arXiv subjects

Raphael Shirley

Publications and source records attributed to Raphael Shirley.

18 recordsLinked to original sources

arXiv2026

Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production

Virtually all extragalactic use cases of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) require the use of galaxy redshift information, yet the vast majority of its sample of tens of billions of galaxies will lack high-fidelity spectroscopic measurements thereof, instead relying on photometric redshifts (photo-$z$) subject to systematic imprecision and inaccuracy best encapsulated by photo-$z$ probability density functions (PDFs). We present the version 1 release of Redshift Assessment Infrastructure Layers (RAIL), an open source Python library for at-scale probabilistic photo-$z$ estimation, initiated by the LSST Dark Energy Science Collaboration (DESC) with contributions from the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) Frameworks team. RAIL's three subpackages provide modular tools for end-to-end stress-testing, including a forward modeling suite to generate realistically complex photometry, a unified API for estimating per-galaxy and ensemble redshift PDFs by an extensible set of algorithms, and built-in metrics of both photo-$z$ PDFs and point estimates. RAIL serves as a flexible toolkit enabling the derivation and optimization of photo-$z$ data products at scale for a variety of science goals and is not specific to LSST data. We thus describe to the extragalactic science community, including and beyond Rubin the design and functionality of the RAIL software library so that any researcher may have access to its wide array of photo-$z$ characterization and assessment tools.

astro-ph.IM↗

arXiv2025

The population of tidal disruption events discovered with eROSITA

This paper presents a systematic study of X-ray-selected canonical tidal disruption events (TDEs) discovered in the western Galactic hemisphere of the first two eROSITA all-sky surveys (eRASS1 and eRASS2) performed between Dec 2019 and Dec 2020. We compiled a TDE sample from the catalog of eROSITA's extragalactic transients and variables eRO-ExTra, which includes X-ray sources with a variability significance and fractional amplitude over four between eRASS1 and eRASS2, not associated with known AGNs. Each X-ray source is associated with an optical counterpart from the Legacy Survey DR10. Canonical TDEs were selected based on their X-ray light-curve properties (single flare or decline), soft X-ray spectra ($Γ>3$), and the absence of archival X-ray variability and AGN signatures in their host photometry and spectroscopy. The sample includes 31 X-ray-selected TDE candidates with redshifts of $0.02< z<0.34$ and luminosities of $5.7 \times 10^{41}<L_X<5.3 \times 10^{44}$ erg/s in the 0.2-6.0 keV rest frame, of which 30 are canonical TDEs and one is an off-nuclear TDE candidate. The derived X-ray luminosity function is best fit by a double power law with a luminosity break at $10^{44}$ erg/s, corresponding to the Eddington-limiting prediction. This corresponds to a TDE volumetric rate of $ (2.3^{+1.2}_{-0.9})\times10^{-7}\,Mpc^{-3} yr^{-1}$ ($\approx1.2\times 10^{-5}$ events per galaxy per year). TDE host galaxies show a green-valley overdensity. In addition, 20%, 30%, and 15% of the sample exhibit flares in the optical, mid-infrared (mid-IR), or radio bands, respectively. We discuss the differences between X-ray, optical, and mid-IR TDE populations and the origins of multiwavelength flares in the context of the obscuring envelope and stream-stream collision models. Finally, we highlight TDE subpopulations that are not included in the canonical sample and should be explored in the future.

astro-ph.HE↗

arXiv2025

LensWatch. II. Improved Photometry and Time-delay Constraints on the Strongly Lensed Type Ia Supernova 2022qmx ("SN Zwicky") with HST Template Observations

Strongly lensed supernovae (SNe) are a rare class of transient that can offer tight cosmological constraints that are complementary to methods from other astronomical events. We present a follow-up study of one recently-discovered strongly lensed SN, the quadruply-imaged Type Ia SN 2022qmx (aka, "SN Zwicky") at z = 0.3544. We measure updated, template-subtracted photometry for SN Zwicky and derive improved time delays and magnifications. This is possible because SNe are transient, fading away after reaching their peak brightness. Specifically, we measure point spread function (PSF) photometry for all four images of SN Zwicky in three Hubble Space Telescope WFC3/UVIS passbands (F475W, F625W, F814W) and one WFC3/IR passband (F160W), with template images taken $\sim 11$ months after the epoch in which the SN images appear. We find consistency to within $2σ$ between lens model predicted time delays ($\lesssim1$ day), and measured time delays with HST colors ($\lesssim2$ days), including the uncertainty from chromatic microlensing that may arise from stars in the lensing galaxy. The standardizable nature of SNe Ia allows us to estimate absolute magnifications for the four images, with images A and C being elevated in magnification compared to lens model predictions by about $6σ$ and $3σ$ respectively, confirming previous work. We show that millilensing or differential dust extinction is unable to explain these discrepancies and find evidence for the existence of microlensing in images A, C, and potentially D, that may contribute to the anomalous magnification.

astro-ph.HE↗

arXiv2024

PICZL: Image-based Photometric Redshifts for AGN

Computing photo-z for AGN is challenging, primarily due to the interplay of relative emissions associated with the SMBH and its host galaxy. SED fitting methods, effective in pencil-beam surveys, face limitations in all-sky surveys with fewer bands available, lacking the ability to capture the AGN contribution to the SED accurately. This limitation affects the many 10s of millions of AGN clearly singled out and identified by SRG/eROSITA. Our goal is to significantly enhance photometric redshift performance for AGN in all-sky surveys while avoiding the need to merge multiple data sets. Instead, we employ readily available data products from the 10th Data Release of the Imaging Legacy Survey for DESI, covering > 20,000 deg$^{2}$ with deep images and catalog-based photometry in the grizW1-W4 bands. We introduce PICZL, a machine-learning algorithm leveraging an ensemble of CNNs. Utilizing a cross-channel approach, the algorithm integrates distinct SED features from images with those obtained from catalog-level data. Full probability distributions are achieved via the integration of Gaussian mixture models. On a validation sample of 8098 AGN, PICZL achieves a variance $σ_{\textrm{NMAD}}$ of 4.5% with an outlier fraction $η$ of 5.6%, outperforming previous attempts to compute accurate photo-z for AGN using ML. We highlight that the model's performance depends on many variables, predominantly the depth of the data. A thorough evaluation of these dependencies is presented in the paper. Our streamlined methodology maintains consistent performance across the entire survey area when accounting for differing data quality. The same approach can be adopted for future deep photometric surveys such as LSST and Euclid, showcasing its potential for wide-scale realisation. With this paper, we release updated photo-z (including errors) for the XMM-SERVS W-CDF-S, ELAIS-S1 and LSS fields.

astro-ph.GA↗

arXiv2024

Overcoming Confusion Noise with Hyperspectral Imaging from PRIMAger

The PRobe far-Infrared Mission for Astrophysics (PRIMA) concept aims to perform mapping with spectral coverage and sensitivities inaccessible to previous FIR space telescopes. PRIMA's imaging instrument, PRIMAger, provides unique hyperspectral imaging simultaneously covering 25-235 $\mu$m. We synthesise images representing a deep, 1500 hr deg$^{-2}$ PRIMAger survey, with realistic instrumental and confusion noise. We demonstrate that we can construct catalogues of galaxies with a high purity ($>95$ per cent) at a source density of 42k deg$^{-2}$ using PRIMAger data alone. Using the XID+ deblending tool we show that we measure fluxes with an accuracy better than 20 per cent to flux levels of 0.16, 0.80, 9.7 and 15 mJy at 47.4, 79.7, 172, 235 $\mu$m respectively. These are a factor of $\sim$2 and $\sim$3 fainter than the classical confusion limits for 72-96 $\mu$m and 126-235 $\mu$m, respectively. At $1.5 \leq z \leq 2$, we detect and accurately measure fluxes in 8-10 of the 10 channels covering 47-235 $\mu$m for sources with $2 \leq$ log(SFR) $\leq 2.5$, a 0.5 dex improvement on what might be expected from the classical confusion limit. Recognising that PRIMager will operate in a context where high quality data will be available at other wavelengths, we investigate the benefits of introducing additional prior information. We show that by introducing even weak prior flux information when employing a higher source density catalogue (more than one source per beam) we can obtain accurate fluxes an order of magnitude below the classical confusion limit for 96-235 $\mu$m.

astro-ph.IM↗

arXiv2023

The LSST AGN Data Challenge: Selection methods

Development of the Rubin Observatory Legacy Survey of Space and Time (LSST) includes a series of Data Challenges (DC) arranged by various LSST Scientific Collaborations (SC) that are taking place during the projects preoperational phase. The AGN Science Collaboration Data Challenge (AGNSCDC) is a partial prototype of the expected LSST AGN data, aimed at validating machine learning approaches for AGN selection and characterization in large surveys like LSST. The AGNSC-DC took part in 2021 focusing on accuracy, robustness, and scalability. The training and the blinded datasets were constructed to mimic the future LSST release catalogs using the data from the Sloan Digital Sky Survey Stripe 82 region and the XMM-Newton Large Scale Structure Survey region. Data features were divided into astrometry, photometry, color, morphology, redshift and class label with the addition of variability features and images. We present the results of four DC submitted solutions using both classical and machine learning methods. We systematically test the performance of supervised (support vector machine, random forest, extreme gradient boosting, artificial neural network, convolutional neural network) and unsupervised (deep embedding clustering) models when applied to the problem of classifying/clustering sources as stars, galaxies or AGNs. We obtained classification accuracy 97.5% for supervised and clustering accuracy 96.0% for unsupervised models and 95.0% with a classic approach for a blinded dataset. We find that variability features significantly improve the accuracy of the trained models and correlation analysis among different bands enables a fast and inexpensive first order selection of quasar candidates

astro-ph.GA↗

arXiv2022

From Data to Software to Science with the Rubin Observatory LSST

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the nature of dark matter and dark energy. Much of this research will depend on the existence of robust, tested, and scalable algorithms, software, and services. Identifying and developing such tools ahead of time has the potential to significantly accelerate the delivery of early science from LSST. Developing these collaboratively, and making them broadly available, can enable more inclusive and equitable collaboration on LSST science. To facilitate such opportunities, a community workshop entitled "From Data to Software to Science with the Rubin Observatory LSST" was organized by the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) and partners, and held at the Flatiron Institute in New York, March 28-30th 2022. The workshop included over 50 in-person attendees invited from over 300 applications. It identified seven key software areas of need: (i) scalable cross-matching and distributed joining of catalogs, (ii) robust photometric redshift determination, (iii) software for determination of selection functions, (iv) frameworks for scalable time-series analyses, (v) services for image access and reprocessing at scale, (vi) object image access (cutouts) and analysis at scale, and (vii) scalable job execution systems. This white paper summarizes the discussions of this workshop. It considers the motivating science use cases, identified cross-cutting algorithms, software, and services, their high-level technical specifications, and the principles of inclusive collaborations needed to develop them. We provide it as a useful roadmap of needs, as well as to spur action and collaboration between groups and individuals looking to develop reusable software for early LSST science.

astro-ph.IM↗

arXiv2022

A hyperluminous obscured quasar at a redshift of z ~ 4.3

In this work we report the discovery of the hyperluminous galaxy HELP_J100156.75+022344.7 at the photometric redshift of z ~ 4.3. The galaxy was discovered in the Cosmological Evolution Survey (COSMOS) field, one of the fields studied by the Herschel Extragalactic Legacy Project (HELP). We present the spectral energy distribution (SED) of the galaxy and fit it with the CYprus models for Galaxies and their NUclear Spectra (CYGNUS) multi-component radiative transfer models. We find that its emission is dominated by an obscured quasar with a predicted total 1-1000um luminosity of $3.91^{+1.69}_{-0.55} \times 10^{13} L_\odot$ and an active galactic nucleus (AGN) fraction of ~89%. We also fit HELP_J100156.75+022344.7 with the Code Investigating GALaxy Emission (CIGALE) code and find a similar result. This is only the second z > 4 hyperluminous obscured quasar discovered to date. The discovery of HELP_J100156.75+022344.7 in the ~ 2deg^2 COSMOS field implies that a large number of obscured hyperluminous quasars may lie in the HELP fields which cover ~ 1300deg^2. If this is confirmed, tension between supermassive black hole evolution models and observations will be alleviated. We estimate the space density of objects like HELP_J100156.75+022344.7 at z ~ 4.5 to be $\sim 1.8 \times 10^{-8}$Mpc$^{-3}$. This is slightly higher than the space density of coeval hyperluminous optically selected quasars suggesting that the obscuring torus in z > 4 quasars may have a covering factor $\gtrsim 50\%$.

astro-ph.GA↗

arXiv2021

Consistent analysis of the AGN LF in X-ray and MIR in the XMM-LSS field

The luminosity function (LF) of active galactic nuclei (AGN) probes the history of supermassive black hole assembly and growth across cosmic time. To mitigate selection biases, we present a consistent analysis of the AGN LFs derived for both X-ray and mid-infrared (MIR) selected AGN in the XMM-Large Scale Structure (XMM-LSS) field. There are 4268 AGN used to construct the MIR luminosity function (IRLF) and 3427 AGN used to construct the X-ray luminosity function (XLF), providing the largest census of the AGN population out to $z=4$ in both bands with significant reduction in uncertainties. We are able for the first time to see the knee of the IRLF at $z>2$ and observe a flattening of the faint-end slope as redshift increases. The bolometric luminosity density, a proxy for the cosmic black hole accretion history, computed from our LFs shows a peak at $z\approx2.25$ consistent with recent estimates of the peak in the star formation rate density (SFRD). However, at earlier epochs, the AGN luminosity density is flatter than the SFRD. If confirmed, this result suggests that the build up of black hole mass outpaces the growth of stellar mass in high mass systems at $z\gtrsim 2.5$. This is consistent with observations of redshift $z\sim 6$ quasars which lie above the local $M-σ$ relationship. The luminosity density derived from the IRLF is higher than that from the XLF at all redshifts. This is consistent with the dominant role of obscured AGN activity in the cosmic growth of supermassive black holes.

astro-ph.GA↗

arXiv2021

Progenitor and Close-In Circumstellar Medium of Type II Supernova 2020fqv from High-Cadence Photometry and Ultra-Rapid UV Spectroscopy

We present observations of SN 2020fqv, a Virgo-cluster Type II core-collapse supernova (CCSN) with a high temporal resolution light curve from the Transiting Exoplanet Survey Satellite (TESS) covering the time of explosion; ultraviolet (UV) spectroscopy from the Hubble Space Telescope (HST) starting 3.3 days post-explosion; ground-based spectroscopic observations starting 1.1~days post-explosion; along with extensive photometric observations. Massive stars have complicated mass-loss histories leading up to their death as CCSNe, creating circumstellar medium (CSM) with which the SNe interact. Observations during the first few days post-explosion can provide important information about the mass-loss rate during the late stages of stellar evolution. Model fits to the quasi-bolometric light curve of SN 2020fqv reveal ~0.23 $M_{\odot}$ of CSM confined within ~1450 $R_{\odot}$ ($10^{14}$ cm) from its progenitor star. Early spectra (<4 days post-explosion), both from HST and ground-based observatories, show emission features from high-ionization metal species from the outer, optically thin part of this CSM. We find that the CSM is consistent with an eruption caused by the injection of $\sim$$5\times 10^{46}$ erg into the stellar envelope $\sim$300 days pre-explosion, potentially from a nuclear burning instability at the onset of oxygen burning. Light-curve fitting, nebular spectroscopy, and pre-explosion \textit{HST} imaging consistently point to a red supergiant (RSG) progenitor with $M_{\rm ZAMS}$$\approx$$13.5$--$15 \, M_{\odot}$, typical for SN~II progenitor stars. This finding demonstrates that a typical RSG, like the progenitor of SN 2020fqv, has a complicated mass-loss history immediately before core collapse.

astro-ph.SR↗

arXiv2021

The Galaxy Evolution Probe

The Galaxy Evolution Probe (GEP) is a concept for a mid- and far-infrared space observatory to measure key properties of large samples of galaxies with large and unbiased surveys. GEP will attempt to achieve zodiacal light and Galactic dust emission photon background-limited observations by utilizing a 6 Kelvin, 2.0 meter primary mirror and sensitive arrays of kinetic inductance detectors. It will have two instrument modules: a 10 - 400 micron hyperspectral imager with spectral resolution R = 8 (GEP-I) and a 24 - 193 micron, R = 200 grating spectrometer (GEP-S). GEP-I surveys will identify star-forming galaxies via their thermal dust emission and simultaneously measure redshifts using polycyclic aromatic hydrocarbon emission lines. Galaxy luminosities derived from star formation and nuclear supermassive black hole accretion will be measured for each source, enabling the cosmic star formation history to be measured to much greater precision than previously possible. Using optically thin far-infrared fine-structure lines, surveys with GEP-S will measure the growth of metallicity in the hearts of galaxies over cosmic time and extraplanar gas will be mapped in spiral galaxies in the local universe to investigate feedback processes. The science case and mission architecture designed to meet the science requirements are described, and the kinetic inductance detector and readout electronics state of the art and needed developments are described. This paper supersedes the GEP concept study report cited in it by providing new content, including: a summary of recent mid-infrared KID development, a discussion of microlens array fabrication for mid-infrared KIDs, and additional context for galaxy surveys. The reader interested in more technical details may want to consult the concept study report.

astro-ph.GA↗

arXiv2021

Getting ready for the LSST data -- estimating the physical properties of $z<2.5$ main sequence galaxies

In this work we study how to employ the upcoming Legacy Survey of Space and Time (LSST) data to constrain physical properties of normal, star forming galaxies. We use simulated LSST data and existing real observations to test the estimations of the physical properties of galaxies, such as star formation rate (SFR), stellar mass ($M_{star}$), and dust luminosity ($L_{dust}$). We focus on normal star-forming galaxies, as they form the majority of the galaxy population in the universe and therefore are more likely to be observed by the LSST. We perform a simulation of LSST observations and uncertainties of 50,385 real galaxies within redshift range $0<z<2.5$. In order to achieve this goal, we used the unique multi-wavelength data from the Herschel Extragalactic Legacy Project (HELP) survey. Our analysis focus on two fields: ELAIS-N1 and COSMOS. To obtain galaxy physical properties we fit their Spectral Energy Distributions (SEDs) using the Code Investigating GALaxy Emission (CIGALE). We compare the main galaxy physical properties obtained from the fit of the observed multi-wavelength photometry of galaxies (from UV to FIR) to the ones obtained from the simulated LSST optical measurements only. The stellar masses estimated based on the LSST measurements are in agreement with the full UV-FIR SED estimations, as they depend mainly on the UV and optical emission, well covered by LSST in the considered redshift range. We obtain a clear overestimation of SFR, $L_{dust}$, $M_{dust}$ estimated with LSST only, highly correlated with redshift. We investigate the cause of this overestimation and we conclude that it is related to an overestimation of the dust attenuation, both UV and NIR. We find that it is necessary to employ auxiliary rest-frame mid-infrared observations, simulated UV observations, or FUV attenuation (AFUV)- Mstar relation, to correct the overestimation.

astro-ph.GA↗

arXiv2020

SCUBA-2 Ultra Deep Imaging Eao Survey (Studies) III: Multi-wavelength properties, luminosity functions and preliminary source catalog of 450-$μ$m-selected galaxies

We construct a SCUBA-2 450-$μ$m map in the COSMOS field that covers an area of 300 arcmin$^{2}$ and reaches a 1$σ$ noise level of 0.65 mJy in the deepest region. We extract 256 sources detected at 450 $μ$m with signal-to-noise ratio $>$ 4.0 and analyze the physical properties of their multi-wavelength counterparts. We find that most of the sources are at $z\lesssim3$, with a median of $z = 1.79^{+0.03}_{-0.15}$. About $35^{+32}_{-25}$% of our sources are classified as starburst galaxies based on their total star-formation rates (SFRs) and stellar masses ($M_{\ast}$). By fitting the far-infrared spectral energy distributions, we find that our 450-$μ$m-selected sample has a wide range of dust temperatures (20 K $ \lesssim T_{\rm d} \lesssim$ 60 K), with a median of ${T}_{\rm d} = 38.3^{+0.4}_{-0.9}$ K. We do not find a redshift evolution in dust temperature for sources with $L_{\rm IR}$ > $10^{12}$ $\rm L_\odot$ at $z<3$. However, we find a moderate correlation where dust temperature increases with the deviation from the SFR-$M_{\ast}$ relation. The increase in dust temperature also correlates with optical morphology, which is consistent with merger-triggered starbursts in sub-millimeter galaxies. Our galaxies do not show the tight IRX-$β_{\rm UV}$ correlation that has been observed in the local Universe. We construct the infrared luminosity functions of our 450-$μ$m sources and measure their comoving SFR densities. The contribution of the $L_{\rm IR}$ > $10^{12}$ $\rm L_\odot$ population to the SFR density rises dramatically from $z$ = 0 to 2 ($\propto$ ($1+z$)$^{3.9\pm1.1}$) and dominates the total SFR density at $z \gtrsim 2$.

astro-ph.GA↗

arXiv2020

An empirical, Bayesian approach to modelling the impact of weather on crop yield: maize in the US

We apply an empirical, data-driven approach for describing crop yield as a function of monthly temperature and precipitation by employing generative probabilistic models with parameters determined through Bayesian inference. Our approach is applied to state-scale maize yield and meteorological data for the US Corn Belt from 1981 to 2014 as an exemplar, but would be readily transferable to other crops, locations and spatial scales. Experimentation with a number of models shows that maize growth rates can be characterised by a two-dimensional Gaussian function of temperature and precipitation with monthly contributions accumulated over the growing period. This approach accounts for non-linear growth responses to the individual meteorological variables, and allows for interactions between them. Our models correctly identify that temperature and precipitation have the largest impact on yield in the six months prior to the harvest, in agreement with the typical growing season for US maize (April to September). Maximal growth rates occur for monthly mean temperature 18-19$^\circ$C, corresponding to a daily maximum temperature of 24-25$^\circ$C (in broad agreement with previous work) and monthly total precipitation 115 mm. Our approach also provides a self-consistent way of investigating climate change impacts on current US maize varieties in the absence of adaptation measures. Keeping precipitation and growing area fixed, a temperature increase of $2^\circ$C, relative to 1981-2014, results in the mean yield decreasing by 8\%, while the yield variance increases by a factor of around 3. We thus provide a flexible, data-driven framework for exploring the impacts of natural climate variability and climate change on globally significant crops based on their observed behaviour. In concert with other approaches, this can help inform the development of adaptation strategies that will ensure food security under a changing climate.

physics.ao-ph↗

arXiv2019

The role of environment in galaxy evolution in the SERVS Survey I: density maps and cluster candidates

We use photometric redshifts derived from new $u$-band through 4.5$μ$m Spitzer IRAC photometry in the 4.8\,deg$^2$ of the XMM-LSS field to construct surface density maps in the redshift range 0.1-1.5. Our density maps show evidence for large-scale structure in the form of filaments spanning several tens of Mpc. Using these maps, we identify 339 overdensities that our simulated lightcone analysis suggests are likely associated with dark matter haloes with masses, $M_{\rm halo}$, log($M_{\rm halo}/M_{\odot})>$13.7. From this list of overdensities we recover 43 of 70 known X-ray detected and spectroscopically confirmed clusters. The missing X-ray clusters are largely at lower redshifts and lower masses than our target log($M_{\rm halo}/M_{\odot})>$13.7. The bulk of the overdensities are compact, but a quarter show extended morphologies which include likely projection effects, clusters embedded in apparent filaments as well as at least one potential cluster merger (at $z\sim1.28$). The strongest overdensity in our highest redshift slice (at $z\sim1.5$) shows a compact red galaxy core potentially implying a massive evolved cluster.

astro-ph.GA↗

arXiv2019

HELP: A catalogue of 170 million objects, selected at 0.36-4.5 $μ$m, from 1270 deg.$^{2}$ of prime extragalactic fields

We present an optical to near-infrared selected astronomical catalogue covering 1270 deg.$^2$. This is the first attempt to systematically combine data from 23 of the premier extragalactic survey fields - the product of a vast investment of telescope time. The fields are those imaged by the Herschel Space Observatory which form the Herschel Extragalactic Legacy Project (HELP). Our catalogue of 170 million objects is constructed by a positional cross match of 51 public surveys. This high resolution optical, near-infrared, and mid-infrared catalogue is designed for photometric redshift estimation, extraction of fluxes in lower resolution far-infrared maps, and spectral energy distribution modelling. It collates, standardises, and provides value added derived quantities including corrected aperture magnitudes and astrometry correction over the Herschel extragalactic wide fields for the first time. $grizy$ fluxes are available on all fields with $g$ band data reaching $5σ$ point-source depths in a 2 arcsec aperture of 23.5, 24.4, and 24.6 (AB) mag at the 25th, 50th, and 75th percentiles, by area covered, across all HELP fields. It has $K$ or $K_s$ coverage over 1146 deg.$^2$ with depth percentiles of 20.2, 20.4, and 21.0 mag respectively. The IRAC Ch 1 band is available over 273 deg.$^2$ with depth percentiles of 17.7, 21.4, and 22.2 mag respectively. This paper defines the "masterlist" objects for the first data release (DR1) of HELP. This large sample of standardised total and corrected aperture fluxes, uniform quality flags, and completeness measures provides large well understood statistical samples over the full Herschel extragalactic sky.

astro-ph.GA↗

arXiv2019

Research and Development for HI Intensity Mapping

Development of the hardware, data analysis, and simulation techniques for large compact radio arrays dedicated to mapping the 21 cm line of neutral hydrogen gas has proven to be more difficult than imagined twenty years ago when such telescopes were first proposed. Despite tremendous technical and methodological advances, there are several outstanding questions on how to optimally calibrate and analyze such data. On the positive side, it has become clear that the outstanding issues are purely technical in nature and can be solved with sufficient development activity. Such activity will enable science across redshifts, from early galaxy evolution in the pre-reionization era to dark energy evolution at low redshift.

astro-ph.IM↗

arXiv2019

HELP project - a dreamed-of multiwavelength dataset for SED fitting: the influence of used models for the main physical properties of galaxies

The Herschel Extragalactic Legacy Project (HELP) focuses to publish an astronomical multiwavelength catalogue of millions of objects over 1300~deg$^2$ of the Herschel Space Observatory survey fields. Millions of galaxies with ultraviolet--far infrared photometry {make} HELP a perfect sample for testing spectral energy distribution fitting models, and to prepare tools for next-generation data. In the frame of HELP collaboration we estimated the main physical properties of all galaxies from the HELP database and we checked a new procedure to select peculiar galaxies from large galaxy sample and we investigated the influence of used modules for stellar mass estimation.

astro-ph.GA↗