As reported in Section \ref{sec:VDQ} one of the main achievements of 
the last months has been the work
that led to the paper \cite{DETCHAR}. 
The group has actively participated to the ERs activity, 
in particular the ``Noise Event Miner'' (NoEMi) ran in-time (once per day)
during ER2 on both Virgo (less interesting for the noise as we have used VSR3 
recolored data) and LIGO (test on the central part of the Interferometer).
The Wavelet Detection Filter (WDF) ran online during ER2 on Virgo data,
testing the interface to on line data distribution and 
triggers database structure.
The noise group presented three works at GWPAW 12 
in Hannover. Two works are linked to non linear analysis 
``Impact of noise canceling by regression on searches for 
continuous gravitational waves''
  
``Non linear system identification in time domain: 
an application to Virgo Noise''
and one on spectral lines identification:
``Noise line investigations in Virgo VSR2-VSR4 data''
\subsubsection{Activities and FTEs}
The noise group is responsible of the characterization of the noise detector from point 
of view of noise contributions source identification, producing tools for commissioning 
and data analysis people devoted to noise understanding and helping in veto definition 
and environmental noise hunting.
The group is involved in the following main activities:
\begin{itemize}
\item
Noise characterization tools developing and on-line/in-time analysis for noise  
\item
Environmental  noise  hunting and characterization
\item
Noise analysis linked to commissioning working group  
\end{itemize}
All these activities are strictly linked to the work done by Virgo Data Quality group (VDQ).
Total FTEs are 4.4. 

Here the list of involved people:

Pia Astone 0.1,
Giancarlo Cella 0.1,
Alberto Colla 0.4,
Elena Cuoco 0.7,
Sabrina D’Antonio 0.2,
Marco Drago 0.3,
Irene Fiori 0.4, 
Sergio Frasca 0.2,
Gary Hemming 0.2,
Cristiano Palomba 0.1,
Francesco Piergiovanni 0.4,
Giovanni Prodi 0.1,
Virgina Re 0.3,
Paolo Ruggi  0.1,
Bas Swinkels 0.5,
Gabriele Vajente 0.1,
Gabriele Vedovato 0.2.
\subsubsection{Noise characterization tools}
Active projects: 
Noise Monitors API (NMAPI) and D-NMAPI, Noise Event Miner (NoEMi), 
Spectral Lines Identification and archiving in LinesDB, WDF  (Wavelet Detection Filter) 
and triggers database. 

We plan to integrate the existing noise monitor tools with new algorithms for detector 
characterization. In particular:
Non-linear noise identification (as the one already tested on VSR4 data), 
non-stationary noise estimators and integrate them in NMAPI. 
Glitch characterization: produce a catalogue of transient noise event waveforms 
(using cWB or WDF pipeline) and archive them in a Database. Channels ranking for glitches.
Tests and comparison on transient events found by Continuous Waves pipeline 
cleaning procedure and the ones found by WDF and  other pipelines.

\subsubsection{Environmental monitoring}
Support to Infrastructure modification projects 
(INF: new clean rooms, new INJ lab isolation, new air conditioning systems)

Support to VAC noise mitigation projects (new vacuum racks, scroll pump room)
characterization of new optical mounts for INJ
(possible, if approved) realization of the external environmental station
collaboration with IPGP seismologists in studies using Virgo data.

Integration of environmental sensors information in Hardware inventory database and preparation for Advanced Virgo 
(in collaboration and supporting Napoli group)
\subsubsection{Detector characterization for commissioning}
Contribution to noise hunting, noise suppression and  veto strategy  developing  
\subsubsection{Collaborations}
We started collaborating with LIGO CW and Detchar people for NoEMI integration as noise 
characterization tool. We plan to start collaborating with Max Planck 
Institute for Gravitational Physics (Albert Einstein Institute) group.

\subsubsection{Updated activities during the last months(May2012/October 2012)}
\begin{itemize}
\item {\bf NMAPI}

NMAPI provides a framework into which different noise monitors can be plugged 
and then configured. 
The application enables authenticated users to not only access information, 
but also to produce results, e.g. HTML pages or text files, based upon 
bespoke search criteria specified by them via the user interface. 
NMAPI was setup before VSR4 and used during the run. 
No major developing in the last 6 months
 
 
\item {\bf NoEMi}
 
  NoEMi is running on-line on the Ligo Hanford "One Arm Test" (OAT) data, since April 2012. 
The framework has been  adapted in order to run on the LIGO Condor clusters, 
allowing the analysis of more than 100 auxiliary channels in parallel. 
The NoEMi data are stored in a centralized database in Cascina, which is integrated in 
the  Noise Monitors API (NMAPI) framework. 
Thanks to the NMAPI infrastructure it is possible to visualize summary pages and  
query data belonging to different detectors with a single and user-friendly interface.  
NoEMi also ran on the second Virgo and LIGO Engineering Run (ER2) during the Summer 2012. 
This work was presented at last LVC meeting.
 \item {\bf LinesDB}

LinesDB is the web interface to the lines database, the archive of noise lines identified 
in the Virgo data by the NoEMi framework. The database is updated in real time during the 
data taking by NoEMi, and is linked to the metadata table, which is filled with user-defined 
information  (type of instrument used in measurement, 
general information relating to the line, etc.) once the source of the disturbance is understood. 
The identification of the noise lines found in VSR2-VSR4 data started just after the end of VSR4.  
Out of the O(1000) lines found in each  run, 90\% have already been identified and less 
than 100 lines still require investigation. The lists  of identified lines are used 
for the veto of fake candidates in the stochastic and CW searches. 
\item {\bf Wavelet Detection Filter (WDF)} 

Wavelet Detection Filter (WDF) is a pipeline for the detection of transient signals of 
generic waveforms. 
It was developed for detector characterization purposes. 
It is plugged in the NMAPI framework and so its results are available on the web as soon 
as produced. It ran online during ER2, testing the interface to on line data 
distribution and triggers database structure. Off-line analysis and tuning of WDF 
is in progress on ER2 and simulated data. The goal is to optimize the signal 
parameters estimation and make the filter much robust with respect the signal waveform.  
The connection of WDF and MySQL database and its integration in NMAPI framework 
was presented at last LVC meeting.
\item {\bf Non linear analysis}  

The Regression project is aimed to measure the linear couplings and some classes of 
non-linear ones among a primary "target" channel 
(e.g. h(t) or dark fringe) and a set of auxiliary channels 
(e.g. environmental or instrumental monitors). 
This information can then be used for commissioning and noise hunting activities, 
as well as for cleaning non Gaussian noises from h(t) by numerical subtraction 
of the measured correlation with the auxiliary channels. 
We are developing and tested two different procedures:

A procedure based on non-linear system identification technique has been developed to 
reconstruct noise structure in target channel. A Volterra series expansion of auxiliary channels 
is employed, so the target channel has been decomposed in a regression of auxiliary channels 
combination. The identification accuracy is improved by filtering the error 
in a specified frequency region. A ranking of the auxiliary channel contribution 
to the model can be obtained from the model structure selection procedure. 
Thanks to the orthogonalization procedure the evaluated contributions of each regressors is 
orthogonal with respect to the others. The ranking of most involved channels can give 
information and hints about noise sources and non-linearities production processes. 
Two different methods have been performed. The first one based on Orthogonal 
Least Square procedure (OLS - Billings et al. 1989) has been applied 
on modulation of calibration lines at 113Hz and 444Hz, on Vela pulsar region and 
on power lines of Virgo VSR4 data. 
Moreover the procedure has been applied on power lines of H1 LIGO S5 data using 
a list of 633 auxiliary channels parallelizing the code as much as possible to save 
computing time. A second method based on Sorted Fast Orthogonal Search 
(H.M. Abbas 2009, M.J. Korenberg 1989) has been used to solve memory usage problem. 
This method allows to keep in memory only regressors which give the 
highest contribution to the model allowing to handle high number of auxiliary 
channels without memory space trouble.  In the future the idea is to use 
the system identification techniques also for glitches analysis 
identifying channels that are involved in glitch events
(Poster presented at GWPAW 2012, LIGO-G1200496-v1)

A procedure  based on a coherent analysis of the target and auxiliary channels. 
It consists in a linear regression or Wiener-Kolmogorof filter which predicts the target 
channel part correlated to auxiliary channels. Considering as input the mixing of more 
auxiliary channels, it is possible also to characterize bilinear (or higher order) 
couplings to the target channel, for instance up-conversion of low frequency noise 
to side-bands of strong spectral lines. 
The algorithm is based on a chi-square minimization to find the optimal prediction, 
with the following steps:

- perform analysis in the wavelet domain. This allows to split a complex computational 
problem in simpler ones, e.g. in many narrow frequency bands;
- analyze coherently a set of many auxiliary channels including mixed channels per each 
narrow band.
- select the most relevant auxiliary channels for the prediction by applying a regulator 
to their ranked list, to avoid increasing prediction noise due to not useful auxiliary channels.

The prototype algorithm has been applied on Virgo data for a period of VSR4 data, 
focusing around some calibration lines and the mains.
 The algorithm has been tested by injecting software CW pulsar-like signals on Hanford data 
from S5 run. 
Applying CW pipelines (Hough transform and coherent pipeline) we tested the difference 
of performances on the two scratches of data, without applying and applying regression filter. 
The test has been made considering the power lines (50, 100, 150, 200, 250, 300 Hz) 
and relative side-bands. Preliminary results show that estimated parameters of the injected 
signals are compatible in the two cases, in particular the results show a gain in 
SNR of a factor of 1.2-1.5, 
at the prize of a loss on amplitude estimation of about 1\%. 
Work presented at GWPAW 2012.

\item {\bf Environmental monitoring}

For the advanced Virgo project we were involved in the following items.
INF chapter of AdV TDR (definition of requirements and mitigation strategies)
devices noise emission mitigation tests (new cooling fans, scroll pump room, racks seismic isolation, magnetic emissions of electronic components)

For EGO site noise climate (preservation and monitoring):
noise evaluation of one speedway project proposed 
for construction near Pontedera; project of one external environmental 
station isolated from the interferometer noise environment. 
\end{itemize}
\subsubsection{Activity in view of Advanced Detector Era}
We report here the Advanced Virgo Noise strategy plans.
The group described the strategy for noise study in the ADE in a 
dedicated document which is still in development 
(https://tds.ego-gw.it/ql/?c=9019). 
The idea was to collect information on all the projects 
linked to noise analysis and their computing requirements for ADE.
\begin{itemize}  
\item {\bf Centralized Data Base}

We have in mind to have a centralized database to store and retrieve 
all Noise Monitor results having a transparent access via a web interface .
The aim is to increase the inter-operability and communication between 
applications within this environment and NMAPI via the use of the 
Connections Database (CDB). 

\item {\bf Framework: from NMAPI to D-NMAPI}

Since we plan to integrate more analysis tools into NMAPI framework and 
having  Advanced Virgo and LIGO in mind  with a significant increases 
in channel examination, we foresee to upgrade NMAPI to a distributed 
version of the application (Distributed NMAPI) which will distribute 
the computational work on different computing nodes improving 
the performance of NMAPI.
Fig.\ref{fig:nmapi1}  and Fig\ref{fig:nmapi2}
show a snapshot of NMAPI and its  D-NMAPI Evolution.
\begin{figure}[bph!]
  \begin{center}
    \includegraphics[angle=0,width=.8\linewidth]{figures/nmapi1.png} 
    \caption{Work of the Noise group.  Snapshot of NMAPI}
    \label{fig:nmapi1}
  \end{center}
\end{figure}

\begin{figure}[bph!]
  \begin{center}
    \includegraphics[angle=0,width=.8\linewidth]{figures/nmapi2.png} 
    \caption{Work of the Noise group.  Snapshot of D-NMAPI}
    \label{fig:nmapi2}
  \end{center}
\end{figure}

\end{itemize}
