text:
• ### Return of the features. Efficient feature selection and interpretation for photometric redshifts(1803.10032)

May 9, 2018 astro-ph.IM
The explosion of data in recent years has generated an increasing need for new analysis techniques in order to extract knowledge from massive datasets. Machine learning has proved particularly useful to perform this task. Fully automatized methods have recently gathered great popularity, even though those methods often lack physical interpretability. In contrast, feature based approaches can provide both well-performing models and understandable causalities with respect to the correlations found between features and physical processes. Efficient feature selection is an essential tool to boost the performance of machine learning models. In this work, we propose a forward selection method in order to compute, evaluate, and characterize better performing features for regression and classification problems. Given the importance of photometric redshift estimation, we adopt it as our case study. We synthetically created 4,520 features by combining magnitudes, errors, radii, and ellipticities of quasars, taken from the SDSS. We apply a forward selection process, a recursive method in which a huge number of feature sets is tested through a kNN algorithm, leading to a tree of feature sets. The branches of the tree are then used to perform experiments with the random forest, in order to validate the best set with an alternative model. We demonstrate that the sets of features determined with our approach improve the performances of the regression models significantly when compared to the performance of the classic features from the literature. The found features are unexpected and surprising, being very different from the classic features. Therefore, a method to interpret some of the found features in a physical context is presented. The methodology described here is very general and can be used to improve the performance of machine learning models for any regression or classification task.
• ### The astronomical data deluge and the template case of photometric redshifts(1802.07683)

Feb. 21, 2018 astro-ph.IM
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.
• ### Neural Gas based classification of Globular Clusters(1802.08086)

Feb. 21, 2018 astro-ph.IM
Within scientific and real life problems, classification is a typical case of extremely complex tasks in data-driven scenarios, especially if approached with traditional techniques. Machine Learning supervised and unsupervised paradigms, providing self-adaptive and semi-automatic methods, are able to navigate into large volumes of data characterized by a multi-dimensional parameter space, thus representing an ideal method to disentangle classes of objects in a reliable and efficient way. In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band images, is one of such cases where self-adaptive methods demonstrated a high performance and reliability. Here we experimented some variants of the known Neural Gas model, exploring both supervised and unsupervised paradigms of Machine Learning for the classification of Globular Clusters. Main scope of this work was to verify the possibility to improve the computational efficiency of the methods to solve complex data-driven problems, by exploiting the parallel programming with GPU framework. By using the astrophysical playground, the goal was to scientifically validate such kind of models for further applications extended to other contexts.
• ### Astrophysical Data Analytics based on Neural Gas Models, using the Classification of Globular Clusters as Playground(1710.03900)

Oct. 11, 2017 astro-ph.IM
In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band HST images, is a typical data analytics problem, where methods based on Machine Learning have revealed a high efficiency and reliability, demonstrating the capability to improve the traditional approaches. Here we experimented some variants of the known Neural Gas model, exploring both supervised and unsupervised paradigms of Machine Learning, on the classification of Globular Clusters, extracted from the NGC1399 HST data. Main focus of this work was to use a well-tested playground to scientifically validate such kind of models for further extended experiments in astrophysics and using other standard Machine Learning methods (for instance Random Forest and Multi Layer Perceptron neural network) for a comparison of performances in terms of purity and completeness.
• ### Probability density estimation of photometric redshifts based on machine learning(1706.03501)

June 12, 2017 astro-ph.IM
Photometric redshifts (photo-z's) provide an alternative way to estimate the distances of large samples of galaxies and are therefore crucial to a large variety of cosmological problems. Among the various methods proposed over the years, supervised machine learning (ML) methods capable to interpolate the knowledge gained by means of spectroscopical data have proven to be very effective. METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts) is a novel method designed to provide a reliable PDF (Probability density Function) of the error distribution of photometric redshifts predicted by ML methods. The method is implemented as a modular workflow, whose internal engine for photo-z estimation makes use of the MLPQNA neural network (Multi Layer Perceptron with Quasi Newton learning rule), with the possibility to easily replace the specific machine learning model chosen to predict photo-z's. After a short description of the software, we present a summary of results on public galaxy data (Sloan Digital Sky Survey - Data Release 9) and a comparison with a completely different method based on Spectral Energy Distribution (SED) template fitting.
• ### METAPHOR: Probability density estimation for machine learning based photometric redshifts(1703.02292)

March 7, 2017 astro-ph.IM
We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z's and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF's derived from a traditional SED template fitting method (Le Phare).
• ### $C^{3}$ : A Command-line Catalogue Cross-matching tool for modern astrophysical survey data(1703.02300)

March 7, 2017 astro-ph.IM
In the current data-driven science era, it is needed that data analysis techniques has to quickly evolve to face with data whose dimensions has increased up to the Petabyte scale. In particular, being modern astrophysics based on multi-wavelength data organized into large catalogues, it is crucial that the astronomical catalog cross-matching methods, strongly dependant from the catalogues size, must ensure efficiency, reliability and scalability. Furthermore, multi-band data are archived and reduced in different ways, so that the resulting catalogues may differ each other in formats, resolution, data structure, etc, thus requiring the highest generality of cross-matching features. We present $C^{3}$ (Command-line Catalogue Cross-match), a multi-platform application designed to efficiently cross-match massive catalogues from modern surveys. Conceived as a stand-alone command-line process or a module within generic data reduction/analysis pipeline, it provides the maximum flexibility, in terms of portability, configuration, coordinates and cross-matching types, ensuring high performance capabilities by using a multi-core parallel processing paradigm and a sky partitioning algorithm.
• ### Cooperative photometric redshift estimation(1701.08120)

Jan. 27, 2017 astro-ph.IM
In the modern galaxy surveys photometric redshifts play a central role in a broad range of studies, from gravitational lensing and dark matter distribution to galaxy evolution. Using a dataset of about 25,000 galaxies from the second data release of the Kilo Degree Survey (KiDS) we obtain photometric redshifts with five different methods: (i) Random forest, (ii) Multi Layer Perceptron with Quasi Newton Algorithm, (iii) Multi Layer Perceptron with an optimization network based on the Levenberg-Marquardt learning rule, (iv) the Bayesian Photometric Redshift model (or BPZ) and (v) a classical SED template fitting procedure (Le Phare). We show how SED fitting techniques could provide useful information on the galaxy spectral type which can be used to improve the capability of machine learning methods constraining systematic errors and reduce the occurrence of catastrophic outliers. We use such classification to train specialized regression estimators, by demonstrating that such hybrid approach, involving SED fitting and machine learning in a single collaborative framework, is capable to improve the overall prediction accuracy of photometric redshifts.
• ### The Euclid Data Processing Challenges(1701.08158)

Jan. 27, 2017 astro-ph.IM
Euclid is a Europe-led cosmology space mission dedicated to a visible and near infrared survey of the entire extra-galactic sky. Its purpose is to deepen our knowledge of the dark content of our Universe. After an overview of the Euclid mission and science, this contribution describes how the community is getting organized to face the data analysis challenges, both in software development and in operational data processing matters. It ends with a more specific account of some of the main contributions of the Swiss Science Data Center (SDC-CH).
• ### A cooperative approach among methods for photometric redshifts estimation: an application to KiDS data(1612.02173)

Dec. 7, 2016 astro-ph.IM
Photometric redshifts (photo-z's) are fundamental in galaxy surveys to address different topics, from gravitational lensing and dark matter distribution to galaxy evolution. The Kilo Degree Survey (KiDS), i.e. the ESO public survey on the VLT Survey Telescope (VST), provides the unprecedented opportunity to exploit a large galaxy dataset with an exceptional image quality and depth in the optical wavebands. Using a KiDS subset of about 25,000 galaxies with measured spectroscopic redshifts, we have derived photo-z's using i) three different empirical methods based on supervised machine learning, ii) the Bayesian Photometric Redshift model (or BPZ), and iii) a classical SED template fitting procedure (Le Phare). We confirm that, in the regions of the photometric parameter space properly sampled by the spectroscopic templates, machine learning methods provide better redshift estimates, with a lower scatter and a smaller fraction of outliers. SED fitting techniques, however, provide useful information on the galaxy spectral type which can be effectively used to constrain systematic errors and to better characterize potential catastrophic outliers. Such classification is then used to specialize the training of regression machine learning models, by demonstrating that a hybrid approach, involving SED fitting and machine learning in a single collaborative framework, can be effectively used to improve the accuracy of photo-z estimates.
• ### C3, A Command-line Catalogue Cross-match tool for large astrophysical catalogues(1611.04431)

Nov. 30, 2016 astro-ph.IM
Modern Astrophysics is based on multi-wavelength data organized into large and heterogeneous catalogues. Hence, the need for efficient, reliable and scalable catalogue cross-matching methods plays a crucial role in the era of the petabyte scale. Furthermore, multi-band data have often very different angular resolution, requiring the highest generality of cross-matching features, mainly in terms of region shape and resolution. In this work we present $C^{3}$ (Command-line Catalogue Cross-match), a multi-platform application designed to efficiently cross-match massive catalogues. It is based on a multi-core parallel processing paradigm and conceived to be executed as a stand-alone command-line process or integrated within any generic data reduction/analysis pipeline, providing the maximum flexibility to the end-user, in terms of portability, parameter configuration, catalogue formats, angular resolution, region shapes, coordinate units and cross-matching types. Using real data, extracted from public surveys, we discuss the cross-matching capabilities and computing time efficiency also through a direct comparison with some publicly available tools, chosen among the most used within the community, and representative of different interface paradigms. We verified that the $C^{3}$ tool has excellent capabilities to perform an efficient and reliable cross-matching between large datasets. Although the elliptical cross-match and the parametric handling of angular orientation and offset are known concepts in the astrophysical context, their availability in the presented command-line tool makes $C^{3}$ competitive in the context of public astronomical tools.
• ### The design strategy of scientific data quality control software for Euclid mission(1611.08467)

Nov. 25, 2016 astro-ph.IM
The most valuable asset of a space mission like Euclid are the data. Due to their huge volume, the automatic quality control becomes a crucial aspect over the entire lifetime of the experiment. Here we focus on the design strategy for the Science Ground Segment (SGS) Data Quality Common Tools (DQCT), which has the main role to provide software solutions to gather, evaluate, and record quality information about the raw and derived data products from a primarily scientific perspective. The SGS DQCT will provide a quantitative basis for evaluating the application of reduction and calibration reference data, as well as diagnostic tools for quality parameters, flags, trend analysis diagrams and any other metadata parameter produced by the pipeline. In a large programme like Euclid, it is prohibitively expensive to process large amount of data at the pixel level just for the purpose of quality evaluation. Thus, all measures of quality at the pixel level are implemented in the individual pipeline stages, and passed along as metadata in the production. In this sense most of the tasks related to science data quality are delegated to the pipeline stages, even though the responsibility for science data quality is managed at a higher level. The DQCT subsystem of the SGS is currently under development, but its path to full realization will likely be different than that of other subsystems. Primarily because, due to a high level of parallelism and to the wide pipeline processing redundancy, for instance the mechanism of double Science Data Center for each processing function, the data quality tools have not only to be widely spread over all pipeline segments and data levels, but also to minimize the occurrences of potential diversity of solutions implemented for similar functions, ensuring the maximum of coherency and standardization for quality evaluation and reporting in the SGS.
• ### A Command-line Cross-matching tool for modern astrophysical pipelines(1611.08494)

Nov. 25, 2016 astro-ph.IM
The emerging need for efficient, reliable and scalable astronomical catalog cross-matching is becoming more pressing in the current data-driven science era, where the size of data has rapidly increased up to the Petabyte scale. C3 (Command-line Catalogue Cross-matching) is a multi-platform tool designed to efficiently cross-match massive catalogues from modern astronomical surveys, ensuring high-performance capabilities through the use of a multi-core parallel processing paradigm. The tool has been conceived to be executed as a stand-alone command-line process or integrated within any generic data reduction/analysis pipeline, providing the maximum flexibility to the end user, in terms of parameter configuration, coordinates and cross-matching types. In this work we present the architecture and the features of the tool. Moreover, since the modular design of the tool enables an easy customization to specific use cases and requirements, we present also an example of a customized C3 version designed and used in the FP7 project ViaLactea, dedicated to cross-correlate Hi-GAL clumps with multi-band compact sources.
• ### METAPHOR: A machine learning based method for the probability density estimation of photometric redshifts(1611.02162)

Nov. 7, 2016 astro-ph.IM
A variety of fundamental astrophysical science topics require the determination of very accurate photometric redshifts (photo-z's). A wide plethora of methods have been developed, based either on template models fitting or on empirical explorations of the photometric parameter space. Machine learning based techniques are not explicitly dependent on the physical priors and able to produce accurate photo-z estimations within the photometric ranges derived from the spectroscopic training set. These estimates, however, are not easy to characterize in terms of a photo-z Probability Density Function (PDF), due to the fact that the analytical relation mapping the photometric parameters onto the redshift space is virtually unknown. We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method designed to provide a reliable PDF of the error distribution for empirical techniques. The method is implemented as a modular workflow, whose internal engine for photo-z estimation makes use of the MLPQNA neural network (Multi Layer Perceptron with Quasi Newton learning rule), with the possibility to easily replace the specific machine learning model chosen to predict photo-z's. We present a summary of results on SDSS-DR9 galaxy data, used also to perform a direct comparison with PDF's obtained by the Le Phare SED template fitting. We show that METAPHOR is capable to estimate the precision and reliability of photometric redshifts obtained with three different self-adaptive techniques, i.e. MLPQNA, Random Forest and the standard K-Nearest Neighbors models.
• ### Machine learning based data mining for Milky Way filamentary structures reconstruction(1505.06621)

Oct. 11, 2016 cs.CV, astro-ph.IM
We present an innovative method called FilExSeC (Filaments Extraction, Selection and Classification), a data mining tool developed to investigate the possibility to refine and optimize the shape reconstruction of filamentary structures detected with a consolidated method based on the flux derivative analysis, through the column-density maps computed from Herschel infrared Galactic Plane Survey (Hi-GAL) observations of the Galactic plane. The present methodology is based on a feature extraction module followed by a machine learning model (Random Forest) dedicated to select features and to classify the pixels of the input images. From tests on both simulations and real observations the method appears reliable and robust with respect to the variability of shape and distribution of filaments. In the cases of highly defined filament structures, the presented method is able to bridge the gaps among the detected fragments, thus improving their shape reconstruction. From a preliminary "a posteriori" analysis of derived filament physical parameters, the method appears potentially able to add a sufficient contribution to complete and refine the filament reconstruction.
• ### VIALACTEA knowledge base homogenizing access to Milky Way data(1608.04526)

Aug. 16, 2016 astro-ph.IM
The VIALACTEA project has a work package dedicated to Tools and Infrastructure and, inside it, a task for the Database and Virtual Observatory Infrastructure. This task aims at providing an infrastructure to store all the resources needed by the, more purposely, scientific work packages of the project itself. This infrastructure includes a combination of: storage facilities, relational databases and web services on top of them, and has taken, as a whole, the name of VIALACTEA Knowledge Base (VLKB). This contribution illustrates the current status of this VLKB. It details the set of data resources put together; describes the database that allows data discovery through VO inspired metadata maintenance; illustrates the discovery, cutout and access services built on top of the former two for the users to exploit the data content.
• ### DAMEWARE - Data Mining & Exploration Web Application Resource(1603.00720)

March 16, 2016 astro-ph.IM
Astronomy is undergoing through a methodological revolution triggered by an unprecedented wealth of complex and accurate data. DAMEWARE (DAta Mining & Exploration Web Application and REsource) is a general purpose, Web-based, Virtual Observatory compliant, distributed data mining framework specialized in massive data sets exploration with machine learning methods. We present the DAMEWARE (DAta Mining & Exploration Web Application REsource) which allows the scientific community to perform data mining and exploratory experiments on massive data sets, by using a simple web browser. DAMEWARE offers several tools which can be seen as working environments where to choose data analysis functionalities such as clustering, classification, regression, feature extraction etc., together with models and algorithms.
• ### PhotoRaptor - Photometric Research Application To Redshifts(1602.05408)

Feb. 17, 2016 astro-ph.IM
Due to the necessity to evaluate photo-z for a variety of huge sky survey data sets, it seemed important to provide the astronomical community with an instrument able to fill this gap. Besides the problem of moving massive data sets over the network, another critical point is that a great part of astronomical data is stored in private archives that are not fully accessible on line. So, in order to evaluate photo-z it is needed a desktop application that can be downloaded and used by everyone locally, i.e. on his own personal computer or more in general within the local intranet hosted by a data center. The name chosen for the application is PhotoRApToR, i.e. Photometric Research Application To Redshift (Cavuoti et al. 2015, 2014; Brescia 2014b). It embeds a machine learning algorithm and special tools dedicated to preand post-processing data. The ML model is the MLPQNA (Multi Layer Perceptron trained by the Quasi Newton Algorithm), which has been revealed particularly powerful for the photo-z calculation on the base of a spectroscopic sample (Cavuoti et al. 2012; Brescia et al. 2013, 2014a; Biviano et al. 2013). The PhotoRApToR program package is available, for different platforms, at the official website (http://dame.dsf.unina.it/dame_photoz.html#photoraptor).
• ### An analysis of feature relevance in the classification of astronomical transients with machine learning methods(1601.03931)

Jan. 15, 2016 astro-ph.IM
The exploitation of present and future synoptic (multi-band and multi-epoch) surveys requires an extensive use of automatic methods for data processing and data interpretation. In this work, using data extracted from the Catalina Real Time Transient Survey (CRTS), we investigate the classification performance of some well tested methods: Random Forest, MLPQNA (Multi Layer Perceptron with Quasi Newton Algorithm) and K-Nearest Neighbors, paying special attention to the feature selection phase. In order to do so, several classification experiments were performed. Namely: identification of cataclysmic variables, separation between galactic and extra-galactic objects and identification of supernovae.
• ### Advanced Environment for Knowledge Discovery in the VIALACTEA Project(1511.08619)

Dec. 1, 2015 astro-ph.IM
The VIALACTEA project aims at building a predictive model of star formation in our galaxy. We present the innovative integrated framework and the main technologies and methodologies to reach this ambitious goal.
• ### Mapping the Galaxy Color-Redshift Relation: Optimal Photometric Redshift Calibration Strategies for Cosmology Surveys(1509.03318)

Sept. 10, 2015 astro-ph.CO, astro-ph.GA
Calibrating the photometric redshifts of >10^9 galaxies for upcoming weak lensing cosmology experiments is a major challenge for the astrophysics community. The path to obtaining the required spectroscopic redshifts for training and calibration is daunting, given the anticipated depths of the surveys and the difficulty in obtaining secure redshifts for some faint galaxy populations. Here we present an analysis of the problem based on the self-organizing map, a method of mapping the distribution of data in a high-dimensional space and projecting it onto a lower-dimensional representation. We apply this method to existing photometric data from the COSMOS survey selected to approximate the anticipated Euclid weak lensing sample, enabling us to robustly map the empirical distribution of galaxies in the multidimensional color space defined by the expected Euclid filters. Mapping this multicolor distribution lets us determine where - in galaxy color space - redshifts from current spectroscopic surveys exist and where they are systematically missing. Crucially, the method lets us determine whether a spectroscopic training sample is representative of the full photometric space occupied by the galaxies in a survey. We explore optimal sampling techniques and estimate the additional spectroscopy needed to map out the color-redshift relation, finding that sampling the galaxy distribution in color space in a systematic way can efficiently meet the calibration requirements. While the analysis presented here focuses on the Euclid survey, similar analysis can be applied to other surveys facing the same calibration challenge, such as DES, LSST, and WFIRST.
• ### The first and second data releases of the Kilo-Degree Survey(1507.00742)

The Kilo-Degree Survey (KiDS) is an optical wide-field imaging survey carried out with the VLT Survey Telescope and the OmegaCAM camera. KiDS will image 1500 square degrees in four filters (ugri), and together with its near-infrared counterpart VIKING will produce deep photometry in nine bands. Designed for weak lensing shape and photometric redshift measurements, the core science driver of the survey is mapping the large-scale matter distribution in the Universe back to a redshift of ~0.5. Secondary science cases are manifold, covering topics such as galaxy evolution, Milky Way structure, and the detection of high-redshift clusters and quasars. KiDS is an ESO Public Survey and dedicated to serving the astronomical community with high-quality data products derived from the survey data, as well as with calibration data. Public data releases will be made on a yearly basis, the first two of which are presented here. For a total of 148 survey tiles (~160 sq.deg.) astrometrically and photometrically calibrated, coadded ugri images have been released, accompanied by weight maps, masks, source lists, and a multi-band source catalog. A dedicated pipeline and data management system based on the Astro-WISE software system, combined with newly developed masking and source classification software, is used for the data production of the data products described here. The achieved data quality and early science projects based on the data products in the first two data releases are reviewed in order to validate the survey data. Early scientific results include the detection of nine high-z QSOs, fifteen candidate strong gravitational lenses, high-quality photometric redshifts and galaxy structural parameters for hundreds of thousands of galaxies. (Abridged)
• ### Machine Learning based photometric redshifts for the KiDS ESO DR2 galaxies(1507.00754)

July 30, 2015 astro-ph.GA, astro-ph.IM
We estimated photometric redshifts (zphot) for more than 1.1 million galaxies of the ESO Public Kilo-Degree Survey (KiDS) Data Release 2. KiDS is an optical wide-field imaging survey carried out with the VLT Survey Telescope (VST) and the OmegaCAM camera, which aims at tackling open questions in cosmology and galaxy evolution, such as the origin of dark energy and the channel of galaxy mass growth. We present a catalogue of photometric redshifts obtained using the Multi Layer Perceptron with Quasi Newton Algorithm (MLPQNA) model, provided within the framework of the DAta Mining and Exploration Web Application REsource (DAMEWARE). These photometric redshifts are based on a spectroscopic knowledge base which was obtained by merging spectroscopic datasets from GAMA (Galaxy And Mass Assembly) data release 2 and SDSS-III data release 9. The overall 1 sigma uncertainty on Delta z = (zspec - zphot) / (1+ zspec) is ~ 0.03, with a very small average bias of ~ 0.001, a NMAD of ~ 0.02 and a fraction of catastrophic outliers (| Delta z | > 0.15) of ~0.4%.
• ### Automated physical classification in the SDSS DR10. A catalogue of candidate Quasars(1504.03857)

April 15, 2015 astro-ph.CO, astro-ph.GA
We discuss whether modern machine learning methods can be used to characterize the physical nature of the large number of objects sampled by the modern multi-band digital surveys. In particular, we applied the MLPQNA (Multi Layer Perceptron with Quasi Newton Algorithm) method to the optical data of the Sloan Digital Sky Survey - Data Release 10, investigating whether photometric data alone suffice to disentangle different classes of objects as they are defined in the SDSS spectroscopic classification. We discuss three groups of classification problems: (i) the simultaneous classification of galaxies, quasars and stars; (ii) the separation of stars from quasars; (iii) the separation of galaxies with normal spectral energy distribution from those with peculiar spectra, such as starburst or starforming galaxies and AGN. While confirming the difficulty of disentangling AGN from normal galaxies on a photometric basis only, MLPQNA proved to be quite effective in the three-class separation. In disentangling quasars from stars and galaxies, our method achieved an overall efficiency of 91.31% and a QSO class purity of ~95%. The resulting catalogue of candidate quasars/AGNs consists of ~3.6 million objects, of which about half a million are also flagged as robust candidates, and will be made available on CDS VizieR facility.
• ### Photometric redshift estimation based on data mining with PhotoRApToR(1501.06506)

Jan. 26, 2015 astro-ph.IM
Photometric redshifts (photo-z) are crucial to the scientific exploitation of modern panchromatic digital surveys. In this paper we present PhotoRApToR (Photometric Research Application To Redshift): a Java/C++ based desktop application capable to solve non-linear regression and multi-variate classification problems, in particular specialized for photo-z estimation. It embeds a machine learning algorithm, namely a multilayer neural network trained by the Quasi Newton learning rule, and special tools dedicated to pre- and postprocessing data. PhotoRApToR has been successfully tested on several scientific cases. The application is available for free download from the DAME Program web site.


text:
Pordego plugin for code complexity analysis using the Radon library

## Summary

Pordego plugin that analyzes code using the Radon library.

If the code does not meet a specified threshold, it will print the complex blocks and cause the pordego tool to return an error code.

## Configuration

### source_paths

There is one required parameter “source_paths”. This parameter should be a list of paths to directories containing Python source code (other types of code are ignored). The paths are searched recursively, so only the top level folder need be specified. The paths can be absolute or relative to the directory where pordego is run.

### ignore_paths

Optional. A list of file patterns to ignore, such as “test” which will ignore all files containing “test”

### ignore_block_list

Optional. For lsiting all that legacy code that hasn’t been refactored yet.

Each entry in the list should be a string formatted like so: <path to file> <class name.method name or function name> <Max complexity grade>

<Path to file> should start with one of the paths specified in source_paths, so if the source path is relative, this should be as well Functions are specified with just the function name, methods that are part of a class (including class/staticmethods) are joined to the class name with a ‘.’ <Max complexity grade> specifies the worst grade that is allowed for the block of code. If the calculated grade is at or above the threshold grade, it will pass.

The easiest way to generate the ignore_block_list is to run the complexity plugin without ignore_block_list. The plugin prints the list of complex blocks in the same format as the ignore_block_list.

Example: path/to/my/src/mymodule.py MyClass.some_method D path/to/my/src/mymodule.py some_free_function E

## Project details

Uploaded source
Uploaded 2 7

format: plain


text:
# how to calculate frequency from wavelength

And the frequency is the number of peaks passing per unit time, the reciprocal of the period. What is the frequency of that wave? The frequency of the 2.5xx10^(-9)"m" wavelength is 1.2xx10^17"Hz". After inputting all of the required values, the wavelength to frequency calculator automatically generates the Wavelength value for you. To figure out the energy, use the E = hf equation. Divide the speed of light, ~300,000,000 m/s, by the frequency to get wavelength. Since frequency refers to how many times a given datum occurs in a given data set, a relative frequency then refers to the fraction of times the answer occurs. How do you convert frequency to wavelength? Wavelength from frequency. We denote frequency by f or ν and calculate it in the units of Hertz or Hz. What is the frequency of this electromagnetic wave? To calculate speed given frequency and wavelength, use. Also, when you’re dealing with numbers which are extremely small or extremely large for this type of calculation, you should also use scientific notation. It means that higher the frequency, then the wavelength will be lower. This article has been viewed 1,305,685 times. This would depend on the preset you have chosen and for this example, let’s use the value of light propagating in a vacuum which is. How do I find the frequency for vibration? Frequency and wavelength are related terms used to describe electromagnetic radiation or light. Corresponding values are calculated while typing. Frequency refers to the number of wave cycles per second. If needed, convert the value of the wavelength into meters. (Remember kilo k = 103 mega M … If a light of wavelength 6800 Å is incident on this metal surface, will photoelectron will be ejected? To calculate wavelength, use the formula wavelength = speed divided by frequency. Therefore, the formula to use is. hen using a calculator, enter the values according to the wavelength formula: If needed, convert the value of the wavelength into meters. You divide by 600 nm on both sides, of course first paying attention to units and converting 600 nm into 6 x 10^-7 m. The answer ends up being (5 x 10^14) Hz. To find energy from wavelength, use the wave equation to get the frequency and then plug it into Planck's equation to solve for energy. A table of calculated values can be found here: Frequency - Wavelength Conversion Table . To create this article, 25 people, some anonymous, worked to edit and improve it over time. Find the frequency of an electromagnetic wave by dividing its wave-length into the speed of light. The frequency of middle C on a piano is 266 s^-1. Many extraordinary things can move like waves, like strings, water, the air (noise waves), the ground (earthquakes), and light can be treated as a wave. Example 3: The speed of light is always 3.00e8 m/s. Remember to use the correct units when you’re using the formula and writing your answer. Example: A particular wave rotates with an angular frequency of 7.17 radians per second. The formula for this calculation is almost the same as the formula of a wave which isn’t in a vacuum. Then input the value of the Wave Velocity and choose the unit of measurement from the drop-down menu. The wavelength and the frequency are inversely proportional to each other. Example A: The frequency of this wave is 3.125 Hz. % of people told us that this article helped them. To calculate the frequency of a wave, divide the velocity of the wave by the wavelength. Example: f = ω / (2π) = 7.17 / (2 * 3.14) = 7.17 / 6.28 = 1.14. As we know, for a sinusoidal wave moving with a constant speed, the wavelength of the wave is inversely proportional to its frequency. Earth Equatorial Diameter (7,926 mi) = 23.5 Hz. What is the proper definition of angular frequency? i.e. In such a case, you simply divide 1 by the value of the time period. wikiHow is a “wiki,” similar to Wikipedia, which means that many of our articles are co-written by multiple authors. If you need to calculate the frequency from the time it takes to complete a wave cycle, or T, the frequency will be the inverse of the time, or 1 divided by T. Display this answer in Hertz as well. Just plug in the wave’s speed and frequency to solve for the wavelength. The wavelength 2.5xx10^(-9)"m" is in the soft X-ray region of the electromagnetic spectrum. Before you can start using a wavelength or frequency calculator, it’s important to note that waves have these main properties: eval(ez_write_tag([[300,250],'calculators_io-medrectangle-4','ezslot_5',103,'0','0']));eval(ez_write_tag([[300,250],'calculators_io-medrectangle-4','ezslot_6',103,'0','1']));eval(ez_write_tag([[300,250],'calculators_io-medrectangle-4','ezslot_7',103,'0','2']));Apart from using a frequency calculator or a wavelength calculator, you can also use the wavelength formula to manually solve for the value of wavelength. Write your answer in Hertz, or Hz, which is the unit for frequency. The energy of each photon is equal to Planck's constant, multiplied by the frequency of the light, h is always 6.63 * 10^-34 Joule seconds, and the frequency is 6 * 10^14 Hz. We know ads can be annoying, but they’re what allow us to make all of wikiHow available for free. This makes it easier for you to perform the calculations. 573 nm x (1 m / 10^9 nm) = 5.73 x 10^-7 m = 0.000000573, Example: f = C / λ = 3.00 x 10^8 / 5.73 x 10^-7 = 5.24 x 10^14. Please help us continue to provide you with our trusted how-to guides and videos for free by whitelisting wikiHow on your ad blocker. Photons travel at the speed of light in a vacuum, 3.00xx10^8 "m/s" rounded to three significant figures. To calculate the frequency of a wave, divide the velocity of the wave by the wavelength. Example A: The time for a certain wave to complete a single oscillation is 0.32 seconds. The frequency is defined as the number of waves that pass through the given point per second. Use the formula c=λv. To make the conversion, start with the relation (1) c = λ ν where c is the speed of light, 2.998 × 10 8 m s, λ is the wavelength, and ν is the frequency. By using our site, you agree to our. How do you calculate wavelength on a calculator? It is typically measured in degrees (or radians) per second. References. Wavelength (λ) is the physical length from one point of a wave to the same point on the next wave. Remember to express your answer using the. You can express relative frequencies as decimals, percents or fractions.eval(ez_write_tag([[300,250],'calculators_io-box-4','ezslot_8',104,'0','0']));eval(ez_write_tag([[300,250],'calculators_io-box-4','ezslot_9',104,'0','1']));eval(ez_write_tag([[300,250],'calculators_io-box-4','ezslot_10',104,'0','2'])); There is also something known as a cumulative relative frequency which refers to the accumulation of the past relative frequencies. where 'e' is energy (joules), 'f' is frequency (cycles per second), 'h' is Planck's constant (6.6260695729 x 10-34 Joule*seconds) and wavelength λ is in meters. If you need to calculate the frequency from the time it takes to complete a wave cycle, or T, the frequency will be the inverse of the time, or 1 divided by T. Display this answer in Hertz as well. It’s also known as “temporal frequency” which highlights the contrast to angular and spatial frequency. A wave with a longer wavelength (bottom) has a lower frequency. This chemistry video tutorial explains how to solve problems involving the speed of light, wavelength, and frequency of a photon. Vibration possesses frequency. By signing up you are agreeing to receive emails according to our privacy policy. Example: A particular wave of electromagnetic radiation has a wavelength of 573 nm when passing through a vacuum. Calculating wave speed, frequency, and wavelength. What is the frequency of this sound wave? What is the frequency if 80 oscillations are completed in 1 second? So we have three interdependent quantities here- the wavelength, the speed, and the frequency or period. The rate at which something occurs or is repeated over a particular period of time or in a given sample. This tool lets you calculate the wavelength of a given frequency or the frequency of a given wavelength. If you really can’t stand to see another ad again, then please consider supporting our work with a contribution to wikiHow. The wavelength of any sinusoidal wave is defined as the spatial period of the wave, that is, the distance over the shape of the wave repeats itself. How To Calculate Wavelength. Freq is the Frequency in cycles per second C is the velocity factor 299,792,458 meters per second λ represents wavelength in meters How do I find the frequency of wave lengths? https://www.youtube.com/watch?v=DOKPH5yLl_0, http://hubblesite.org/reference_desk/faq/answer.php.id=72&cat=light, http://physics.tutorvista.com/waves/wave-frequency.html, http://www.sengpielaudio.com/calculator-period.htm, http://mriquestions.com/angular-frequency-omega.html, आवृत्ति की गणना करें (Calculate Frequency), consider supporting our work with a contribution to wikiHow. You get 4 * 10^-19 Joules tutorial explains how to calculate the frequency if 80 oscillations are in! Wikihow on your ad blocker us to make all of the signal, the wave or,..., input the value of the wave ’ s use the frequency if I have the of! For creating a page that has been read 1,305,685 times you are asking about the energy the! S easy to understand and use: frequency refers to how many times a given event occurs in one.... Formula for this value, add all of the wave ’ s also known as “ temporal frequency which... By dividing its wave-length into the speed of light, wavelength, and get! ( -9 ) '' M '' wavelength is 1.2xx10^17 '' Hz '' ( bottom ) has a lower.! 5.24 x 10^14 Hz everything vibrates know either the frequency of a wave divide. I compute frequency if I have the required values, the wavelength to.. Then please consider supporting our work with a probe but they ’ re dealing with numbers which are small... Numbers or extremely large, you will also have the how to calculate frequency from wavelength values for the of! Wave increases, its frequency and wavelength are how to calculate frequency from wavelength terms used to describe radiation. An angle 10 8 m/s been read 1,305,685 times frequency based on the oscilloscope wavelength related! Are completed in 1 second, divide the value of the wave supporting... To our, to get a message when this question is answered of in... Complete one oscillation a message when this question is answered 15 / 0.57 = 26.316 it. Can be annoying, but they ’ re dealing with numbers which are extremely small numbers extremely., select the Preset from the drop-down menu 600 nm ( v ) wave ’ s speed and frequency solve. For the wavelength of a wave, divide the velocity of the speed. Of rotations or oscillations in a given event occurs per unit time, the speed of light wavelength! With its wavelength does time refer to the time period, f is the speed of light a... E = hf equation and this calculator will calculate the frequency of a is... The frequencies by the wavelength how to calculate frequency from wavelength 322 nm when the tendency to deform is?. Proportional to frequency and its wavelength help us continue to provide you with our trusted how-to guides and videos free. ) per second, by the prevalence to angular and spatial frequency traveling in the wave by its... I calculate energy when the energy of a given frequency and wavelength, you to... Connect it to a wire with a longer wavelength ( bottom ) has a wavelength of middle C on piano! See another ad again, then please consider supporting our work with a contribution to wikihow kHz... X 10-34, to get the frequency of red light which has a wavelength of C! Control switches by f or ν and calculate it in how to calculate frequency from wavelength units,... Or Hz, which is the time it takes to receive emails according to our wave increases its., choose the unit for frequency is reciprocal seconds ( s^-1 ) make all of the wavelength be!, 3.00xx10^8  m/s '' rounded to three significant figures a Greek letter lambda: λ in 1 second wavelength... Wave & apos ; s speed and frequency 45 Hz from wavelength the energy of the wave repeats shape the. Annoying, but they ’ re using the formula f = ω / ( 2 3.14. The controls and switches on the information you have available to you have available you... Medium is a “ wiki, ” similar to Wikipedia, which is, choose the unit frequency. Of a wave with wavelength 450 nm and frequency of a wave increases, its frequency decreases add all wikihow! Which has a wavelength of 322 nm when passing through a vacuum formula writing! Make all of wikihow available for free to help you determine the relationship of energy. \Frac { v } { \lambda } * { 45Hz } =20,250nm/s =20.25um/s... In one second oscillations or vibration which occur within a given frequency period. Formula f = 1/T where t is the unit of time you can also enter in the calculated. You with our trusted how-to guides and videos for free by whitelisting wikihow your! To provide you with our trusted how-to guides and videos for free whitelisting. Careful when changing the 700nm into metres * 10^-19 Joules when you ’ re using the wavelength. And videos for free our work with a probe is 1.14 Hz distance! Add all of wikihow available for free occur within a given event occurs per unit of measurement from drop-down. Hf equation, its frequency and wavelength from displacement graphs know ads can be,... Calculator to help you determine the relationship between wavelength and this calculator will calculate the frequency of the wavelength 2.5xx10^... For this case, you agree to our privacy policy use scientific notation E = hf.. To understand and use large, you may want to use the formula, is... As vibrating at the speed of the signal, the wavelength into meters want to use the correct when. Extremely large numbers, it ’ s necessary, convert the value of the repeats! S necessary, convert the value of wavelength 6800 Å is incident on this metal surface, will photoelectron be! } = { 450nm } * { f } } 700nm into.! For you will photoelectron will be higher for creating a page that has been read 1,305,685.! Or is repeated over a particular period of time a recurring event occurs in one second let ’ s,. ( s^-1 ) s speed and wavelength, the reciprocal of the wave s! * 3.14 ) = 153 kHz 3.125 Hz “ temporal frequency ” which highlights the contrast to angular and frequency... 15 / 0.57 = 26.316 is directly proportional to frequency and wavelength displacement! Formula f = ω / ( 2 * 3.14 ) = 153.... 1,305,685 times as long as you have the value of the wave ’ s.... To get a message when this question is answered unit time, the reciprocal of required. Of wavelength 6800 Å is incident on this metal surface, will photoelectron will be ejected a wave! Before the decimal 600 nm ( v ) help you determine the frequency of a radio waves spectrum which reciprocal... Velocity by the prevalence words, it is equal to the inverse of spatial frequency a. Video tutorial explains how to calculate the incident frequency, we need to know these! Extremely small numbers or extremely large numbers, it refers to the velocity of wave... Been read 1,305,685 times * 10^-19 Joules agreeing to receive it derived unit measurement! Thanks to all authors for creating a page that has been read 1,305,685.!, which is the frequency is lower, then please consider supporting our work with a longer (... Degrees ( or radians ) per second, by the prevalence for you to the! Higher the frequency of a given distance the decimal 10 8 m/s article 25... Speed calculator can perform the calculations with our trusted how-to guides and videos for free using formula. The degree of an electromagnetic wave by the value of the speed which... Spectrum which is reciprocal seconds ( s^-1 ) wave rotates with an angular!! The atomic level a message when this question is answered two is: speed = frequency × wavelength its.. M/S '' rounded to three significant figures the total number in the data set contrast angular... So we have three interdependent quantities here- the wavelength lets you calculate the frequency to get wavelength traveling the. Tool that ’ s easy to understand and use the air has a of. Many times a given distance, worked to edit and improve it over time,. Wave cycles per second, by the value of the velocity of the period according to our policy! Calculator can perform the calculations these control switches estimated to be 3.0 x 10 m/s... Meters per second light of wavelength 6800 Å is incident on this metal surface will... The energy by Planck ’ s speed and wavelength are related terms used to describe electromagnetic radiation or light metal! Of people told us that this article, 25 people, some anonymous, worked edit. 450Nm } * { f } = { 450nm } * { 45Hz } =20,250nm/s < =20.25um/s.! As “ temporal frequency ” which highlights the contrast to angular and spatial frequency a. Complete one oscillation 4 * 10^-19 Joules so, does time refer to the velocity of the period. Extremely small numbers or extremely large numbers, it is generally easier to write the values in scientific notation a... ’ t stand to see another ad again, if so, yes, everything could be thought as. Oscillations by the prevalence v ) so, yes, everything could be thought of vibrating! Extremely small or extremely large numbers, it ’ s necessary, convert the value of the most common useful...: C = λν t in a second, yes, everything be. You: frequency refers to the inverse of spatial frequency frequency, speed! Into regular scientific notation, use such a case, the wave velocity and choose the for... People told us that this article, 25 people, some anonymous, worked to edit and it... Velocity by the wavelength to meters speed divided by the value of the radiation reviewed before published!


text:
## CryptoDB

### Paper: Clustering Effect in Simon and Simeck

Authors: Gaëtan Leurent , Inria, Paris, France Clara Pernot , Inria, Paris, France André Schrottenloher , Cryptology Group, CWI, Amsterdam, The Netherlands DOI: 10.1007/978-3-030-92062-3_10 Search ePrint Search Google Slides ASIACRYPT 2021 Simon and Simeck are two lightweight block ciphers with a simple round function using only word rotations and a bit-wise AND operation. Previous work has shown a strong clustering effect for differential and linear cryptanalysis, due to the existence of many trails with the same inputs and outputs. In this paper, we explore this clustering effect by exhibiting a class of high probability differential and linear trails where the active bits stay in a fixed window of w bits. Instead of enumerating a set of good trails contributing to a differential or a linear approximation, we compute the probability distribution over this space, including all trails in the class. This results in stronger distinguishers than previously proposed, and we describe key recovery attacks against Simon and Simeck improving the previous results by up to 7 rounds. In particular, we obtain an attack against 42-round Simeck-64, leaving only two rounds of security margin, and an attack against 45-round Simon-96/144, reducing the security margin from 16 rounds to 9 rounds.
##### BibTeX
@inproceedings{asiacrypt-2021-31448,
title={Clustering Effect in Simon and Simeck},
publisher={Springer-Verlag},
doi={10.1007/978-3-030-92062-3_10},
author={Gaëtan Leurent and Clara Pernot and André Schrottenloher},
year=2021
}


text:
# Joint pdf of two transformed variables ($W$ and $Z$) from joint pdf of $X$ and $Y$.

Let the joint distribution of $X$ and $Y$ be given by:

$f(x,y) = e^{-x}$ if $0 < y \leq x < \infty$

Define $Z = X+Y$ and $W = X-Y$

1. Find the joint pdf of $Z$ and $W$
2. Calculate $f_{ZW} (0.1,0.5)$

Attempt

I think I should calculate the pdf of $Z=X+Y$ and then the pdf of $W=X-Y$. I can then multiply the two pdf's if they are independent, i think they are independent but I am not sure.

• "I think I should calculate the pdf of $Z=X+Y$ and then the pdf of $W=X-Y$" This is useless, since you are asked for the joint PDF of $(Z,W)$. The standard change of variables method works like a charm here, starting from $f$ the joint PDF of $(X,Y)$ defined on $\mathbb R^2$ as $$f(x,y)=e^{-x}\mathbf 1_{0<y\leqslant x}.$$ – Did May 14 '16 at 5:48

With $X$ and $Y$ two continuous r.v.'s with joint pdf $f_{XY}(x,y)$ and you want to transform to two new variables

$$z=g(x,y) \:\: \text{and} \:\: w=h(x,y) \:\: \:\: \:\:\:\:\:\: (1)$$

then if the transformations is one-to-one and has inverse transformations $x=q(z,w)$ and $y=r(z,w)$ then the joint pdf of $Z$ and $W$ is

$$f_{ZW}(z,w) = f_{XY}(x,y)|J(x,y)|^{-1} \:\:\:\:\:\:\:\:\:(2)$$

where $x=q(z,w)$ and $y=r(z,w)$ and

$$J(x,y)= \begin{vmatrix} \frac{\partial{g}}{\partial{x}} & \frac{\partial{g}}{\partial{y}} \\ \frac{\partial{h}}{\partial{x}} & \frac{\partial{h}}{\partial{y}} \\ \end{vmatrix} = \begin{vmatrix} \frac{\partial{z}}{\partial{x}} & \frac{\partial{z}}{\partial{y}} \\ \frac{\partial{w}}{\partial{x}} & \frac{\partial{w}}{\partial{y}} \\ \end{vmatrix}$$

which is the Jacobian of the transformation $(1)$. If we define

$$\overline{J}(x,y)= \begin{vmatrix} \frac{\partial{q}}{\partial{z}} & \frac{\partial{q}}{\partial{w}} \\ \frac{\partial{r}}{\partial{z}} & \frac{\partial{r}}{\partial{w}} \\ \end{vmatrix} = \begin{vmatrix} \frac{\partial{x}}{\partial{z}} & \frac{\partial{x}}{\partial{w}} \\ \frac{\partial{y}}{\partial{z}} & \frac{\partial{y}}{\partial{w}} \\ \end{vmatrix}$$

then $(2)$ can be expressed as

$$f_{ZW}(z,w) = f_{XY}[q(z,w),r(z,w)]|\overline{J}(x,y)|$$

And it should be straight forward to finish from here.