pass filtered at 0.
5 Hz. To address this issue, we compared two approaches. We compared an approach proposed by Luck and collaborators using a filter at 0. 01 Hz with a 200ms pre-baseline interval (baseline method)  17  , with a filter at 0. 5 Hz and no baseline (Supplementary Figure  11 ). For all three datasets, using the baseline method led to a significant and large decrease in performance of 30% for the Go/No-go dataset (p < 0. 0001), 8% for the Face dataset (trend at p = 0. 02), 42% for the Oddball dataset (p < 0. 0001) (Supplementary Figure  11 ). As indicated in Supplementary Table  1 , the Face dataset was high-pass filtered at 0. 1 Hz at recording time, and the decrease of 8% in performance we observed would likely be higher if this had not been the case.
Optimized pipelines. For each open-source software package, we built an optimal pipeline based on results presented in the "Artifact rejection methods" section. The "Artifact rejection methods" section indicates how we chose parameters for each artifact rejection method. At the end of the preprocessing pipelines, epochs are extracted, and performance is computed (see "Methods" section). Unlike "High-pass filtering", "Line noise removal", "Referencing", "Artifact rejection methods" sections for which EEGLAB was always used to filter the raw data and extract data epochs, all pipelines were implemented stand-alone in each software package, meaning that code from other toolboxes was not used. The goal is to provide an optimal integrated and self-contained pipeline to the users of each software package-pipelines are available publicly on GitHub (https:// github.
com/ sccn/ eeg_ pipel ines). These pipelines are optimized to maximize the percentage of significant channels, so no referencing ("Referencing" section) and no baseline ("Baseline" section) are applied (see "Methods" section). Despite using the most conservative available settings to reject artifacts, the Brainstorm pipeline rejected all trials for some subjects (Fig. 5 ; Go/No-go: n = 4; Face: n = 14; Oddball n = 1). This is likely because Brainstorm is tailored for MEG processing, where artifacts' amplitude might differ. The FieldTrip pipeline failed to process some datasets (Go/No-go: n = 3; Face: n = 4) for which time-locking event latencies were not falling on data samples. Both the FieldTrip and Brainstorm tool developers have been notified of these issues, and we expect they will fix them.
For the Go/No-go and Oddball datasets, all pipelines led to a higher percentage of significant electrodes than no preprocessing (p < 0. 0001). The EEGLAB pipeline performed best overall. It performed better than the high-pass 0. 5 Hz pipeline for the Face (p < 0. 004) and the Oddball dataset (p = 0. 0008). The HAPPE, Brainstorm, Fieldtrip, and MNE pipelines did not perform significantly better than the high-pass 0. 5 Hz pipeline for any of the datasets. The EEGLAB pipeline performed significantly better in pairwise comparisons with all other pipelines for the Face dataset (p < 0. 006 except FieldTrip), and it performed better than all other pipelines for the Oddball dataset (p < 0. 005 except Brainstorm). Fieldtrip performed significantly better than HAPPE (p = 0. 01) for the Go/ No-go dataset.