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37 results for “SED”
SEDflow: Accelerated Bayesian SED Modeling using Amortized Neural Posterior Estimation
<p><a href="http://changhoonhahn.github.io/SEDflow">SEDflow</a> is an accelerated Bayesian SED modeling method that uses the <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220201809H/abstract">Hahn et al. (2022a)</a> PROVABGS SED model and Amortized Neural Posterior Estimation (ANPE) to derive posterior probability distributions of galaxy properties from optical photometry. SEDflow is<span class="math-tex">\(10^5\times\)</span> faster than conventional Markov Chain Monte Carlo sampling methods and takes ~1 second per galaxy to obtain posteriors. This repository includes all of the data used to train, validate, and test SEDflow.</p> <p>This repository also includes a value-added catalog with detailed physical properties of 33,884 galaxies in the NASA-Sloan Atlas (http://www.nsatlas.org/). The properties are inferred from optical photometry in the <em>u, g, r, i, z</em> bands using SEDflow. For more details on this catalog and SEDflow see the <a href="http://changhoonhahn.github.io/SEDflow">documentation</a> and Hahn & Melchior (2022). </p> <p>For each galaxy, the catalog provides posteriors of: </p> <ul> <li>log_mstar: log10 of stellar mass</li> <li>log_sfr_1gyr: log10 of average star formation rate over 1Gyr</li> <li>log_z_mw: log10 of mass-weighted metallicity</li> <li>beta1, beta2, beta3, beta4: coefficients of the non-negative matrix factorization (NMF) star formation history basis functions</li> <li>fburst: fraction of stellar mass formed by a starburst event</li> <li>tburst: time of the starburst event</li> <li>log_gamma1, log_gamma2: log10 of coefficients of the NMF metallicity history basis functions</li> <li>tau_bc: birth cloud optical depth</li> <li>tau_ism: diffuse dust optical depth</li> <li>n_dust: Calzetti (2001) dust index</li> </ul>
SONYC-FSD-SED
<p><strong>Created by</strong></p> <p>Yu Wang, Mark Cartwright, and Juan Pablo Bello</p> <p> </p> <p><strong>Publication</strong></p> <p>If using this data in academic work, please cite the following paper, which presented this dataset:</p> <blockquote> <p>Y. Wang, M. Cartwright, and J. P. Bello. "Active Few-Shot Learning for Sound Event Detection", INTERSPEECH, 2022</p> </blockquote> <p> </p> <p><strong>Description</strong></p> <p>SONYC-FSD-SED is an open dataset of programmatically mixed audio clips that simulates audio data in an environmental sound monitoring system, where sound class occurrences and co-occurrences exhibit seasonal periodic patterns. We use recordings collected from the Sound of New York City (<a href="https://cacm.acm.org/magazines/2019/2/234354-sonyc/fulltext">SONYC</a>) acoustic sensor network as backgrounds, and single-labeled clips in the <a href="https://zenodo.org/record/4060432#.YWsYokbMIWo">FSD50K</a> dataset as foreground events to generate 576,591 10-second strongly-labeled soundscapes with <a href="https://github.com/justinsalamon/scaper">Scaper</a> (including 111,294 additional test data for the experiment of sampling window). Instead of sampling foreground sound events uniformly, we simulate the occurrence probability of each class at different times in a year, creating more realistic temporal characteristics.</p> <p> </p> <p><strong>Source material and annotations</strong></p> <p>Due to the large size of the dataset, instead of releasing the raw audio files, we release the source material and soundscape annotations in <a href="https://github.com/marl/jams/">JAMS</a> format, which can be used to reproduce SONYC-FSD-SED using Scaper with the script in the project <a href="https://github.com/wangyu/active-fsl">repository</a>. </p> <p> </p> <p><strong>Background material from SONYC recordings</strong></p> <p>We pick a sensor from the SONYC sensor network and subsample from recordings it collected within a year (2017). We categorize these ∼550k 10-second clips into 96 bins based on timestamps, where each bin represents a unique combination of the <em>month of a year</em>, <em>day of a week </em>(weekday or weekend), and <em>time of a day</em> (divided into four 6-hour blocks). Next, we run a pre-trained <a href="https://arxiv.org/pdf/2009.05188.pdf">urban sound event classifier</a> over all recordings and filter out clips with active sound classes. We do not filter out footstep and bird since they appear too frequently, instead, we remove these two classes from the foreground sound material. Then from each bin, we choose the clip with the lowest sound pressure level, yielding 96 background clips.</p> <p> </p> <p><strong>Foreground material from FSD50K</strong></p> <p>We follow the same filtering process as in <a href="https://zenodo.org/record/5574135#.YyeeNuzMLUJ">FSD-MIX-SED</a> to get the subset of FSD50K with short single-labeled clips. In addition, we remove two classes, "Chirp_and_tweet" and "Walk_and_footsteps", that exist in our SONYC background recordings. This results in <strong>87 sound classes</strong>. <em>vocab.json</em> contains the list of 87 classes, each class is then labeled by its index in the list. 0-42: train, 43-56: val, 57-86: test. </p> <p> </p> <p><strong>Occurrence probability modelling</strong></p> <p>For each class, we model its occurrence probability within a year. We use von Mises probability density functions to simulate the probability distribution over different weeks in a year and hours in a day considering their cyclic characteristics: <span class="math-tex">\(f(x|μ, κ) = e^{κcos(x−μ)}/2πI_0(κ)\)</span>, where <span class="math-tex">\(I_0(κ)\)</span> is the modified Bessel function of order <span class="math-tex">\(0\)</span>, <span class="math-tex">\(\mu\)</span> and <span class="math-tex">\(1/\kappa\)</span> are analogous to the mean and variance in the normal distribution. We randomly sample <span class="math-tex">\((\mu_{year}, \mu_{day})\)</span> from <span class="math-tex">\([-\pi, \pi]\)</span> and <span class="math-tex">\((\kappa_{year}, \kappa_{day})\)</span> from <span class="math-tex">\([0, 10]\)</span>. We also randomly assign <span class="math-tex">\(p_{weekday} \in [0, 1] \)</span>, <span class="math-tex">\(p_{weekend} = 1 − p_{weekday}\)</span> to simulate the probability distribution over different days in a week. Finally, we get the probability distribution over the entire year with a 1-hour resolution. At a given timestamp, we integrate <span class="math-tex">\(f_{year}\)</span> and <span class="math-tex">\(f_{day}\)</span> over the 1-hour window and multiply them together with <span class="math-tex">\(p_{weekday}\)</span> or <span class="math-tex">\(p_{weekend}\)</span> depends on the day. To speed up the following sampling process, we scale the final probability distribution using a temperature parameter randomly sampled from <span class="math-tex">\([2,3]\)</span>.</p> <p> </p> <p><strong>Files</strong></p> <ul> <li><em>SONYC_FSD_SED.source.tar.gz</em>: 96 SONYC backgrounds and 10,158 foreground sounds in `.wav` format. The original file size is 2GB.</li> <li><em>SONYC_FSD_SED.annotations.tar.gz</em>: 465,467 JAMS files. The original file size is 57GB. </li> <li><em>SONYC_FSD_SED_add_test.annotations.tar.gz: </em>111,294 JAMS files for additional test data. The original file size is 14GB. </li> <li><em>vocab.json:</em> 87 classes.</li> <li><em>occ_prob_per_cl.pkl</em>: Occurrence probability for each foreground sound class. </li> </ul> <p> </p> <p><strong>References</strong></p> <p>[1] J. P. Bello, C. T. Silva, O. Nov, R. L. DuBois, A. Arora, J. Salamon, C. Mydlarz, and H. Doraiswamy, “SONYC: A system for monitoring, analyzing, and mitigating urban noise pollution,” Commun. ACM, 2019</p> <p>[2] E. Fonseca, X. Favory, J. Pons, F. Font, X. Serra. "FSD50K: an Open Dataset of Human-Labeled Sound Events", arXiv:2010.00475, 2020.</p> <p> </p>
The Quest for the Missing Dust: New Herschel Maps of Local Group Galaixes (LMC, SMC, M31, M33) that Restore Previously-Missed Extended Emission, Along With SED-Fitting Results, Hydrogen Gas Maps, and Swift UV Observations
<p>Here we provide the data products from publications:</p> <p>Clark, C.J.R., et al., <em>The Quest for the Missing Dust: I – Restoring Large Scale Emission in Herschel Maps of Local Group Galaxies</em>, ApJ 921 35</p> <p>Clark, C.J.R., et al., <em>The Quest for the Missing Dust: II – Two Orders of Magnitude of Evolution in the Dust-to-Gas Ratio Resolved Within Local Group Galaxies</em>, ApJ 946 42</p> <p>This data concerns four Local Group galaxies: the Large Magellanic Cloud (LMC), the Small Magellanic Cloud (SMC), M31, and M33.</p> <p> </p> <p>For each galaxy, we provide our new Herschel maps, as described in the above publications, which were combined in Fourier space ('feathered') with Planck, IRAS, and COBE data, in order to restore extended emission that was removed from previous Herschel reductions for these galaxies.</p> <p>For each galaxy, we provide this new Herschel data for 5 Hershcel bands: the PACS 100 and 160 <span>\(\mu\)</span>m bands, and the SPIRE 250, 350, and 500 <span>\(\mu\)</span>m bands. This data is provided in FITS format, with one FITS file for each band for each galaxy. Each of these files contains 4 extensions. Extension 1 (IMAGE) provides the standard feathered map. Extension 2 (UNC) provides the uncertainty map. Extension 3 (MASK) provides a binary mask map indicating the portion of the data where reliable, fully-feathered high-resolution coverage is available. Extension 4 provides the foreground-subtracted version of the feathered map (FGND_SUB), the header of which also describes the uncertainty on that subtraction. All maps are in units of MJy/sr (except the MASK extension, which is boolean).</p> <p> </p> <p>We also provide the outputs of our Spectral Energy Distribution (SED) fitting to this data, as described in the publications. For each galaxy, we provide FITS files giving the median value of each parameter in each pixel, and maps of the uncertainties on those medians (being the 68.3% quantile around the median). The parameters are dust mass surface density (SED_Sigma_Mass.fits), dust temperature (SED_Temp.fits), beta 1 (SED_Beta1.fits), beta 2 (SED_Beta2.fits), break wavelength (SED_Break.fits), and 500 <span>\(\mu\)</span>m excess (SED_Excess500.fits). Each of these files contain 2 extensions. Extension 1 (median) provides the map of pixel parameter median values. Extension 2 (uncert) provides the map of uncertainties on those medians.</p> <p>Additionally, we provide the full posterior probability distribution for all SED parameters, consisting of 1000 posterior samples, for all pixels, in the form of a FITS file containing a 4-dimensional hypercube, with axes corresponding to right ascension, declination, parameters (in order: dust mass surface density, dust temperature, beta 1, beta 2, break wavelength, and 500 <span>\(\mu\)</span>m excess), and samples. This is provided as a gzip compressed FITS file for each galaxy.</p> <p>Furthermore, provide the Swift-UVOT maps used in Paper II. This data is provided for Swift-UVOT bands W1, W2, and M2. For each band, we provide a FITS file containing 3 extensions. Extension 1 (SURF_BRI) provides the map of surface brightness in MJy/sr (converted using the Swift-UVOT zero points given in Breeveld et al., 2011). Extension 2 (RATE) provides the map of count rate (in photons/sec). Extension 3 (EXP) provides the map of exposure time (in sec). The maps for the LMC and SMC are those presented in Hagen et al. (2017). The maps for M31 and M33 are were reduced following the same process as those in Hagen et al. (2017), and will be fully presented in Decleir et al. (in prep.), but are provided here for the purposes of reproducibility.</p> <p>Lastly, for each galaxy, we provide our maps of the hydrogen surface density (Sigma_H.fits), and dust-to-gas ratio (DtG.fits). None of the maps presented have had deprojection corrections applied</p> <p> </p>
JWST convolutions for a modular set of synthetic SEDs for young stellar objects (Robitaille, 2017)
<p>This is a companion to the models released alongside the publication:</p> <p><em>A modular set of synthetic spectral energy distributions for young stellar objects</em>, Robitaille (2017)</p> <p>The models are convolved with JWST filters taken from the SVO’s filter profile service. Some models with rotationally flattened envelopes (i.e. geometries with<strong> u</strong>)<strong> </strong>not present in the original model grid have since been completed; their convolved SEDs are included here.</p> <p>Files unzip to {geometry}/convolved/JWST/{SVO_filtername}.fits.</p> <p>This is a subset of the information included in https://doi.org/10.5281/zenodo.8114592.</p>
ETHZ-SED EEW unit-test dataset
<p>This is a preliminary version of a unit-test dataset for EEW algorithms developed at SED-ETHZ based on the data from the MLh 4.3 earthquake on October 25, 2020 in Elm (Switzerland). Further information about this earthquake dataset can be found in Massin, Clinton & Böse (2021).</p>
Dataset for "From Halos to Galaxies. X: Decoding Galaxy SEDs with Physical Priors and Accurate Star Formation History Reconstruction"
<p>This deposit contains the data related to the manuscript "<em>From Halos to Galaxies. X: Decoding Galaxy SEDs with Physical Priors and Accurate Star Formation History Reconstruction</em>" submitted to the Astrophysical Journal. It includes the basic SDSS identifier, stellar mass, star formation rate, fractional formation time, and their errors. A detailed description can be found in Table 1 of the manuscript.</p> <p>The data is stored in a CSV file. It can be read with standard data analysis packages like <a href="https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html">Pandas</a> in Python. </p> <p>This deposit has also be updated to include a machine-readable table that follows the standards of the AAS Journals and Vizier (<span>datafile1_ApJ57534.mrt). More information on this standard can be found in the <a href="https://journals.aas.org/mrt-overview/">AAS</a> or <a href="http://cds.u-strasbg.fr/doc/catstd.htx">CDS</a> documentation. This format can be read in Python with packages like <a href="https://docs.astropy.org/en/stable/api/astropy.io.ascii.Mrt.html">astropy</a>. </span></p>
SED templates for "Dwarf AGNs from Variability for the Origins of Seeds (DAVOS): Intermediate-mass black hole demographics from optical synoptic surveys"
<p>FITS file containing the pre-computed grid of Done or Nemmen model SEDs. See Table 2 in the publication for details.</p>
Best-fit SED templates of each CAMIRA member galaxy (Table 3)
<p>This upload includes individual SED templates as described in Table 3 of the related manuscript, "<em>Active Galactic Nucleus Properties of ~1 Million Member Galaxies of Galaxy Groups and Clusters at z < 1.4 Based on the Subaru Hyper Suprime-Cam Survey</em>", by Yoshiki Toba et al., published in the Astrophysical Journal (Accepted: 15-Feb-2024; published: 17-May-2024, doi:<a href="https://doi.org/10.3847/1538-4357/ad32c6">10.3847/1538-4357/ad32c6</a>).</p> <p>This archive is compressed with xz, part of <a href="https://github.com/tukaani-project/xz">XZ Utils</a>. </p> <p>A description of these files is given below.</p> <p>The "ID" field is captured in the file naming in the compressed archive:</p> <ul> <li> <p>1_SED.eps [EPS plot of SED, SED fit, for Object ID = 1]</p> </li> <li> <p>1_SED_data.fits [Binary Table FITS (HDU=1) where the contents of HDU=1 are given in the table below]</p> </li> </ul> <p> </p> <table> <tbody> <tr> <td> <div> <div> <div> <p>Column name</p> </div> </div> </div> </td> <td> <div> <div> <div> <p>Format</p> </div> </div> </div> </td> <td> <div> <div> <div> <p>Unit</p> </div> </div> </div> </td> <td> <div> <div> <div> <p>Description</p> </div> </div> </div> </td> </tr> <tr> <td> <div> <div> <div> <p>Wavelength</p> </div> </div> </div> </td> <td> <div> <div> <div> <p>DOUBLE</p> </div> </div> </div> </td> <td> <div> <div> <div> <p>μm</p> </div> </div> </div> </td> <td> <div> <div> <div> <p>Wavelength (observed frame)</p> </div> </div> </div> </td> </tr> <tr> <td> <div> <div> <div> <p>FNU</p> </div> </div> </div> </td> <td> <div> <div> <div> <p>DOUBLE</p> </div> </div> </div> </td> <td> <div> <div> <div> <p>mJy</p> </div> </div> </div> </td> <td> <div> <div> <div> <p>Flux density at each wavelength</p> </div> </div> </div> </td> </tr> <tr> <td> <div> <div> <div> <p>log_L</p> </div> </div> </div> </td> <td> <div> <div> <div> <p>DOUBLE</p> </div> </div> </div> </td> <td>erg/s</td> <td> <div> <div> <div> <p>Luminosity at each wavelength</p> </div> </div> </div> </td> </tr> </tbody> </table> <p> </p> <p> </p>
URBAN-SED
<p><strong>DESCRIPTION</strong></p> <p>URBAN-SED is a dataset of 10,000 soundscapes with sound event annotations generated using scaper (github.com/justinsalamon/scaper).</p> <p>A detailed description of the dataset is provided in the following article:</p> <p><strong>Scaper: A Library for Soundscape Synthesis and Augmentation</strong><br> J. Salamon, D. MacConnell, M. Cartwright, P. Li, and J. P. Bello.<br> In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), New Paltz, NY, USA, Oct. 2017.<br> (PDF: https://goo.gl/RsfRhP)</p> <p>A summary is provided here:</p> <ul> <li>The dataset includes 10,000 soundscapes, totals almost 30 hours and includes close to 50,000 annotated sound events</li> <li>Complete annotations are provided in JAMS format, and simplified annotations are provided as tab-separated text files</li> <li>Every soundscape is 10 seconds long and has a background of Brownian noise resembling the typical "hum" often heard in urban environments</li> <li>Every soundscape contains between 1-9 sound events from the following classes: <ul> <li>air_conditioner, car_horn, children_playing, dog_bark, drilling, engine_idling, gun_shot, jackhammer, siren and street_music</li> </ul> </li> <li>The source material for the sound events are the clips from the UrbanSound8K dataset (https://serv.cusp.nyu.edu/projects/urbansounddataset/)</li> <li>URBAN-SED comes pre-sorted into three sets: train, validate and test: <ul> <li>There are 6000 soundscapes in the training set, generated using clips from folds 1-6 in UrbanSound8K</li> <li>There are 2000 soundscapes in the validation set, generated using clips from folds 7-8 in UrbanSound8K</li> <li>There are 2000 soundscapes in the test set, generated using clips from folds 9-10 in UrbanSound8K</li> </ul> </li> <li>Further details about how the soundscapes were generated including the distribution of sound event start times, durations, signal-to-noise ratios, pitch shifting, time stretching, and the range of sound event polyphony (overlap) can be found in Section 3 of the scaper paper: https://goo.gl/RsfRhP</li> <li>The scripts used to generated URBAN-SED using scaper can be found here: https://github.com/justinsalamon/scaper_waspaa2017/tree/master/notebooks</li> </ul> <p><strong>AUDIO FILES INCLUDED</strong></p> <p>* 10,000 synthesized soundscapes in single channel (mono), 44100Hz, 16-bit, WAV format.<br> * The files are split into a training set (6000), validation set (2000) and test set (2000).</p> <p><strong>ANNOTATION FILES INCLUDED</strong><br> <br> The annotations list the sound events that occur in every soundscape. The annotations are "strong", meaning for every <br> sound event the annotations include (at least) the start time, end time, and label of the sound event. Sound events <br> come from the following 10 labels (categories):<br> * air_conditioner, car_horn, children_playing, dog_bark, drilling, engine_idling, gun_shot, jackhammer, <br> siren, street_music</p> <p>There are two types of annotations: full annotations in JAMS format, and simplified annotations in <br> tab-separated txt format.</p> <p>JAMS Annotations<br> -------------------------<br> * The full annotations are distributed in JAMS format (https://github.com/marl/jams).<br> * There are 10,000 JAMS annotation files, each one corresponding to a single soundscape with the same filename (other than the extension)<br> * Each JAMS file contains a single annotation in the scaper namespace format - jams >=v0.3.2 is required in order to load the annotation into python with jams:<br> import jams <br> jam = jams.load('soundscape_train_bimodal0.jams').<br> * The value of each observation (sound event) is a dictionary storing all scaper-related sound event parameters:<br> * label, source_file, source_time, event_time, event_duration, snr, role, pitch_shift, time_stretch.<br> * Note: the event_duration stored in the value dictionary represents the specified duration prior to any time <br> stretching. The actual event durtation in the soundscape is stored in the duration field of the JAMS observation.<br> * The observations (sound events) in the JAMS annotation include both foreground sound events and the background(s).<br> * The probabilistic scaper foreground and background event specifications are stored in the annotation's sandbox, allowing<br> a complete reconstruction of the soundscape audio from the JAMS annotation (assuming access to the original source material)<br> using scaper.generate_from_jams('soundscape_train_bimodal0.jams').<br> * The annotation sandbox also includes additional metadata such as the total number of foreground sound events, the <br> maximum polyphony (sound event overlap) of the soundscape and its gini coefficient (a measure of soundscape complexity).</p> <p>Simplified Annotations<br> ------------------------------<br> * The simplified annotations are distributed as tab-separated text files.<br> * There are 10,000 simplified annotation files, each one corresponding to a single soundscape with the same filename (other than the extension)<br> * Each simplified annotation has a 3-column format (no header): start_time, end_time, label.<br> * Background sounds are NOT included in the simplified annotations (only foreground sound events)<br> * No additional information is stored in the simplified events (see the JAMS annotations for more details).</p> <p><strong>Please acknowledge this dataset in academic research</strong></p> <p>We would highly appreciate it if scientific publications of work partly based on URBAN-SED and/or scaper cite the aforementioned publication.</p> <p>The creation of this dataset was supported by NSF award 1544753.</p> <p><strong>Version 2.0.0</strong><br> - Audio files generated with scaper v0.1.0 (identical to audio in URBAN-SED 1.0)<br> - Jams annotation files generated with scaper v0.1.0 and updated to comply with scaper v1.0.0 (namespace changed from "sound_event" to "scaper")<br> - NOTE: due to updates to the scaper library, regenerating the audio from the jams annotations using scaper >=1.0.0 will result in audio files that are highly similar, but not identical, to the audio files provided. This is because the provided audio files were generated with scaper v0.1.0 and have been purposely kept the same as in URBAN-SED v1.0 to ensure comparability to previously published results.</p>
Predicted UV-to-Radio SEDs of little red dots: AGN and SF
<p>Predicted Dust SEDs for JWST's Little Red Dots<br>17-Sep-2024</p> <p>Here are the average UV through radio spectral energy distributions predicted for the LRD population from Figure 2 of Casey et al. (2024b, https://arxiv.org/abs/2407.05094, ApJL in press). There are two SEDs: one predicting the emission is due to AGN and one that it is due to star-formation. Wavelength is given as log10 of rest-frame wavelength in microns. Flux density is log10 Fnu in mJy. Email cmcasey.astro@gmail.com with questions.</p>
M81 post-processed results from Lightning SED fitting
<p>The post-processed results from the Lightning SED fitting code as fit by the authors for the spatially resolved map of M81. The results are for the MCMC sampler (m81_map_mcmc.fits.gz) and the MPFIT minimizer (m81_map_mpfit.fits.gz). Fitting of this example was performed on a 32-core node of the Pinnacle cluster at the Arkansas High Performance Computing Center.</p>
Simulation of Cold Ion Transport Originating from the SED Plume into Dayside Magnetosphere
<p>Data output from DyFK simulations for ''Simulation of Cold Ion Transport Originating from the SED Plume into Dayside Magnetosphere''. Data descriptions are listed in "dataDescription.docx". </p>
sed_catalogs
<p>SED catalogs</p>
Synthetic Emission Line Catalog and High-Resolution SEDs for SPHEREx Galaxy Simulations
<p><strong>Overview:</strong></p> <p>This repository contains the synthetic galaxy models derived from multi-wavelength photometry in the COSMOS field (166k galaxies over 1.27 sq. deg., 18 < i < 25) and from the GAMA survey (44k galaxies over 217 sq. deg., i < 18). These encompass a representative sample of galaxies SPHEREx will observe, and should be applicable for other surveys as well.</p> <ul> <li>Synthetic emission line strengths in the rest-frame optical/near-infrared (H-alpha, H-beta, Paschen-alpha, [OIII], [OII], [NII], [SII])</li> <li>High-resolution SEDs (0.1 - 8 micron), which are produced by 1) fitting a library of 160 galaxy templates to multi-band photometry and 2) inserting emission lines with strengths predicted by an empirical model that depends on galaxy type, redshift and stellar mass.</li> </ul> <p>These products were made using the modeling framework <strong>C</strong>onditional <strong>LI</strong>ne <strong>P</strong>ainting on <strong>S</strong>ynthetic <strong>S</strong>pectra (<em>CLIPonSS</em>), the details of which are summarized in Feder+2023b (<a href="https://arxiv.org/abs/2312.04636">arXiv link here</a>). The synthetic emission line catalog is validated against a variety of LF measurements, line ratio trends and direct line comparisons. </p> <p><strong>Data description:</strong></p> <p><em><strong>High-resolution SEDs</strong></em>: The SEDs are stored in .FITS files, for which each galaxy SED has its own Header Data Unit (HDU). This format was chosen due to the fact that the wavelength sampling for the 160 galaxy empirical and model-based templates varies, i.e., we do not perform any interpolation onto a homogenized wavelength grid. In cases where emission lines are added but the continuum template resolution is coarse, the resolution is increased in the vicinity of the emission line(s) to adequately sample the line profiles. Each galaxy's HDU is indexed by its Farmer ID (for COSMOS) or alternatively its uberID (GAMA). The COSMOS SEDs are split into three files for relative ease of access, while the GAMA sources are all in one file.</p> <p><em><strong>Emission line catalogs:</strong></em> The emission line catalogs are stored in .csv format with the following information:</p> <p>Tractor_ID (or uberID) [integer]: Unique identifier for each source</p> <p>RA/DEC [float, in degrees]: Celestial coordinates</p> <p>imag [AB]: i-band magnitude</p> <p>mass_best [float]: log-stellar masses estimated through SED fitting process</p> <p>ebv [float]: Galaxy intrinsic dust extinction (ranging from ebv=0 to 1)</p> <p>redshift [float]: Best-fit redshift from COSMOS2020 (GAMA) catalogs</p> <p>bfit_tid [integer]: Best fit template ID from library of templates</p> <p>dustlaw [integer]: Best fit dust law (1=Prevot, 2=Calzetti, 3=Seaton, 4=Allen, 5=Fitzpatrick)</p> <p>L_{line} [float, erg s-1]: Line luminosities from empirical model</p> <p>F_{line} [float, erg cm-2 s-1]: Line fluxes from empirical model</p> <p>ew_ha [float, Angstrom]: Equivalent width of H-alpha from empirical model</p> <p><strong><em>Fitting templates: </em></strong>We include the 31 model-based templates and 129 empirical templates from Brown+2014. In the Brown templates we have removed any relevant lines that were directly measured in the initial spectra, however we do not fit for/remove PAH features. These can serve as the basis for other empirically based galaxy simulations with different emission line prescriptions.</p> <p>Any questions regarding the use of these data products or related issues can be directed to Richard Feder (link to <a href="https://richardfeder.github.io/">personal website</a>). </p> <p> </p>
SED and REWARD
<p>This depository contains : </p> <ul> <li>Preregistration for the two main studies</li> <li>Data and codes for the two main studies</li> </ul>
Example stacked images and best-fit dust SEDs.
<p>Example stacked images, shown in bins of stellar mass and redshift, for the Herschel SPIRE 500 μm band.</p> <p>Stacked far-IR photometry for the whole sample of galaxies (SEDs_all) and for the star-forming only (SEDs_SF). The best-fit<br>dust emission curves, adopted from Casey (2012), are shown in solid green with 1σ standard deviation in grey.</p>
JWST spectra and best-fit SED models of massive quiescent galaxies from the Blue Jay survey.
<ul> <li>The JWST/NIRSpec spectra are stored as FITS tables which include wavelength (in angstrom, in rest-frame), calibrated flux, uncertainty, and best-fit model (in 1e-19 erg/s/cm^2/AA).</li> </ul>
Mega-MUSCLES survey paper updated SEDs
<p>Spectral Energy Distributions (SEDs) for M dwarf stars described in Wilson et al. 2024. Use this repository until the updated SEDs are avaialable at MAST. For simplicty only the full SEDs are incuded here (as described in Section 3.7 of the paper), please contact David Wilson david.wilson@lasp.coloroado.edu for sub-spectra. The tar file contains all four formats of the SEDs for all stars. </p>
Complementary dataset of the paper "The viewing angle in AGN SED models, a data-driven analysis"
<p>In this repository, you can find the SED data from the X-CIGALE estimates from the article: "<a href="https://academic.oup.com/mnras/article/510/1/687/6448487">The viewing angle in AGN SED models: a data-driven analysis</a>"</p>
sed_models_es1
<p>SED models for ELAIS-S1</p>
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