Limits on Polarized Dust Spectral Index Variations for CMB Foreground Analysis

Using Planck polarization data, we search for and constrain spatial variations of the polarized dust foreground for cosmic microwave background (CMB) observations, specifically in its spectral index, β d . Failure to account for such variations will cause errors in the foreground cleaning that propagate into errors on cosmological parameter recovery from the cleaned CMB map. It is unclear how robust prior studies of the Planck data that constrained β d variations are due to challenges with noise modeling, residual systematics, and priors. To clarify constraints on β d and its variation, we employ two pixel space analyses of the polarized dust foreground at >3.°7 scales on ≈60% of the sky at high Galactic latitudes. A template fitting method, which measures β d over three regions of ≈20% of the sky, does not find significant deviations from a uniform β d = 1.55, consistent with prior Planck determinations. An additional analysis in these regions, based on multifrequency fits to a dust and CMB model per pixel, puts limits on σ β d , the Gaussian spatial variation in β d . The data support σ β d up to 0.45 at the highest latitudes, 0.30 at midlatitudes, and 0.15 at low latitudes. We also demonstrate that care must be taken when interpreting the current Planck constraints, β d maps, and noise simulations. Due to residual systematics and low dust signal-to-noise ratios at high latitudes, forecasts for ongoing and future missions should include the possibility of large values of σ β d as estimated in this paper, based on current polarization data.

STUDY OF ENERGETIC TRANSIENTS USING TOOLS LIKE MESA & SNEC, MOSFIT AND SNCOSMO

We explore the study of energetic transients including core-collapse supernovae using various publicly available analysis tools like MESA & SNEC, MOSFiT and SNCOSMO. We used MESA to evolve a star having zero age main sequence mass (Mzams) of 24 M⊙ until the onset of core-collapse. Then we exploded this model using openly available explosion codes, STELLA & SNEC and obatined various observable parameters such as bolometric luminosity and photospheric velocities etc. We also used MOSFiT to model the light curve of a type Ic supernova, SN1999ex and obtained various physical parameters. SNCOSMO is used for template fitting of various supernovae by varying various parameters such as red shift, dust map, stretch factor of light curve, explosion epoch of supernova etc.

A machine learning approach to galaxy properties: joint redshift–stellar mass probability distributions with Random Forest

ABSTRACT We demonstrate that highly accurate joint redshift–stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. We build two ML models: one containing deep photometry in the griz bands, and the second reflecting the photometric scatter present in the main DES survey, with carefully constructed representative training data in each case. We validate our joint PDFs for 10 699 test galaxies by utilizing the copula probability integral transform and the Kendall distribution function, and their univariate counterparts to validate the marginals. Benchmarked against a basic set-up of the template-fitting code bagpipes, our ML-based method outperforms template fitting on all of our predefined performance metrics. In addition to accuracy, the RF is extremely fast, able to compute joint PDFs for a million galaxies in just under 6 min with consumer computer hardware. Such speed enables PDFs to be derived in real time within analysis codes, solving potential storage issues. As part of this work we have developed galpro 1, a highly intuitive and efficient python package to rapidly generate multivariate PDFs on-the-fly. galpro is documented and available for researchers to use in their cosmology and galaxy evolution studies.

Euclid preparation

Forthcoming large photometric surveys for cosmology require precise and accurate photometric redshift (photo-z) measurements for the success of their main science objectives. However, to date, no method has been able to produce photo-zs at the required accuracy using only the broad-band photometry that those surveys will provide. An assessment of the strengths and weaknesses of current methods is a crucial step in the eventual development of an approach to meet this challenge. We report on the performance of 13 photometric redshift code single value redshift estimates and redshift probability distributions (PDZs) on a common set of data, focusing particularly on the 0.2 − 2.6 redshift range that the Euclid mission will probe. We designed a challenge using emulated Euclid data drawn from three photometric surveys of the COSMOS field. The data was divided into two samples: one calibration sample for which photometry and redshifts were provided to the participants; and the validation sample, containing only the photometry to ensure a blinded test of the methods. Participants were invited to provide a redshift single value estimate and a PDZ for each source in the validation sample, along with a rejection flag that indicates the sources they consider unfit for use in cosmological analyses. The performance of each method was assessed through a set of informative metrics, using cross-matched spectroscopic and highly-accurate photometric redshifts as the ground truth. We show that the rejection criteria set by participants are efficient in removing strong outliers, that is to say sources for which the photo-z deviates by more than 0.15(1 + z) from the spectroscopic-redshift (spec-z). We also show that, while all methods are able to provide reliable single value estimates, several machine-learning methods do not manage to produce useful PDZs. We find that no machine-learning method provides good results in the regions of galaxy color-space that are sparsely populated by spectroscopic-redshifts, for example z > 1. However they generally perform better than template-fitting methods at low redshift (z < 0.7), indicating that template-fitting methods do not use all of the information contained in the photometry. We introduce metrics that quantify both photo-z precision and completeness of the samples (post-rejection), since both contribute to the final figure of merit of the science goals of the survey (e.g., cosmic shear from Euclid). Template-fitting methods provide the best results in these metrics, but we show that a combination of template-fitting results and machine-learning results with rejection criteria can outperform any individual method. On this basis, we argue that further work in identifying how to best select between machine-learning and template-fitting approaches for each individual galaxy should be pursued as a priority.

A SCUBA-2 selected Herschel-SPIRE dropout and the nature of this population

ABSTRACT Dusty star-forming galaxies (DSFGs) detected at z &gt; 4 provide important examples of the first generations of massive galaxies. However, few examples with spectroscopic confirmation are currently known, with Hershel struggling to detect significant numbers of z &gt; 6 DSFGs. NGP6_D1 is a bright 850 $\mathrm{ \mu}$m source (12.3 ± 2.5 mJy) with no counterparts at shorter wavelengths (a SPIRE dropout). Interferometric observations confirm it is a single source, with no evidence for any optical or NIR emission, or nearby likely foreground lensing sources. No &gt;3σ detected lines are seen in both LMT Redshift Search Receiver and IRAM 30 m EMIR spectra of NGP6_D1 across 32 GHz of bandwidth despite reaching detection limits of $\sim 1\, \mathrm{mJy}/500 \, \mathrm{km~s}^{-1}$, so the redshift remains unknown. Template fitting suggests that NGP6_D1 is most likely between z = 5.8 and 8.3. SED analysis finds that NGP6_D1 is a ULIRG, with a dust mass ∼108–109 M⊙ and a star-formation rate of ∼500 M⊙ yr−1. We place upper limits on the gas mass of NGP6_D1 of MH2 &lt;(1.1 ± 3.5) × 1011 M⊙, consistent with a gas-to-dust ratio of ∼100–1000. We discuss the nature of NGP6_D1 in the context of the broader sub-mm population, and find that comparable SPIRE dropouts account for ∼20 per cent of all SCUBA-2 detected sources, but with a similar flux density distribution to the general population.

Lick Observatory Supernova Search follow-up program: photometry data release of 93 Type Ia supernovae

ABSTRACT We present BVRI and unfiltered light curves of 93 Type Ia supernovae (SNe Ia) from the Lick Observatory Supernova Search (LOSS) follow-up program conducted between 2005 and 2018. Our sample consists of 78 spectroscopically normal SNe Ia, with the remainder divided between distinct subclasses (3 SN 1991bg-like, 3 SN 1991T-like, 4 SNe Iax, 2 peculiar, and 3 super-Chandrasekhar events), and has a median redshift of 0.0192. The SNe in our sample have a median coverage of 16 photometric epochs at a cadence of 5.4 d, and the median first observed epoch is ∼4.6 d before maximum B-band light. We describe how the SNe in our sample are discovered, observed, and processed, and we compare the results from our newly developed automated photometry pipeline to those from the previous processing pipeline used by LOSS. After investigating potential biases, we derive a final systematic uncertainty of 0.03 mag in BVRI for our data set. We perform an analysis of our light curves with particular focus on using template fitting to measure the parameters that are useful in standardizing SNe Ia as distance indicators. All of the data are available to the community, and we encourage future studies to incorporate our light curves in their analyses.