Data Deluge in Astrophysics: Photometric Redshifts as a Template Use Case

Astronomy has entered the big data era and Machine Learning based methods have found widespread use in a large variety of astronomical applications. This is demonstrated by the recent huge increase in the number of publications making use of this new approach. The usage of machine learning methods, however is still far from trivial and many problems still need to be solved. Using the evaluation of photometric redshifts as a case study, we outline the main problems and some ongoing efforts to solve them.Comment: 13 pages, 3 figures, Springer's Communications in Computer and Information Science (CCIS), Vol. 82

Euclid: Superluminous supernovae in the Deep Survey

Context. In the last decade, astronomers have found a new type of supernova called superluminous supernovae (SLSNe) due to their high peak luminosity and long light-curves. These hydrogen-free explosions (SLSNe-I) can be seen to z ~ 4 and therefore, offer the possibility of probing the distant Universe. Aims. We aim to investigate the possibility of detecting SLSNe-I using ESA’s Euclid satellite, scheduled for launch in 2020. In particular, we study the Euclid Deep Survey (EDS) which will provide a unique combination of area, depth and cadence over the mission. Methods. We estimated the redshift distribution of Euclid SLSNe-I using the latest information on their rates and spectral energy distribution, as well as known Euclid instrument and survey parameters, including the cadence and depth of the EDS. To estimate the uncertainties, we calculated their distribution with two different set-ups, namely optimistic and pessimistic, adopting different star formation densities and rates. We also applied a standardization method to the peak magnitudes to create a simulated Hubble diagram to explore possible cosmological constraints. Results. We show that Euclid should detect approximately 140 high-quality SLSNe-I to z ~ 3.5 over the first five years of the mission (with an additional 70 if we lower our photometric classification criteria). This sample could revolutionize the study of SLSNe-I at z > 1 and open up their use as probes of star-formation rates, galaxy populations, the interstellar and intergalactic medium. In addition, a sample of such SLSNe-I could improve constraints on a time-dependent dark energy equation-of-state, namely w(a), when combined with local SLSNe-I and the expected SN Ia sample from the Dark Energy Survey. Conclusions. We show that Euclid will observe hundreds of SLSNe-I for free. These luminous transients will be in the Euclid data-stream and we should prepare now to identify them as they offer a new probe of the high-redshift Universe for both astrophysics and cosmology.Acknowledgements. We thank the internal EC referees (P. Nugent and J. Brichmann) as well as the many comments from our EC colleagues and friends. C.I. thanks Chris Frohmaier and Szymon Prajs for useful discussions about supernova rates. C.I. and R.C.N. thank Mark Cropper for helpful information about the V IS instrument. C.I. thanks the organisers and participants of the Munich Institute for Astro- and Particle Physics (MIAPP) workshop “Superluminous supernovae in the next decade” for stimulating discussions and the provided online material. The Euclid Consortium acknowledges the European Space Agency and the support of a number of agencies and institutes that have supported the development of Euclid. A detailed complete list is available on the Euclid web site (http://www.euclid-ec.org). In particular the Agenzia Spaziale Italiana, the Centre National dEtudes Spatiales, the Deutsches Zentrum für Luft- and Raumfahrt, the Danish Space Research Institute, the Fundação para a Ciênca e a Tecnologia, the Ministerio de Economia y Competitividad, the National Aeronautics and Space Administration, The Netherlandse Onderzoekschool Voor Astronomie, the Norvegian Space Center, the Romanian Space Agency, the State Secretariat for Education, Research and Innovation (SERI) at the Swiss Space Office (SSO), the United Kingdom Space Agency, and the University of Helsinki. R.C.N. acknowledges partial support from the UK Space Agency. D.S. acknowledges the Faculty of Technology of the University of Portsmouth for support during his PhD studies. C.I. and S.J.S. acknowledge funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC Grant agreement No. [291222]. C.I. and M.S. acknowledge support from EU/FP7-ERC grant No. [615929]. E.C. acknowledge financial contribution from the agreement ASI/INAF/I/023/12/0. The work by KJ and others at MPIA on NISP was supported by the Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) under grant 50QE1202. M.B. and S.C. acknowledge financial contribution from the agreement ASI/INAF I/023/12/1. R.T. acknowledges funding from the Spanish Ministerio de Economía y Competitividad under the grant ESP2015-69020-C2- 2-R. I.T. acknowledges support from Fundação para a Ciência e a Tecnologia (FCT) through the research grant UID/FIS/04434/2013 and IF/01518/2014. J.R. was supported by JPL, which is run under a contract for NASA by Caltech and by NASA ROSES grant 12-EUCLID12-0004

The VOICE Survey : VST Optical Imaging of the CDFS and ES1 Fields

Indexación: Scopus.We present the VST Optical Imaging of the CDFS and ES1 Fields (VOICE) Survey, a VST INAF Guaranteed Time program designed to provide optical coverage of two 4 deg2 cosmic windows in the Southern hemisphere. VOICE provides the first, multi-band deep optical imaging of these sky regions, thus complementing and enhancing the rich legacy of longer-wavelength surveys with VISTA, Spitzer, Herschel and ATCA available in these areas and paving the way for upcoming observations with facilities such as the LSST, MeerKAT and the SKA. VOICE exploits VST's OmegaCAM optical imaging capabilities and completes the reduction of WFI data available within the ES1 fields as part of the ESO-Spitzer Imaging Extragalactic Survey (ESIS) program providing ugri and uBVR coverage of 4 and 4 deg2 areas within the CDFS and ES1 field respectively. We present the survey's science rationale and observing strategy, the data reduction and multi-wavelength data fusion pipeline. Survey data products and their future updates will be released at http://www.mattiavaccari.net/voice/ and on CDS/VizieR.https://pos.sissa.it/275/026/pd

Astroinformatics

As President of Commission on Astroinformatics and Astrostatistics of the International Astronomical Union, I welcome you to the first IAU Symposium on astroinformatics. This is not the first meeting in the field: the 26th meeting on ADASS (Astronomical Data Analysis Software and Systems) was held last weak in Trieste (and members of that group are here today), and this symposium has a strong heritage in workshops held in recent years at Caltech, Seattle, and Sydney. But this is the first time that the broader community of astronomers, through the IAU in collaboration of the giant IEEE organization has recognized this new field of study devoted to the challenges of Big Data and advanced methodology in astronomical research. This is the first time experts from around the world have gathered to share experiences and plan for the future. I have a comment to make. The typical IAU Symposium treats some well-established field of stars or galaxies or cosmology where the leading groups know each other well. But astroinformatics is such a young field, that we do not know each other and we do not know what ideas will emerge from this meeting. So I encourage each of us to have a creative approach to this meeting, work hard to talk to strangers, and help generate a community of scholars who can lead this field into the future

Supervised machine learning on Galactic filaments. Revealing the filamentary structure of the Galactic interstellar medium

Context. Filaments are ubiquitous in the Galaxy, and they host star formation. Detecting them in a reliable way is therefore key towards our understanding of the star formation process. Aims: We explore whether supervised machine learning can identify filamentary structures on the whole Galactic plane. Methods: We used two versions of UNet-based networks for image segmentation. We used H2 column density images of the Galactic plane obtained with Herschel Hi-GAL data as input data. We trained the UNet-based networks with skeletons (spine plus branches) of filaments that were extracted from these images, together with background and missing data masks that we produced. We tested eight training scenarios to determine the best scenario for our astrophysical purpose of classifying pixels as filaments. Results: The training of the UNets allows us to create a new image of the Galactic plane by segmentation in which pixels belonging to filamentary structures are identified. With this new method, we classify more pixels (more by a factor of 2 to 7, depending on the classification threshold used) as belonging to filaments than the spine plus branches structures we used as input. New structures are revealed, which are mainly low-contrast filaments that were not detected before. We use standard metrics to evaluate the performances of the different training scenarios. This allows us to demonstrate the robustness of the method and to determine an optimal threshold value that maximizes the recovery of the input labelled pixel classification. Conclusions: This proof-of-concept study shows that supervised machine learning can reveal filamentary structures that are present throughout the Galactic plane. The detection of these structures, including low-density and low-contrast structures that have never been seen before, offers important perspectives for the study of these filaments

Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks

We present a machine-learning photometric redshift analysis of the Kilo-Degree Survey Data Release 3, using two neural-network based techniques: ANNz2 and MLPQNA. Despite limited coverage of spectroscopic training sets, these ML codes provide photo-zs of quality comparable to, if not better than, those from the BPZ code, at least up to zphot<0.9 and r<23.5. At the bright end of r<20, where very complete spectroscopic data overlapping with KiDS are available, the performance of the ML photo-zs clearly surpasses that of BPZ, currently the primary photo-z method for KiDS. Using the Galaxy And Mass Assembly (GAMA) spectroscopic survey as calibration, we furthermore study how photo-zs improve for bright sources when photometric parameters additional to magnitudes are included in the photo-z derivation, as well as when VIKING and WISE infrared bands are added. While the fiducial four-band ugri setup gives a photo-z bias $\delta z=-2e-4$ and scatter $\sigma_z<0.022$ at mean z = 0.23, combining magnitudes, colours, and galaxy sizes reduces the scatter by ~7% and the bias by an order of magnitude. Once the ugri and IR magnitudes are joined into 12-band photometry spanning up to 12 $\mu$, the scatter decreases by more than 10% over the fiducial case. Finally, using the 12 bands together with optical colours and linear sizes gives $\delta z<4e-5$ and $\sigma_z<0.019$. This paper also serves as a reference for two public photo-z catalogues accompanying KiDS DR3, both obtained using the ANNz2 code. The first one, of general purpose, includes all the 39 million KiDS sources with four-band ugri measurements in DR3. The second dataset, optimized for low-redshift studies such as galaxy-galaxy lensing, is limited to r<20, and provides photo-zs of much better quality than in the full-depth case thanks to incorporating optical magnitudes, colours, and sizes in the GAMA-calibrated photo-z derivation.Comment: A&A, in press. Data available from the KiDS website http://kids.strw.leidenuniv.nl/DR3/ml-photoz.php#annz

A Comparison of Photometric Redshift Techniques for Large Radio Surveys

Future radio surveys will generate catalogs of tens of millions of radio sources, for which redshift estimates will be essential to achieve many of the science goals. However, spectroscopic data will be available for only a small fraction of these sources, and in most cases even the optical and infrared photometry will be of limited quality. Furthermore, radio sources tend to be at higher redshift than most optical sources (most radio surveys have a median redshift greater than 1) and so a significant fraction of radio sources hosts differ from those for which most photometric redshift templates are designed. We therefore need to develop new techniques for estimating the redshifts of radio sources. As a starting point in this process, we evaluate a number of machine-learning techniques for estimating redshift, together with a conventional template-fitting technique. We pay special attention to how the performance is affected by the incompleteness of the training sample and by sparseness of the parameter space or by limited availability of ancillary multiwavelength data. As expected, we find that the quality of the photometric-redshift degrades as the quality of the photometry decreases, but that even with the limited quality of photometry available for all-sky-surveys, useful redshift information is available for the majority of sources, particularly at low redshift. We find that a template-fitting technique performs best in the presence of high-quality and almost complete multi-band photometry, especially if radio sources that are also X-ray emitting are treated separately, using specific templates and priors. When we reduced the quality of photometry to match that available for the EMU all-sky radio survey, the quality of the template-fitting degraded and became comparable to some of the machine-learning methods. Machine learning techniques currently perform better at low redshift than at high redshift, because of incompleteness of the currently available training data at high redshifts

A comparison of photometric redshift techniques for large radio surveys

Future radio surveys will generate catalogs of tens of millions of radio sources, for which redshift estimates will be essential to achieve many of the science goals. However, spectroscopic data will be available for only a small fraction of these sources, and in most cases even the optical and infrared photometry will be of limited quality. Furthermore, radio sources tend to be at higher redshift than most optical sources (most radio surveys have a median redshift greater than 1) and so a significant fraction of radio sources hosts differ from those for which most photometric redshift templates are designed. We therefore need to develop new techniques for estimating the redshifts of radio sources. As a starting point in this process, we evaluate a number of machine-learning techniques for estimating redshift, together with a conventional template-fitting technique. We pay special attention to how the performance is affected by the incompleteness of the training sample and by sparseness of the parameter space or by limited availability of ancillary multiwavelength data. As expected, we find that the quality of the photometric-redshift degrades as the quality of the photometry decreases, but that even with the limited quality of photometry available for all-sky-surveys, useful redshift information is available for the majority of sources, particularly at low redshift. We find that a template-fitting technique performs best in the presence of high-quality and almost complete multi-band photometry, especially if radio sources that are also X-ray emitting are treated separately, using specific templates and priors. When we reduced the quality of photometry to match that available for the EMU all-sky radio survey, the quality of the template-fitting degraded and became comparable to some of the machine-learning methods. Machine learning techniques currently perform better at low redshift than at high redshift, because of incompleteness of the currently available training data at high redshifts

Vialactea Visual Analytics tool for Star Formation studies of the Galactic Plane

We present a visual analytics tool, based on the VisIVO suite, to exploit a combination of all new-generation surveys of the Galactic Plane to study the star formation process of the Milky Way. The tool has been developed within the VIALACTEA project, founded by the 7th Framework Programme of the European Union, that creates a common forum for the major new-generation surveys of the Milky Way Galactic Plane from the near infrared to the radio, both in thermal continuum and molecular lines. Massive volumes of data are produced by space missions and ground-based facilities and the ability to collect and store them is increasing at a higher pace than the ability to analyze them. This gap leads to new challenges in the analysis pipeline to discover information contained in the data. Visual analytics focuses on handling these massive, heterogeneous, and dynamic volumes of information accessing the data previously processed by data mining algorithms and advanced analysis techniques with highly interactive visual interfaces offering scientists the opportunity for in-depth understanding of massive, noisy, and high-dimensional data

Building the Largest Spectroscopic Sample of Ultracompact Massive Galaxies with the Kilo Degree Survey

Ultracompact massive galaxies (ucmgs), i.e., galaxies with stellar masses M∗ > 8× 10-10 M⊙ and effective radii R e< 1.5 kpc, are very rare systems, in particular at low and intermediate redshifts. Their origin as well as their number density across cosmic time are still under scrutiny, especially because of the paucity of spectroscopically confirmed samples. We have started a systematic census of ucmg candidates within the ESO Kilo Degree Survey, together with a large spectroscopic follow-up campaign to build the largest possible sample of confirmed ucmgs. This is the third paper of the series and the second based on the spectroscopic follow-up program. Here, we present photometrical and structural parameters of 33 new candidates at redshifts 0.15≲ z≲ 0.5 and confirm 19 of them as ucmgs, based on their nominal spectroscopically inferred M and R e. This corresponds to a success rate of ∼ 58%, nicely consistent with our previous findings. The addition of these 19 newly confirmed objects allows us to fully assess the systematics on the system selection-and to finally reduce the number density uncertainties. Moreover, putting together the results from our current and past observational campaigns and some literature data, we build the largest sample of ucmgs ever collected, comprising 92 spectroscopically confirmed objects at 0.1≲ z≲ 0.5. This number raises to 116, allowing for a 3σ tolerance on the M∗ and Re thresholds for the ucmg definition. For all these galaxies, we have estimated the velocity dispersion values at the effective radii, which have been used to derive a preliminary mass-velocity dispersion correlation