Imaging surveys will find many tens to hundreds of thousands of Type Ia supernovae in the next decade, and measure their light curves. In addition to a need for characterizing their types and subtypes, a redshift is required to place them on a Hubble diagram to map the cosmological expansion. We investigate the requirements on redshift systematics control in order not to bias cosmological results, in particular dark energy parameter estimation. We find that additive and multiplicative systematics must be constrained at the few$\,\times 10^{-3}$ level, effectively requiring spectroscopic followup for robust use of photometric supernovae. Catastrophic outliers need control at the subpercent level. We also investigate sculpting the spectroscopic sample.
We present a method using the SALT2 light curve fitter to determine the redshift of Type Ia supernovae in the Supernova Legacy Survey (SNLS) based on their photometry in g', r', i' and z'. On 289 supernovae of the first three years of SNLS data, we obtain a precision $σ_{Δz/(1+z)} = 0.022$ on average up to a redshift of 1.0, with a higher precision of 0.016 for z<0.45 and a lower one of 0.025 for z>0.45. The rate of events with $|Δz|/(1+z)>0.15$ (catastrophic errors) is 1.4%. Both the precision and the rate of catastrophic errors are better than what can be currently obtained using host galaxy…
We present a sample of 485 photometrically identified Type Ia supernova candidates mined from the first three years of data of the CFHT SuperNova Legacy Survey (SNLS). The images were submitted to a deferred processing independent of the SNLS real-time detection pipeline. Light curves of all transient events were reconstructed in the g_M, r_M, i_M and z_M filters and submitted to automated sequential cuts in order to identify possible supernovae. Pure noise and long-term variable events were rejected by light curve shape criteria. Type Ia supernova identification relied on event characteristic…
The case of SN Ia Hubble diagrams from photometrically selected samples using photometric SN host galaxy redshifts is investigated. The host redshift uncertainties and the contamination by core collapse SNe are addressed. As a test, we use the 3-year photometric SN Ia sample of the SuperNova Legacy Survey (SNLS), made of 437 objects between 0.1 and 1.05 in redshift. We combine this sample with non-SNLS objects of the JLA spectroscopic sample, made of 501 objects mostly below 0.4 in redshift. We study two options for the origin of the redshifts of the photometric sample, either provided entirel…
Large planned photometric surveys will discover hundreds of thousands of supernovae (SNe), outstripping the resources available for spectroscopic follow-up and necessitating the development of purely photometric methods to exploit these events for cosmological study. We present a light-curve fitting technique for SN Ia photometric redshift (photo-z) estimation in which the redshift is determined simultaneously with the other fit parameters. We implement this "LCFIT+Z" technique within the frameworks of the MLCS2k2 and SALT-II light-curve fit methods and determine the precision on the redshift …
We present photometric properties and distance measurements of 252 high redshift Type Ia supernovae (0.15 < z < 1.1) discovered during the first three years of the Supernova Legacy Survey (SNLS). These events were detected and their multi-colour light curves measured using the MegaPrime/MegaCam instrument at the Canada-France-Hawaii Telescope (CFHT), by repeatedly imaging four one-square degree fields in four bands. Follow-up spectroscopy was performed at the VLT, Gemini and Keck telescopes to confirm the nature of the supernovae and to measure their redshifts. Systematic uncertainties arising…
We discuss the extent to which photometric measurements alone can be used to identify Type Ia supernovae (SNIa) and to determine redshift and other parameters of interest for cosmological studies. We fit the light curve data of the type expected from a survey such as the one planned with Large Synoptic Survey Telescope (LSST) and also to remove the contamination from the core-collapse supernovae to SNIa samples. We generate 1000 SNIa mock flux data for each of the LSST filters based on existing design parameters, then use a Markov Chain Monte-Carlo (MCMC) analysis to fit for the redshift, appa…
Much of the science that is made possible by multiwavelength redshift surveys requires the use of photometric redshifts. But as these surveys become more ambitious, and as we seek to perform increasingly accurate measurements, it becomes crucial to take proper account of the photometric redshift uncertainties. Ideally the uncertainties can be directly measured using a comparison to spectroscopic redshifts, but this may yield misleading results since spectroscopic samples are frequently small and not representative of the parent photometric samples. We present a simple and powerful empirical me…
A rare class of `super-luminous' supernovae that are about ten or more times more luminous at their peaks than other types of luminous supernovae has recently been found at low to intermediate redshifts. A small subset of these events have luminosities that evolve slowly and result in radiated energies of around 10^51 ergs or more. Therefore, they are likely examples of `pair-instability' or `pulsational pair-instability' supernovae with estimated progenitor masses of 100 - 250 times that of the Sun. These events are exceedingly rare at low redshift, but are expected to be more common at high …
Improvement in the precision of measurements of cosmological parameters with Type Ia Supernovae (SNIa) is expected to come from large photometrically identified (photometric) SN samples. Here we re-analyse the SDSS photometric SN sample, with roughly 700 high-quality, likely but unconfirmed SNIa light-curves, to develop new analysis tools aimed at evaluating systematic uncertainties on the dark energy equation-of-state parameter $w$. Since we require a spectroscopically measured host galaxy redshift for each SN, we determine the associated selection efficiency of host galaxies in order to simu…
Accurately characterizing the redshift distributions of galaxies is essential for analysing deep photometric surveys and testing cosmological models. We present a technique to simultaneously infer redshift distributions and individual redshifts from photometric galaxy catalogues. Our model constructs a piecewise constant representation (effectively a histogram) of the distribution of galaxy types and redshifts, the parameters of which are efficiently inferred from noisy photometric flux measurements. This approach can be seen as a generalization of template-fitting photometric redshift methods…
We present results of using a basic binary classification neural network model to identify likely catastrophic outlier photometric redshift estimates of individual galaxies, based only on the galaxies' measured photometric band magnitude values. We find that a simple implementation of this classification can identify a significant fraction of galaxies with catastrophic outlier photometric redshift estimates while falsely categorizing only a much smaller fraction of non-outliers. These methods have the potential to reduce the errors introduced into science analyses by catastrophic outlier photo…
We present a new method for inferring photometric redshifts in deep galaxy and quasar surveys, based on a data driven model of latent spectral energy distributions (SEDs) and a physical model of photometric fluxes as a function of redshift. This conceptually novel approach combines the advantages of both machine-learning and template-fitting methods by building template SEDs directly from the training data. This is made computationally tractable with Gaussian Processes operating in flux--redshift space, encoding the physics of redshift and the projection of galaxy SEDs onto photometric band pa…
We present supernova rate measurements at redshift 0.1-1.0 from the Stockholm VIMOS Supernova Survey (SVISS). The sample contains 16 supernovae in total. The discovered supernovae have been classified as core collapse or type Ia supernovae (9 and 7, respectively) based on their light curves, colour evolution and host galaxy photometric redshift. The rates we find for the core collapse supernovae are 3.29 (-1.78,-1.45)(+3.08,+1.98) x 10^-4 yr^-1 Mpc^-3 h70^3 (with statistical and systematic errors respectively) at average redshift 0.39 and 6.40 (-3.12,-2.11)(+5.30,+3.65) x 10^-4 yr^-1 Mpc^-3 h7…
We present improved photometric supernovae classification using deep recurrent neural networks. The main improvements over previous work are (i) the introduction of a time gate in the recurrent cell that uses the observational time as an input; (ii) greatly increased data augmentation including time translation, addition of Gaussian noise and early truncation of the lightcurve. For post Supernovae Photometric Classification Challenge (SPCC) data, using a training fraction of $5.2\%$ (1103 supernovae) of a representational dataset, we obtain a type Ia vs. non type Ia classification accuracy of …
Filament finders are limited, among other things, by the abundance of spectroscopic redshift data. As there are proportionally more photometric redshift data than spectroscopic, we aim to use photometric data to improve and expand the areas where we can detect the large-scale structure of the Universe. We present a proof of concept, showing that the Bisous filament finder can improve the detected filamentary network with photometric redshift data. We created mock data from the MultiDark-Galaxies catalogue. Galaxies with spectroscopic redshifts were given exact positions from the simulation. Ga…
We present results from the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) photometric redshift methods investigation. In this investigation, the results from eleven participants, each using a different combination of photometric redshift code, template spectral energy distributions (SEDs) and priors, are used to examine the properties of photometric redshifts applied to deep fields with broad-band multi-wavelength coverage. The photometry used includes U-band through mid-infrared filters and was derived using the TFIT method. Comparing the results, we find that there…
The Carnegie Supernova Project (CSP) is a five-year survey being carried out at the Las Campanas Observatory to obtain high-quality light curves of ~100 low-redshift Type Ia supernovae in a well-defined photometric system. Here we present the first release of photometric data that contains the optical light curves of 35 Type Ia supernovae, and near-infrared light curves for a subset of 25 events. The data comprise 5559 optical (ugriBV) and 1043 near-infrared (YJHKs) data points in the natural system of the Swope telescope. Twenty-eight supernovae have pre-maximum data, and for 15 of these, the…
Cosmology with Type Ia supernovae heretofore has required extensive spectroscopic follow-up to establish a redshift. Though tolerable at the present discovery rate, the next generation of ground-based all-sky survey instruments will render this approach unsustainable. Photometry-based redshift determination is a viable alternative, but introduces non-negligible errors that ultimately degrade the ability to discriminate between competing cosmologies. We present a strictly template-based photometric redshift estimator and compute redshift reconstruction errors in the presence of photometry and s…
We use simulations to demonstrate that photometric redshift "errors" can be greatly reduced by using the photometric redshift probability distribution p(z) rather than a one-point estimate such as the most likely redshift. In principle this involves tracking a large array of numbers rather than a single number for each galaxy. We introduce a very simple estimator that requires tracking only a single number for each galaxy, while retaining the systematic-error-reducing properties of using the full p(z) and requiring only very minor modifications to existing photometric redshift codes. We find t…
In addition to the maximum likelihood approach, there are two other methods which are commonly used to reconstruct the true redshift distribution from photometric redshift datasets: one uses a deconvolution method, and the other a convolution. We show how these two techniques are related, and how this relationship can be extended to include the study of galaxy scaling relations in photometric datasets. We then show what additional information photometric redshift algorithms must output so that they too can be used to study galaxy scaling relations, rather than just redshift distributions. We a…
We describe the implementation of the PhotoZ code in the framework of the Astro-WISE package and as part of the Photometric Classification Server of the PanSTARRS pipeline. Both systems allow the automatic measurement of photometric redshifts for the millions of objects being observed in the PanSTARRS project or expected to be observed by future surveys like KIDS, DES or EUCLID.
We describe a technique for simultaneously classifying and estimating the redshift of quasars. It can separate quasars from stars in arbitrary redshift ranges, estimate full posterior distribution functions for the redshift, and naturally incorporate flux uncertainties, missing data, and multi-wavelength photometry. We build models of quasars in flux-redshift space by applying the extreme deconvolution technique to estimate the underlying density. By integrating this density over redshift one can obtain quasar flux-densities in different redshift ranges. This approach allows for efficient, con…
We have revised the SWIRE Photometric Redshift Catalogue to take account of new optical photometry in several of the SWIRE areas, and incorporating 2MASS and UKIDSS near infrared data. Aperture matching is an important issue for combining near infrared and optical data, and we have explored a number of methods of doing this. The increased number of photometric bands available for the redshift solution results in improvements both in the rms error and, especially, in the outlier rate. We have also found that incorporating the dust torus emission into the QSO templates improves the performance f…
We examine the redshifts of a comprehensive set of published Type Ia supernovae, and provide a combined, improved catalogue with updated redshifts. We improve on the original catalogues by using the most up-to-date heliocentric redshift data available; ensuring all redshifts have uncertainty estimates; using the exact formulae to convert heliocentric redshifts into the Cosmic Microwave Background (CMB) frame; and utilising an improved peculiar velocity model that calculates local motions in redshift-space and more realistically accounts for the external bulk flow at high-redshifts. In total we…
Imaging surveys of galaxies will have a high number density and angular resolution yet a poor redshift precision. Intensity maps of neutral hydrogen (HI) will have accurate redshift resolution yet will not resolve individual sources. Using this complementarity, we show how the clustering redshifts approach, proposed for spectroscopic surveys can also be used in combination with intensity mapping observations to calibrate the redshift distribution of galaxies in an imaging survey and, as a result, reduce uncertainties in photometric redshift measurements. We show how the intensity mapping surve…
The uncertainty in the redshift distributions of galaxies has a significant potential impact on the cosmological parameter values inferred from multi-band imaging surveys. The accuracy of the photometric redshifts measured in these surveys depends not only on the quality of the flux data, but also on a number of modeling assumptions that enter into both the training set and SED fitting methods of photometric redshift estimation. In this work we focus on the latter, considering two types of modeling uncertainties: uncertainties in the SED template set and uncertainties in the magnitude and type…
We present a new photometric identification technique for SN 1991bg-like type Ia supernovae (SNe Ia), i.e. objects with light-curve characteristics such as later primary maxima and absence of secondary peak in redder filters. This method is capable of selecting out this sub-group from the normal type Ia population. Furthermore, we find that recently identified peculiar sub-types such as SNe Iax and super-Chandrasekhar SNe Ia have similar photometric characteristics as 91bg-like SNe Ia, namely the absence of secondary maxima and shoulders at longer wavelengths, and can also be classified with o…
We present the temporal evolution of line profiles ranging from near ultraviolet to optical wavelengths by analyzing 59 Subaru telescope spectra of normal Type Ia Supernovae (SNe Ia) in the intermediate redshift range (0.05 < z < 0.4) discovered by the Sloan Digital Sky Survey-II (SDSS-II) Supernova Survey. We derive line velocities, peak wavelengths and pseudo-equivalent widths (pEWs) of these lines. Additionally, we compare the line profiles around the date of maximum brightness with those from their nearby counterparts. We find that line profiles represented by their velocities and pEWs for…
The next generation of proposed galaxy surveys will increase the number of galaxies with photometric redshifts by two orders of magnitude, drastically expanding both redshift range and detection threshold from the current state of the art. Obtaining spectra for a fair sub-sample of this new data could be cumbersome and expensive. However, adequate calibration of the true redshift distribution of galaxies is vital to tapping the potential of these surveys. We examine a promising alternative to direct spectroscopic follow up: calibration of the redshift distribution of photometric galaxies via c…
We present a catalogue of galaxy photometric redshifts and k-corrections for the Sloan Digital Sky Survey Seven Data Release (SDSS-DR7), available on the World Wide Web. The photometric redshifts were estimated with an artificial neural network using five ugriz bands, concentration indices and Petrosian radii in the g and r bands. We have explored our redshift estimates with different training set concluding that the best choice to improve redshift accuracy comprises the Main Galaxies Sample (MGS), the Luminous Red Galaxies, and galaxies of active galactic nuclei covering the redshift range 0<…
With the growth of large photometric surveys, accurately estimating photometric redshifts, preferably as a probability density function (PDF), and fully understanding the implicit systematic uncertainties in this process has become increasingly important. In this paper, we present a new, publicly available, parallel, machine learning algorithm that generates photometric redshift PDFs by using prediction trees and random forest techniques, which we have named TPZ. This new algorithm incorporates measurement errors into the calculation while also dealing efficiently with missing values in the da…
We present broad band photometry and photometric redshifts for 187611 sources located in ~0.5deg^2 in the Lockman Hole area. The catalog includes 389 X-ray detected sources identified with the very deep XMM-Newton observations available for an area of 0.2 deg^2. The source detection was performed on the Rc, z' and B band images and the available photometry is spanning from the far ultraviolet to the mid infrared, reaching in the best case scenario 21 bands. Astrometry corrections and photometric cross-calibrations over the entire dataset allowed the computation of accurate photometric redshift…
We introduce an ordinal classification algorithm for photometric redshift estimation, which significantly improves the reconstruction of photometric redshift probability density functions (PDFs) for individual galaxies and galaxy samples. As a use case we apply our method to CFHTLS galaxies. The ordinal classification algorithm treats distinct redshift bins as ordered values, which improves the quality of photometric redshift PDFs, compared with non-ordinal classification architectures. We also propose a new single value point estimate of the galaxy redshift, that can be used to estimate the f…
We present an improved photometric redshift estimator code, CuBAN$z$, that is publicly available at https://goo.gl/fpk90V}{https://goo.gl/fpk90V. It uses the back propagation neural network along with clustering of the training set, which makes it more efficient than existing neural network codes. In CuBAN$z$, the training set is divided into several self learning clusters with galaxies having similar photometric properties and spectroscopic redshifts within a given span. The clustering algorithm uses the color information (i.e. $u-g$, $g-r$ etc.) rather than the apparent magnitudes at various…
There are many kinds of models which describe the dynamics of dark energy (DE). Among all we adopt an equation of state (EoS) which varies as a function of time. We adopt Markov Chain Monte Carlo method to constrain the five parameters of our models. As a consequence, we can show the characteristic behavior of DE during the evolution of the universe. We constrain the EoS of DE with use of the avairable data of gamma-ray bursts and type Ia supernovae (SNe Ia) concerning the redshift-luminosity distance relations. As a result, we find that DE is quintessence-like in the early time and phantom-li…
We determine the accuracy of galaxy redshift distributions as estimated from photometric redshift probability distributions $p(z)$. Our method utilises measurements of the angular cross-correlation between photometric galaxies and an overlapping sample of galaxies with spectroscopic redshifts. We describe the redshift leakage from a galaxy photometric redshift bin $j$ into a spectroscopic redshift bin $i$ using the sum of the $p(z)$ for the galaxies residing in bin $j$. We can then predict the angular cross-correlation between photometric and spectroscopic galaxies due to intrinsic galaxy clus…
We investigate the evolution of the galaxy two point correlation function (CF) over a wide redshift range, 0.2 < z < 3. For the first time the systematic analysis covers the redshifts above 1 - 1.5. The catalogue of ~250000 galaxies with i+ < 25 and known photometric redshifts in the Subaru Deep field is used. The galaxies are divided into three luminosity classes and several distance/redshift bins. First, the 2D CF is determined for each luminosity class and distance bin. Calculations are based on the quantitative differences between the surface distributions of galaxy pairs with comparable a…
It has recently been demonstrated that one can accurately derive galaxy morphology from particular primary and secondary isophotal shape estimates in the Sloan Digital Sky Survey imaging catalog. This was accomplished by applying Machine Learning techniques to the Galaxy Zoo morphology catalog. Using the broad bandpass photometry of the Sloan Digital Sky Survey in combination with with precise knowledge of galaxy morphology should help in estimating more accurate photometric redshifts for galaxies. Using the Galaxy Zoo separation for spirals and ellipticals in combination with Sloan Digital Sk…
We report our first discoveries of high-redshift supernovae from the Subaru HIgh-Z sUpernova CAmpaign (SHIZUCA), the transient survey using Subaru/Hyper Suprime-Cam. We report the discovery of three supernovae at the spectroscopically-confirmed redshifts of 2.399 (HSC16adga), 1.965 (HSC17auzg), and 1.851 (HSC17dbpf), and two supernova candidates with the host-galaxy photometric redshifts of 3.2 (HSC16apuo) and 4.2 (HSC17dsid), respectively. In this paper, we present their photometric properties and the spectroscopic properties of the confirmed high-redshift supernovae are presented in the acco…
The Vera C. Rubin Observatory will increase the number of observed supernovae (SNe) by an order of magnitude; however, it is impossible to spectroscopically confirm the class for all the SNe discovered. Thus, photometric classification is crucial but its accuracy depends on the not-yet-finalized observing strategy of Rubin Observatory's Legacy Survey of Space and Time (LSST). We quantitatively analyze the impact of the LSST observing strategy on SNe classification using simulated multi-band light curves from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC). Fir…
In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a method to photometrically classify type Ia supernovae based on machine learning with redshifts that are derived from the SN light-curves. This method is implemented on real data from the SNLS deferred pipeline, a purely photometric pipeline that identifies SNe Ia at high-redshifts ($0.2<z<1.1$). Our method consists of two stages: feature extraction (obtaining …
We present broadband photometric and polarimetric observations of two type II supernovae (SNe) 2013hj and 2014G. SN 2014G is a spectroscopically classified type IIL event, which we also confirm photometrically as its light curve show characteristic features (plateau slope of 2.55 mag (100 d)$ ^{-1} $ in V-band and duration of $ \sim77 $d) of a generic IIL SN. On the other hand SN 2013hj also shows high plateau decline rate of $ 1.5 $ mag (100 d)$ ^{-1} $ in V-band, similar to SNe IIL, but marginally lower than SNe IIL template light curves. Our high cadence photometric observations of SNe 2013…
We study a sample of 23 Type II Plateau supernovae (SNe II-P), all observed with the same set of instruments. Analysis of their photometric evolution confirms that their typical plateau duration is 100 days with little scatter, showing a tendency to get shorter for more energetic SNe. The rise time from explosion to plateau does not seem to correlate with luminosity. We analyze their spectra, measuring typical ejecta velocities, and confirm that they follow a well behaved power-law decline. We find indications of high-velocity material in the spectra of six of our SNe. We test different dust e…
Redshift measurement has always been a constant need in modern astronomy and cosmology. And as new surveys have been providing an immense amount of data on astronomical objects, the need to process such data automatically proves to be increasingly necessary. In this article, we use simulated data from the Dark Energy Survey, and from a pipeline originally created to classify supernovae, we developed a linear regression algorithm optimized through novel automated machine learning (AutoML) frameworks achieving an error score better than ordinary data pre-processing methods when compared with oth…
Supernova surveys can be used to study a variety of subjects such as: (i) cosmology through type Ia supernovae (SNe), (ii) star-formation rates through core-collapse SNe, and (iii) supernova properties and their connection to host galaxy characteristics. The Stockholm VIMOS Supernova Survey (SVISS) is a multi-band imaging survey aiming to detect supernovae at redshift ~0.5 and derive thermonuclear and core-collapse supernova rates at high redshift. In this paper we present the supernovae discovered in the survey along with light curves and a photometric classification into thermonuclear and co…
The Brans-Dicke model with a variable cosmological term ($BDΛ$) has been investigated with use of the coupling constant of $ω=10^4$. Parameters inherent in this model are constrained from comparison between Big Bang nucleosynthesis and the observed abundances. Furthermore, the magnitude redshift ($m-z$) relations are studied for $BDΛ$ with and without another constant cosmological term in a flat universe. Observational data of Type Ia Supernovae are used in the redshift range of $0.01<z<2$. It is found that our model with energy density of the constant cosmological term with the value of 0.7 c…
This paper describes the aims, objectives and first results of the observational program for the study of distant core-collapse supernovae (SNe) with redshifts z < 0.3. This work is done within the framework of an international cooperation program on the SNe monitoring at the 6-m BTA telescope of the Special Astrophysical Observatory of the Russian Academy of Sciences, and other telescopes. We study both the early phases of events (SN type determination, redshift estimation, and a search for manifestations of a wind envelope), and the nebular phase (the effects of explosion asymmetry). The SNe…
Future cosmological galaxy surveys such as the Large Synoptic Survey Telescope (LSST) will photometrically observe very large numbers of galaxies. Without spectroscopy, the redshifts required for the analysis of these data will need to be inferred using photometric redshift techniques that are scalable to large sample sizes. The high number density of sources will also mean that around half are blended. We present a Bayesian photometric redshift method for blended sources that uses Gaussian mixture models to learn the joint flux-redshift distribution from a set of unblended training galaxies, …
We apply clustering-based redshift inference to all extended sources from the Sloan Digital Sky Survey photometric catalogue, down to magnitude r = 22. We map the relationships between colours and redshift, without assumption of the sources' spectral energy distributions (SED). We identify and locate star-forming, quiescent galaxies, and AGN, as well as colour changes due to spectral features, such as the 4000 Å break, redshifting through specific filters. Our mapping is globally in good agreement with colour-redshift tracks computed with SED templates, but reveals informative differences, suc…
Analysis of type 1a supernovae observations out to a redshift of $z$=1.6 shows that there is good agreement between the light-curve widths and $(1+z)$ which is usually interpreted as a strong support for time dilation due to an expanding universe. This paper argues that a strong case can be made for a static universe where the supernovae light-curve-width dependence on redshift is due to selection effects. The analysis is based on the principle that it is the total energy (the fluence) and not the peak magnitude that is the best `standard candle' for type 1a supernovae. A simple model using a …
In this work, we propose the use of Kernel Principal Component Analysis (KPCA) combined with k = 1 nearest neighbour algorithm (1NN) as a framework for supernovae (SNe) photometric classification. The classification is entirely based on information within the spectroscopic confirmed sample and each new light curve is classified one at a time. This allows us to update the principal component (PC) parameter space if a new spectroscopic light curve is available while also avoids the need of re-determining it for each individual new classification. We applied the method to different instances of t…
We introduce Z-Sequence, a novel empirical model that utilises photometric measurements of observed galaxies within a specified search radius to estimate the photometric redshift of galaxy clusters. Z-Sequence itself is composed of a machine learning ensemble based on the k-nearest neighbours algorithm. We implement an automated feature selection strategy that iteratively determines appropriate combinations of filters and colours to minimise photometric redshift prediction error. We intend for Z-Sequence to be a standalone technique but it can be combined with cluster finders that do not intri…
The efficient classification of different types of supernova is one of the most important problems for observational cosmology. However, spectroscopic confirmation of most objects in upcoming photometric surveys, such as the The Rubin Observatory Legacy Survey of Space and Time (LSST), will be unfeasible. The development of automated classification processes based on photometry has thus become crucial. In this paper we investigate the performance of machine learning (ML) classification on the final cosmological constraints using simulated lightcurves from The Supernova Photometric Classificati…
We study the relation of Type Ia Supernovae (SNe Ia) with host galaxies from a large low redshift sample. By examining the Hubble residuals of the entire sample from the best-fit cosmology, we show that SNe Ia in passive hosts are brighter than those in star-forming hosts after light curve correction at 2.1σconfidence level. We find that SNe Ia in high luminosity hosts are brighter after light-curve correction at>3σconfidence level. We also find that SNe Ia in large size galaxies are brighter after light-curve correction at?2σ confidence level. We demonstrate that the residual linearly depends…
Measuring environment for large numbers of distant galaxies is still an open problem, for which we need galaxy positions and redshifts. Photometric redshifts are more easily available for large numbers of galaxies, but at the price of larger uncertainties than spectroscopic ones. In this work we study how photometric redshifts affect the measurement of galaxy environment and how this may limit an analysis of the galaxy stellar mass function (GSMF) in different environments. Using mock galaxy catalogues, we measured the environment with a fixed aperture method, using each galaxy's true and phot…
